code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : List[Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = {'vocab_file': 'vocab.json... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_... | 327 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCamelCase__ ():
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT):
with pytest.raises(_... | 327 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : List[Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = {'vocab_file': 'vocab.json... | 327 | 1 |
from __future__ import annotations
from math import pi
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
if (inductance, frequency, reactance).count(0) != 1:
raise ValueError('One and only one argument must be 0')
if inductance < 0:
raise... | 327 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=Fa... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = current_set.copy()
for row_index, row in enumerate(_UpperCAmelCase):
SCREAMING_SNAKE_CASE = row[0]
for column_index, column in enumerate(_UpperCAmelCase):
if magnitude == 0:
SCREAMING_SNAKE_... | 327 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, va... | 327 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ (_UpperCAmelCase ,... | 327 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 327 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 327 |
from scipy.stats import pearsonr
import datasets
a_ : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption t... | 327 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils ... | 327 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availa... | 327 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as... | 327 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_uti... | 327 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ... | 327 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ... | 327 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
Vi... | 327 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.t... | 327 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
a_ : int = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 327 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 327 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'mo... | 327 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_b... | 327 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ : List[Any] = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
if not is_torch_ava... | 327 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_co... | 327 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ : Optional[int]... | 327 |
from math import isqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , _UpperCAmelCase , _UpperCAmelCase):
SCREAMI... | 327 | 1 |
import sys
a_ : int = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'6689664895044524452316173185640309871112... | 327 |
import baseaa
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaaencode(string.encode('utf-8'))
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaadecode(_UpperCAmelCase).decode('utf-8')
if __name__ == "__main__":
import doctest
doctest.testmod()
| 327 | 1 |
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative')
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative')... | 327 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decode... | 327 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ : List[str] = {
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
'tokenizatio... | 327 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
if len(_UpperCAmelCase) != len(_UpperCAmelCase):
raise ValueError('The length of profit and weight must be same.')
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.')
if ... | 327 |
import argparse
import datetime
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
SCREAMING_SNAKE_CASE ... | 327 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _snake_case :
_lowercase : float
_lowercase : TreeNode | None = None
_lowercase : TreeNode | None = None
def lowerCamelCase__ (_Upp... | 327 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
... | 327 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
a_ : List[str] = TypeVar('T')
a_ : Tuple = TypeVar('U')
class _snake_case ( Generic[T, U] ):
def __init__( self , a , a) -> Dic... | 327 |
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = val
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def SCREAMING_SNAKE_CASE__ ( self , a) -> str:
if self.v... | 327 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_avail... | 327 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
a_ : Optional... | 327 | 1 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from .... | 327 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCa... | 327 | 1 |
from math import sqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 0
for i in range(1 , int(sqrt(_UpperCAmelCase) + 1)):
if n % i == 0 and i != sqrt(_UpperCAmelCase):
total += i + n // i
elif i == sqrt(_UpperCAmelCase):
total += i
... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_... | 327 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
a_ : Dict = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n ... | 327 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : List[Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = {'vocab_file': 'vocab.json... | 327 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available... | 327 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=Fa... | 327 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ : str = logging.get_logger(__name__)
a_ : List[Any] ... | 327 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, va... | 327 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow... | 327 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 327 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[Any] = logging.get_logger(__name__)
a_ : Optional[Any] = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
'microsoft/markuplm-... | 327 |
from scipy.stats import pearsonr
import datasets
a_ : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption t... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_UpperCAmelCase)
if number < 0:
return False
SCREAMING_SNAKE_CASE ... | 327 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availa... | 327 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ : List[Any] = namedtuple('covid_data', 'cases deaths recovered')
def lowerCamelCase__ (_UpperCAmelCase = "https://www.worldometers.info/coronavirus/"):
SCREAMING_SNAKE_CASE = '//div[... | 327 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_uti... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase = 100_0000):
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = {1: 1}
for inputa in range(2 , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = inputa
... | 327 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase = False):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3170_4406_4679_8873_8596_1981 and not allow_proba... | 327 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.t... | 327 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_... | 327 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 327 | 1 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
a_ : Tuple = re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class _snake_case :... | 327 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_b... | 327 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _snake_case ... | 327 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_co... | 327 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE ... | 327 |
