code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOut... | 715 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 0 |
'''simple docstring'''
import pytest
__SCREAMING_SNAKE_CASE : Any = '__dummy_dataset1__'
__SCREAMING_SNAKE_CASE : List[Any] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/mai... | 716 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 0 |
'''simple docstring'''
import math
def _snake_case ( lowercase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 717 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , low... | 718 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
__SCREAMING_SNAKE_CASE : List[str] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentat... | 719 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 0 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impo... | 720 |
'''simple docstring'''
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 ImageProcessingS... | 697 | 0 |
'''simple docstring'''
def _snake_case ( lowercase = 4_0_0_0_0_0_0 ) -> int:
__a : int = [0, 1]
__a : int = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
__a : Dic... | 721 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 0 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils i... | 698 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 698 | 1 |
__A : Tuple = 'Tobias Carryer'
from time import time
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[str] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Union[... | 698 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE... | 698 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Optional[Any] = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json... | 698 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torc... | 698 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multi... | 698 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
... | 698 | 1 |
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 (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_f... | 698 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any... | 698 | 1 |
import math
def __a ( A__ : int ):
assert isinstance(A__ , A__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negative... | 698 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 698 | 1 |
import random
from typing import Any
def __a ( A__ : list ):
for _ in range(len(A__ ) ):
SCREAMING_SNAKE_CASE = random.randint(0 , len(A__ ) - 1 )
SCREAMING_SNAKE_CASE = random.randint(0 , len(A__ ) - 1 )
... | 698 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging... | 698 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = "timm_backbone"
de... | 698 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNA... | 698 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__A : int = 3
def __a ( A__ : int ):
print("Generating primitive root of p" )
while True:
SCREAMING_SNAKE_CASE = random.randrange(3 , ... | 698 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch,... | 698 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTe... | 698 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 698 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = (UnCLIPScheduler,)
def _snake_case ( self... | 698 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = R... | 698 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Be... | 698 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
i... | 698 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __a ( A__ : List[Any] , A__ : List[Any] , A__ : Any ):
SCREAMING_SNAKE_CASE = 0
if start < end:
SCREAMING_SNAKE_CASE = randint(A__ , A__ ... | 698 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr":... | 698 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNA... | 698 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
... | 698 | 1 |
from math import pi
def __a ( A__ : int , A__ : int ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0)) | 698 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Optional[int] = logging.get_... | 698 | 1 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__A : str = 'scheduler_config.json'
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''s... | 698 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
... | 698 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenizati... | 698 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectang... | 698 | 1 |
from math import factorial
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Any , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict ):
SCREAMING_SNAKE_CASE = real
if isinstance(__lowerC... | 698 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
... | 698 | 1 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
... | 698 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, Bl... | 698 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 698 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 698 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 698 |
# Copyright 2023 The HuggingFace Inc. 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 requ... | 698 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils impo... | 698 |
# Copyright 2021 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 ... | 698 | 1 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr":... | 698 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 698 | 1 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipel... | 698 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE... | 698 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
@register_to_config
def __init__... | 698 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torc... | 698 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A : Union[str, Any] = logging.get_logger(__name__)
__A : int = 'https:/... | 698 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
... | 698 | 1 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_h... | 698 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any... | 698 | 1 |
# using dfs for finding eulerian path traversal
def __a ( A__ : Union[str, Any] , A__ : List[Any] , A__ : Dict , A__ : int=None ):
SCREAMING_SNAKE_CASE = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 698 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 698 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Any = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Ro... | 698 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging... | 698 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__A : Optional[Any] = logging.get_logger(__name... | 698 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNA... | 698 | 1 |
def __a ( A__ : list ):
for i in range(len(A__ ) - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE = False
for j in range(A__ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_C... | 698 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch,... | 698 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ = ["image_processor", "tokenizer"]
lowerCamelCas... | 698 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 698 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Optional[int] = logging.get_... | 698 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = R... | 698 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : str = {
't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',... | 698 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
i... | 698 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Dict = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxConfig',
... | 698 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr":... | 698 | 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"... | 698 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
... | 698 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassific... | 698 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Optional[int] = logging.get_... | 698 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
... | 698 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
... | 698 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
... | 698 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectang... | 698 | 1 |
from __future__ import annotations
from collections.abc import Generator
def __a ( ):
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = 2
while True:
SCREAMING_SNAKE_CASE = factor_map.pop(A__ , A__ )
if factor:
... | 698 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
... | 698 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common ... | 698 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, Bl... | 698 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copi... | 698 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 698 | 1 |
from __future__ import annotations
def __a ( A__ : int = 4 ):
SCREAMING_SNAKE_CASE = abs(A__ ) or 4
return [[1 + x + y * row_size for x in range(A__ )] for y in range(A__ )]
def __a ( A__ : list[list[int]] ):
return reverse_r... | 698 |
# Copyright 2023 The HuggingFace Inc. 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 requ... | 698 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKE... | 698 |
# Copyright 2021 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 ... | 698 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class _SCREAMING_SNAKE_CASE ( __snake_case ):
... | 698 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 698 | 1 |
def __a ( A__ : int = 10 , A__ : int = 1000 , A__ : bool = True ):
assert (
isinstance(A__ , A__ )
and isinstance(A__ , A__ )
and isinstance(A__ , A__ )
), "Invalid type of value(s) specified to function!"
