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 |
|---|---|---|---|---|
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def __snake_case ( __UpperCamelCase : int ,__UpperCamelCase : int ,__UpperCamelCase : int ,__UpperCamelCase : int ,__UpperCamelCase : int ... | 357 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAmelCas... | 329 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : List[Any] ):
... | 358 |
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... | 329 | 0 |
from __future__ import annotations
__a :str = "#"
class _a :
"""simple docstring"""
def __init__( self : Tuple ):
A_ = {}
def __A ( self : Tuple , UpperCAmelCase : str ):
A... | 359 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_co... | 329 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__a :Optional[Any] = {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
__a :Optional[int] = _LazyModule(__name__, globals()['__... | 360 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map... | 329 | 0 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
... | 361 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( __UpperCamelCase : List[str] ,__UpperCamelCase : str ,__UpperCa... | 362 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGE... | 329 | 0 |
def __snake_case ( __UpperCamelCase : list[int] ,__UpperCamelCase : list[int] ,__UpperCamelCase : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCa... | 363 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def ... | 329 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,... | 364 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase :... | 329 | 0 |
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = len(_UpperCAmelCase )
for i in range(1 ,_UpperCAmelCase ):
A_ = collection[i]
A_ = 0
A_ = i - ... | 365 |
import itertools
import math
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
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 ... | 329 | 0 |
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here t... | 366 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 329 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__a :Union[str, Any] = False
class _a ( unittest.TestCase ):
... | 367 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Dict = logging.get_logger(__name__)
__a :int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'goo... | 329 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__a :Union[str, Any] = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'PatchingSpec',
],
'convert': ['export', 'va... | 368 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers... | 329 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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... | 369 |
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
# Copied from ... | 329 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
lo... | 370 |
from math import isqrt, loga
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,... | 329 | 0 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__a :Optional[Any] = 'http://www.mocksite.com/file1.txt... | 371 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_c... | 329 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseMode... | 350 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
... | 329 | 0 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
__a :List[str] = get_logger(__name__)
class _a :
"""simple docstring"""
def __init__( self : str , UpperCAmelCase : Optional[Any] , UpperCAmelCase ... | 351 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :Any = [file for file in filepaths if file != file.lower... | 329 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a :int = logging.get_logger(__name__)
__a :Optional[int] = {
"""facebook/data2vec-text-base""": """https://huggingface.co/data2ve... | 352 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Union[str, Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}... | 329 | 0 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :int = logging.get_logger(__name__)
__a :Union[str, Any] = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/... | 353 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engi... | 329 | 0 |
__a :Optional[Any] = 6_5521
def __snake_case ( __UpperCamelCase : Any ):
"""simple docstring"""
A_ = 1
A_ = 0
for plain_chr in plain_text:
A_ = (a + ord(_lowerCAmelCase )) % MOD_ADLER
A_ ... | 354 |
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... | 329 | 0 |
from jiwer import compute_measures
import datasets
__a :Optional[int] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures ... | 355 |
import math
__a :Union[str, Any] = 10
__a :Union[str, Any] = 7
__a :int = BALLS_PER_COLOUR * NUM_COLOURS
def __snake_case ( __UpperCamelCase : int = 20 ):
"""simple docstring"""
A_ = math.comb(__UpperCamelCase ,__UpperCamelCase )
A_ ... | 329 | 0 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 356 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Optional[Any] = logging.get_logger(__name__)
__a :Any = {... | 329 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__a :List[str] = datasets.load_iris()
__a :Optional[Any] = np.array(data['data'])
__a :str = np.array(data['target'])
__a :List[Any] = data["target_names"]
__a... | 357 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAmelCas... | 329 | 0 |
def __snake_case ( __UpperCamelCase : Tuple ):
"""simple docstring"""
stooge(A__ ,0 ,len(A__ ) - 1 )
return arr
def __snake_case ( __UpperCamelCase : List[Any] ,__UpperCamelCase : int ,__UpperCamelCase : Optiona... | 358 |
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... | 329 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :int = {'configuration_xglm': ['XGLM_PRETRAINED_CONFIG... | 359 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_co... | 329 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
... | 360 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map... | 329 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgP... | 361 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_model... | 362 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGE... | 329 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUM... | 363 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def ... | 329 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a :List[Any] = logging.get_logger(__name__)
__a :Union[str, Any] = {
'shi-labs/dinat-mini-in1k-224': 'https://hugg... | 364 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase :... | 329 | 0 |
import csv
import tweepy
# Twitter API credentials
__a :Dict = ''
__a :Optional[Any] = ''
__a :int = ''
__a :Optional[Any] = ''
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
A_ = tweepy.OAuthHandler(_a ,_a )
... | 365 |
import itertools
import math
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
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 ... | 329 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __snake_case ( __UpperCamelCase : Dict ):
"""simple docstring"""
return ConvertCommand(
args.model_type ,args.tf_checkpoint ,ar... | 366 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 329 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :Optional[Any] = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', ... | 367 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Dict = logging.get_logger(__name__)
__a :int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'goo... | 329 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __snake_case ( ... | 368 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers... | 329 | 0 |
from math import pi, sqrt