from math import isqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , _UpperCAmelCase , _UpperCAmelCase):
SCREAMI... | 327 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( A__ ):
_lowercase : ... | 327 |
import baseaa
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaaencode(string.encode('utf-8'))
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaadecode(_UpperCAmelCase).decode('utf-8')
if __name__ == "__main__":
import doctest
doctest.testmod()
| 327 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class _snake_case ( A__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JS... | 327 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decode... | 327 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=Fa... | 327 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
_validate_point(_UpperCAmelCase)
_validate_point(_UpperCAmelCase)
if len(_UpperCAmelCase) != len(_UpperCAmelCase):
raise ValueError('Both points must be in the same n-dimensional space')
return float(sum(abs(a - b) for a, ... | 327 |
import argparse
import datetime
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
SCREAMING_SNAKE_CASE ... | 327 | 1 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
print(F'''Vertex\tShortest Distance from vertex {src}''')
for i, d in enumerate(_UpperCAmelCase):
print(F'''{i}\t\t{d}''')
def lowerCamelCase__ (_UpperCAmelCase , _Uppe... | 327 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
... | 327 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( A__ ):
_lowercase : ... | 327 |
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = val
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def SCREAMING_SNAKE_CASE__ ( self , a) -> str:
if self.v... | 327 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Tuple = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-30... | 327 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
a_ : Optional... | 327 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(_UpperCAmelCase)
SCREAMING_SNAKE_CA... | 327 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCa... | 327 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_... | 327 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availa... | 327 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : List[Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = {'vocab_file': 'vocab.json... | 327 | 1 |
from __future__ import annotations
class _snake_case :
def __init__( self , a) -> None:
SCREAMING_SNAKE_CASE = data
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def lowerCamelCase__ (_UpperCAmelCase): # In Order tra... | 327 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=Fa... | 327 | 1 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 327 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, va... | 327 | 1 |
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=A__ ):
_lowercase : Tuple = ['''note_seq''']
def __init__( self , *a , **a) -> List[str]:
requires_backends(self , ['note_seq'])
@clas... | 327 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase):
raise TypeError('only integers accepted as input')
else:
SCREAMING_SNAKE_CASE = str(abs(_UpperCAmelCase))
SCREAMING_SNAKE_CASE = [list(_UpperCAmelCase) for char in ... | 327 |
from scipy.stats import pearsonr
import datasets
a_ : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption t... | 327 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.defa... | 327 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availa... | 327 | 1 |
import math
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_UpperCAmelCase)
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y... | 327 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_uti... | 327 | 1 |
from math import pi
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 327 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ... | 327 | 1 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None):
if start is None:
SCREAMING_SNAKE_CASE = 0
if end is None:
SCREAMING_SNAKE_CASE = len(_UpperCAmelCase) - 1
if start >= e... | 327 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.t... | 327 | 1 |
import requests
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = {'Content-Type': 'application/json'}
SCREAMING_SNAKE_CASE = requests.post(_UpperCAmelCase , json={'text': message_body} , headers=_UpperCAmelCase)
if res... | 327 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 327 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test... | 327 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_b... | 327 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 327 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_co... | 327 | 1 |
from math import ceil, sqrt
def lowerCamelCase__ (_UpperCAmelCase = 100_0000):
SCREAMING_SNAKE_CASE = 0
for outer_width in range(3 , (limit // 4) + 2):
if outer_width**2 > limit:
SCREAMING_SNAKE_CASE = max(ceil(sqrt(outer_width**2 - limit)) , 1)
... | 327 |
from math import isqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , _UpperCAmelCase , _UpperCAmelCase):
SCREAMI... | 327 | 1 |
import numpy as np
def lowerCamelCase__ (_UpperCAmelCase):
return 1 / (1 + np.exp(-vector))
def lowerCamelCase__ (_UpperCAmelCase):
return vector * sigmoid(1.7_02 * vector)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 327 |
import baseaa
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaaencode(string.encode('utf-8'))
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaadecode(_UpperCAmelCase).decode('utf-8')
if __name__ == "__main__":
import doctest
doctest.testmod()
| 327 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ : Optional[int] = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_a... | 327 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decode... | 327 | 1 |
import os
def lowerCamelCase__ ():
with open(os.path.dirname(_UpperCAmelCase) + '/grid.txt') as f:
SCREAMING_SNAKE_CASE = [] # noqa: E741
for _ in range(20):
l.append([int(_UpperCAmelCase) for x in f.readline().split()])
SCREAMING_SNAKE_CASE = 0
#... | 327 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require... | 327 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Union[str, Any] = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
... | 327 |
import argparse
import datetime
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
SCREAMING_SNAKE_CASE ... | 327 | 1 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
SCREAMING_SNAKE_CASE = len(_UpperCAmelCase)
# If row is equal to the size of the board it me... | 327 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
... | 327 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
a_ : Dict = pytest.mark.integration
@pytest.mark.parametrize('path' , ['paws',... | 327 |
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = val
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def SCREAMING_SNAKE_CASE__ ( self , a) -> str:
if self.v... | 327 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : List[str] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFormerOnnx... | 327 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
a_ : Optional... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 0
# if input_string is "aba" than new_input_string become "a|b|a"
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_s... | 327 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCa... | 327 | 1 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decode... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_... | 327 | 1 |