if min_val ... | 698 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE... | 698 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A : List[str] = pd.read_csv('sample_data.csv', header=None)
__A : List[Any] = df.... | 698 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torc... | 698 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A : Union[str, Any] = logging.get_log... | 698 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
... | 698 | 1 |
from math import isqrt, loga
def __a ( A__ : int ):
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 , A__ , A__ ):
SCREAMING_... | 698 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any... | 698 | 1 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
__A ... | 698 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 698 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _SCREAMING_SNAKE_CASE ( __snake_case ):
... | 698 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging... | 698 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 698 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNA... | 698 | 1 |
def __a ( A__ : int ):
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
__A : Dict = int(input('Enter number: ').strip())
print(... | 698 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch,... | 698 | 1 |
# Copyright 2021 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 ... | 698 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 698 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging... | 698 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = R... | 698 | 1 |
import math
def __a ( A__ : list , A__ : int = 0 , A__ : int = 0 ):
SCREAMING_SNAKE_CASE = end or len(A__ )
for i in range(A__ , A__ ):
SCREAMING_SNAKE_CASE = i
SCREAMING_SNAKE_CASE = array[i]
... | 698 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
i... | 698 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[Any] = {
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
),
}
... | 698 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr":... | 698 | 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 __a ( A__ : List[str] , A__ : Optional[Any] , A__ : Union[str... | 698 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
... | 698 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from d... | 698 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Optional[int] = logging.get_... | 698 | 1 |
from itertools import product
def __a ( A__ : int , A__ : int ):
SCREAMING_SNAKE_CASE = sides_number
SCREAMING_SNAKE_CASE = max_face_number * dice_number
SCREAMING_SNAKE_CASE = [0] * (max_total + 1)
SCREAMING_SNAKE_CASE ... | 698 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
... | 698 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__A : int = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to t... | 698 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectang... | 698 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@req... | 698 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
... | 698 | 1 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall... | 698 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, Bl... | 698 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, Bl... | 698 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 698 | 1 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torc... | 698 |
# Copyright 2023 The HuggingFace Inc. 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 requ... | 698 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , __lowerCamelCase : Optional[Any] ):
SCREAMING_SNAKE_CASE = dat... | 698 |
# Copyright 2021 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 ... | 698 | 1 |
from datetime import datetime as dt
import os
from github import Github
__A : int = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def __a ( ):
SCREAMING_SNAKE_CASE = Github(os... | 698 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 698 | 1 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
i... | 698 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE... | 698 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
... | 698 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torc... | 698 | 1 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 698 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
... | 698 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
Reques... | 698 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any... | 698 | 1 |
from __future__ import annotations
__A : Tuple = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__A : Any = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def __a ( A__ : list[float] ):
SCREAMING_SNAKE_CASE = []
SCREAMING... | 698 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 698 | 1 |
from collections.abc import Sequence
def __a ( A__ : Sequence[int] | None = None ):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
SCREAMING_SNAKE_CASE = nums[0]
for i in range(1 , len(A__ ) ):
... | 698 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__A : str = logging... | 698 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : str = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'Gro... | 698 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _SCREAMING_SNA... | 698 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__A : Optional[int] = models.Sequential()
# Step 1... | 698 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch,... | 698 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_ver... | 698 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 698 | 1 |
def __a ( A__ : list[int] , A__ : int ):
SCREAMING_SNAKE_CASE = len(A__ )
SCREAMING_SNAKE_CASE = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# h... | 698 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = R... | 698 | 1 |
# Copyright 2021 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 ... | 698 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
i... | 698 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
... | 698 |
__A : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr":... | 698 | 1 |
import argparse
import os
import re
import packaging.version
__A : str = 'examples/'
__A : Union[str, Any] = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULT... | 698 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
... | 698 | 1 |
def __a ( A__ : list[list] ):
SCREAMING_SNAKE_CASE = current_set.copy()
for row_index, row in enumerate(A__ ):
SCREAMING_SNAKE_CASE = row[0]
for column_index, column in enumerate(A__ ):
if magnitude == 0:
SCREAM... | 698 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Optional[int] = logging.get_... | 698 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_t... | 698 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : Any = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
... | 698 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Union[str, Any] = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https://huggingface.co/mod... | 698 |
import cmath
import math
def __a ( A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = math.radians(A__ )
SCREAMING_SNAKE_CASE = math.radians(A__ )
# Convert voltage and current to rectang... | 698 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def __a ( A__ : Callable[[int | float], int | float] , A__ : int | float , A__ : int | float , A__ : int = 100 , ):
SCREAMING_SNAKE_CASE = x_start
SCREAMING... | 698 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __a ( A__ : List[str] ):
... | 698 | 1 |
def __a ( A__ : list ):
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]... | 698 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, Bl... | 698 | 1 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_... | 698 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 698 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
requir... | 698 |
# Copyright 2023 The HuggingFace Inc. 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 requ... | 698 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__A : int = logging.get_logger(__name__)
def __a ( A__ : Union[tf.Tensor, np.ndarray] ):
if isinstance(A__ , np.ndarray ):
return list(tensor... | 698 |
# Copyright 2021 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 ... | 698 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : Tuple = {'vocab_file': 'spiece.mo... | 698 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 698 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx impo... | 698 |
from __future__ import annotations
from cmath import sqrt
def __a ( A__ : int , A__ : int , A__ : int ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
SCREAMING_SNAKE_CASE = b * b - 4 * a * c
SCREAMING_SNAKE... | 698 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__A : Union[str, Any] = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig... | 698 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torc... | 698 | 1 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : str = {'vocab_file': 'vocab.txt'}
__A : Tuple = {
... | 698 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
... | 698 | 1 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs... | 698 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any... | 698 | 1 |
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