def __snake_case ( __UpperCamelCase : Optional[Any] ):
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
if num > 171.5:
raise OverflowError("math range error" )
elif num - int(a__ ) not in (0, ... | 369 |
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
# Copied from ... | 329 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseSch... | 370 |
from math import isqrt, loga
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,... | 329 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if... | 371 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_c... | 329 | 0 |
import math
class _a :
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase : Union[str, Any]=0 ): # a graph with Node 0,1,...,N-1
A_ = n
A_ = [
[math.inf for j in ran... | 350 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
... | 329 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Optional[int] = logging.get_logger(__name__)
__a :Union[str, Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all... | 351 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :Any = [file for file in filepaths if file != file.lower... | 329 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipeli... | 352 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Union[str, Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}... | 329 | 0 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_b... | 353 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engi... | 329 | 0 |
from __future__ import annotations
import os
from typing import Any
import requests
__a :List[str] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__a :Tuple = BASE_URL + '''/user'''
# https://github.com/settings/t... | 354 |
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... | 329 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCa... | 355 |
import math
__a :Union[str, Any] = 10
__a :Union[str, Any] = 7
__a :int = BALLS_PER_COLOUR * NUM_COLOURS
def __snake_case ( __UpperCamelCase : int = 20 ):
"""simple docstring"""
A_ = math.comb(__UpperCamelCase ,__UpperCamelCase )
A_ ... | 329 | 0 |
__a :Union[str, Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase ... | 356 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Optional[Any] = logging.get_logger(__name__)
__a :Any = {... | 329 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class _a :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : List[str] ):
A_ = list_of_points
# Degree dete... | 357 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAmelCas... | 329 | 0 |
from math import pow, sqrt
def __snake_case ( *__UpperCamelCase : Optional[int] ):
"""simple docstring"""
A_ = len(A_ ) > 0 and all(value > 0.0 for value in values )
return result
def __snake_case ( __UpperCamelCase : Union[str, Any... | 358 |
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... | 329 | 0 |
import re
def __snake_case ( __UpperCamelCase : Dict ):
if len(re.findall("[ATCG]" ,_lowercase ) ) != len(_lowercase ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" ,"TAGC" ) )
if __name__ == "__ma... | 359 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_co... | 329 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_con... | 360 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map... | 329 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__a :Optional[Any] = 5_0000
__a :Optional[int] = 5000
__a , __a :Optional[int] = os.path.split(__file__)
__a :Any = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.re... | 361 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCamelCase__ ):
_lowerCamelCase : List[str] = (DDIMParallelScheduler,)
_lowerCamelCase : Dict = (("""eta""", 0.0), ("""num_infere... | 362 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGE... | 329 | 0 |
from math import factorial
__a :dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(_lowercase ,_lowercase ):
raise TypeError("Parameter number ... | 363 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def ... | 329 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Union[str, Any] = logging.get_logger(__name__)
__a :int = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
... | 364 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase :... | 329 | 0 |
import os
import sys
import unittest
__a :List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, r... | 365 |
import itertools
import math
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
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 ... | 329 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenizati... | 366 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 329 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, id... | 367 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Dict = logging.get_logger(__name__)
__a :int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'goo... | 329 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __snake_case ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ = args.pruning_method
A_ ... | 368 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers... | 329 | 0 |
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [[0 for _ in range(__UpperCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
A_ = 1
for n in range(m + 1 ):
for k in range(1 ,_... | 369 |
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
# Copied from ... | 329 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from... | 370 |
from math import isqrt, loga
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,... | 329 | 0 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax... | 371 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_c... | 329 | 0 |
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
@... | 350 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
... | 329 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Union[str, Any] = logging.get_... | 351 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :Any = [file for file in filepaths if file != file.lower... | 329 | 0 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__a :Union[str, Any] = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n ... | 352 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Union[str, Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}... | 329 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = (DDIMParallelScheduler,)
_lowerCamelCase : Any ... | 353 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engi... | 329 | 0 |
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.che... | 354 |
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... | 329 | 0 |
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 :List[Any] = pytest.mark.integration
@pytest.mark.parametrize("path" ,["paws", "cs... | 355 |
import math
__a :Union[str, Any] = 10
__a :Union[str, Any] = 7
__a :int = BALLS_PER_COLOUR * NUM_COLOURS
def __snake_case ( __UpperCamelCase : int = 20 ):
"""simple docstring"""
A_ = math.comb(__UpperCamelCase ,__UpperCamelCase )
A_ ... | 329 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_ut... | 356 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Optional[Any] = logging.get_logger(__name__)
__a :Any = {... | 329 | 0 |
def __snake_case ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
A_ = len(UpperCAmelCase_ )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse f... | 357 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAmelCas... | 329 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