import re
import string
import numpy as np
import datasets
a_ : Any = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
a_ : Union[str, Any] = '\nArgs:\n predictions: ... | 327 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : List[Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = {'vocab_file': 'vocab.json... | 327 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
a_ : int = logging.get_l... | 327 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=Fa... | 327 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F4... | 327 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, va... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
while b:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = b, a % b
return a
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return a if b == 0 else euclidean_gcd_recursive(_UpperCAmelCase ... | 327 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
return round(float(moles / volume) * nfactor)
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
return round(float((moles * 0.08_21 * temperature) / (vol... | 327 |
from scipy.stats import pearsonr
import datasets
a_ : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption t... | 327 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _snake_case ( A__ ):
def __init__( self , a , a = None ... | 327 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availa... | 327 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_uti... | 327 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_uti... | 327 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _snake_case ( A__ ):
_l... | 327 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
SCREAMING_SNAKE_CASE = grid[0]
for row_n in r... | 327 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.t... | 327 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTest... | 327 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 327 | 1 |
a_ : str = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o': 'ABBAB',
'p': 'ABBBA... | 327 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_b... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 1
for i in range(1 , num + 1):
fact *= i
return fact
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 0
while number > 0:
SCREAMING_SNAKE_CASE = number % 10
... | 327 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_co... | 327 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=1000):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
SCREAMING_SNAKE_CASE = n - 1
SCREAMING_SNAKE_CASE = 0
... | 327 |
from math import isqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , _UpperCAmelCase , _UpperCAmelCase):
SCREAMI... | 327 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=None):
SCREAMING_SNAKE_CASE = None
if token is not None:
SCREAMING_SNAKE_C... | 327 |
import baseaa
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaaencode(string.encode('utf-8'))
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaadecode(_UpperCAmelCase).decode('utf-8')
if __name__ == "__main__":
import doctest
doctest.testmod()
| 327 | 1 |
a_ : Any = 'Alexander Joslin'
import operator as op
from .stack import Stack
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
SCREAMING_SNAKE_CASE = Stack()
SCREAMING_SNAKE_CASE = ... | 327 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decode... | 327 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class _snake_case ( A__ ):
_lowercase : str = field(default='''language-modeling''' , ... | 327 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require... | 327 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 327 |
import argparse
import datetime
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
SCREAMING_SNAKE_CASE ... | 327 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
... | 327 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
... | 327 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : Optional[Any] = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDepe... | 327 |
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = val
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def SCREAMING_SNAKE_CASE__ ( self , a) -> str:
if self.v... | 327 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a_ : str = ... | 327 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
a_ : Optional... | 327 | 1 |
from __future__ import annotations
class _snake_case :
def __init__( self , a = 0) -> Optional[Any]:
SCREAMING_SNAKE_CASE = key
def SCREAMING_SNAKE_CASE__ ( self , a , a) -> list[str]:
assert isinstance(a , a) and i... | 327 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCa... | 327 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _snake_case ( A__ , unittest.TestCase ):
_lowercase : Any = CTR... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_... | 327 | 1 |
import random
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = a[left_index]
SCREAMING_SNAKE_CASE = left_index + 1
for j in range(left_index + 1 , _UpperCAmelCase):
if a[j] < pivot:
SCR... | 327 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : List[Any] = logging.get_logger(__name__)
a_ : Union[str, Any] = {'vocab_file': 'vocab.json... | 327 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class _snake_case ( ... | 327 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=Fa... | 327 | 1 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
a_ : List[str] = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of th... | 327 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, va... | 327 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
a_ : Union[str, Any] = datasets.logging.get_logger(__name__)
a_ : Optional[int] = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n ... | 327 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 0
for ch in input_str:
SCREAMING_SNAKE_CASE = ord(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = pow(2 , _UpperCAmelCase)
# If we already turned on bit for current character's unicode
if b... | 327 |
from scipy.stats import pearsonr
import datasets
a_ : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption t... | 327 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def ... | 327 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_availa... | 327 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCa... | 327 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_uti... | 327 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
if not head:
return True
# split the list to two parts
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = head.next, head
while fast and fast.next:
SCREAMING_SNAKE_CASE = fast.next.next
SCREAMING_SNAKE_CASE = slow.ne... | 327 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ... | 327 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Tuple = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
... | 327 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.t... | 327 | 1 |
from itertools import permutations
def lowerCamelCase__ (_UpperCAmelCase):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
SCREAMING_SNAKE_CASE = [7, 11, 13, 17]
for i, test in enumerate(... | 327 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 327 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCamelCase__ (_UpperCAmelCase):
if "model" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.replace('model.' , '')
if "norm1" in orig_key:
SCREAMING_SNAKE_CASE = orig_key.... | 327 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_b... | 327 | 1 |
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