... | 358 |
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... | 329 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__a :List[str] = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and... | 359 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_co... | 329 | 0 |
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : List[str] ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
A_ = str(bin(__UpperCamelCase ) )[2... | 360 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map... | 329 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCamelCase__ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = (UnCLIPScheduler,)
def __A ( self : ... | 361 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 | 0 |
import re
import subprocess
import sys
__a :int = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__a :Tuple = (
subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode('utf-8').split()
)
__a :int = '|'.join(sys.argv[1:])
_... | 362 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGE... | 329 | 0 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .datacla... | 363 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def ... | 329 | 0 |
__a :Optional[Any] = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
... | 364 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase :... | 329 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __snake_case ( __UpperCamelCase : np.ndarray ,__UpperCamelCase : np.ndarray ):
"""simple docstring"""
return math.sqrt(sum(pow(a - b ,2 ) for a, b ... | 365 |
import itertools
import math
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
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 ... | 329 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__a :Dict = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
__a :str = _LazyModule(__name__, globals()['__file__'], _i... | 366 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 329 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __snake_cas... | 367 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Dict = logging.get_logger(__name__)
__a :int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'goo... | 329 | 0 |
__a :List[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( __UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Any ,__UpperCamelCase ... | 368 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers... | 329 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __snake_case ( __UpperCamelCase : Dict ):
"""simple docstring"""
return "".join(sorted(lowerCAmelCase_ ) )
def __snake_case ( __UpperCamelCase : List[Any] ):
... | 369 |
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
# Copied from ... | 329 | 0 |
from __future__ import annotations
import math
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# N... | 370 |
from math import isqrt, loga
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
A_ = [True] * max_number
for i in range(2 ,isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 ,... | 329 | 0 |
import os
import sys
import unittest
__a :str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model... | 371 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_c... | 329 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__a :Any = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import iter... | 350 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
... | 329 | 0 |
"""simple docstring"""
__a :str = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
... | 351 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :Any = [file for file in filepaths if file != file.lower... | 329 | 0 |
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
# Copied from diffusers.sche... | 352 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a :Union[str, Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}... | 329 | 0 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake... | 353 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engi... | 329 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __snake_case ( ):
"""simple docstring"""
print("Making key files..." )
make_key_files("rsa" ,1024 )
... | 354 |
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... | 329 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoM... | 355 |
import math
__a :Union[str, Any] = 10
__a :Union[str, Any] = 7
__a :int = BALLS_PER_COLOUR * NUM_COLOURS
def __snake_case ( __UpperCamelCase : int = 20 ):
"""simple docstring"""
A_ = math.comb(__UpperCamelCase ,__UpperCamelCase )
A_ ... | 329 | 0 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAme... | 356 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Optional[Any] = logging.get_logger(__name__)
__a :Any = {... | 329 | 0 |
import math
__a :Union[str, Any] = 10
__a :Union[str, Any] = 7
__a :int = BALLS_PER_COLOUR * NUM_COLOURS
def __snake_case ( __UpperCamelCase : int = 20 ):
"""simple docstring"""
A_ = math.comb(__UpperCamelCase ,__UpperCamelCase )
A_ ... | 357 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAmelCas... | 329 | 0 |
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,
... | 358 |
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... | 329 | 0 |
class _a :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase : Any ):
# we need a list not a string, so do something to change the type
A_ = arr.split("," )
def __A ... | 359 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_co... | 329 | 0 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : list[int] ,__UpperCamelCase : int ):
"""simple docstring"""
A_ = 0
A_ = len(__UpperCamelCase ) - 1
while i < j:
if nums[i] + nums[... | 360 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map... | 329 | 0 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : dict ,__UpperCamelCase : str ):
"""simple docstring"""
A_ , A_ = set(__UpperCamelCase ), [start]
while stack:
A_ = stack.pop()... | 361 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 | 0 |
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
... | 362 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGE... | 329 | 0 |
import os
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
A_ = len(grid[0] )
A_ = len(__UpperCamelCase )
A_ = 0
A_ = 0
A_ = 0
# Check ver... | 363 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def ... | 329 | 0 |
from heapq import heappop, heappush
import numpy as np
def __snake_case ( __UpperCamelCase : np.ndarray ,__UpperCamelCase : tuple[int, int] ,__UpperCamelCase : tuple[int, int] ,__UpperCamelCase : bool ,):
"""simple docstring"""
... | 364 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase :... | 329 | 0 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def ... | 365 |
import itertools
import math
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
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 ... | 329 | 0 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attenti... | 366 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 329 | 0 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 367 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Dict = logging.get_logger(__name__)
__a :int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'goo... | 329 | 0 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int | str ):
"""simple docstring"""
A_ = str(__UpperCamelCase )
return n == n[::-1]
def __snake_case ( __UpperCamelCase : int = 100_0000 ):
"""sim... | 368 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers... | 329 | 0 |
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