code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from sklearn.metrics import mean_squared_error
import datasets
SCREAMING_SNAKE_CASE : Tuple = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. an... | 21 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCAmelCase = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
try:
if not is_torch_availa... | 341 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCamelCase ( lo... | 328 | 0 |
"""simple docstring"""
def a_ ( lowerCamelCase ):
if not isinstance(lowerCamelCase , lowerCamelCase ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
UpperCAmelCase__ = 0
while number:
# This way we arrive a... | 98 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 320 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase = 0 , __lowercase = 0 ) -> int:
A: Union[str, Any] = right or len(__lowercase ) - 1
if left > right:
return -1
elif lis... | 334 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase ) -> Tuple:
A: Tuple = len(__lowercase )
for i in range(length - 1 ):
A: Dict = i
for k in range(i + 1 , __lowercase ):
... | 334 | 1 |
"""simple docstring"""
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... | 16 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cl... | 273 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, 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, rand... | 369 |
import unittest
import numpy as np
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
if is_torch_available():
import t... | 140 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
... | 265 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
a : Optional[int] = 200
# Number of elements selected in every generation of evolution. The selection takes
#... | 368 |
"""simple docstring"""
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_... | 79 | 0 |
from math import factorial
def lowerCamelCase__ ( _A = 20 ):
'''simple docstring'''
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(factorial(_A ... | 187 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
impor... | 187 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.tra... | 355 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow... | 319 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _a ( lowerCamelCase: int , lowerCamelCase: Tuple , lowerCamelCase: Union[str, Any] , lowerCamelCase: Tuple ) -> Any:
'''sim... | 117 |
import argparse
import copy
def _a ( lowerCamelCase: List[Any] ) -> List[str]:
'''simple docstring'''
__A = {}
with open(lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neig... | 117 | 1 |
import math
import random
def _a ( UpperCAmelCase , UpperCAmelCase = False ) -> float:
"""simple docstring"""
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_A : Tuple = 0.02
def _a ( UpperCAmelCase ... | 369 |
def _a ( UpperCAmelCase ) -> bool:
"""simple docstring"""
return str(UpperCAmelCase ) == str(UpperCAmelCase )[::-1]
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
return int(UpperCAmelCase ) + int(str(UpperCAmelCase )[::-1]... | 265 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowerCAmelCase :
def __init__( self : List[str] , __lowercase : Collection[float] | None = None ):
... | 141 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class lowerCAmelCase ( A ):
def __init__( self : Optional[Any] , *__lowercase : str , **__lowercase : Union[str, Any] ):
"""simple docstrin... | 141 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Dict = logging.get_logger(__name__)
_a : List[Any] = {
'bert-base-uncased': '... | 355 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 100 ) -> int:
_lowerCAmelCase : Optional[Any] = n * (n + 1) * (2 * n + 1) / 6
_lowerCAmelCase : Tuple = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
... | 126 | 0 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def _a ( ) -> None:
snake_case_ = input("""Enter message: """ )
snake_case_ = input("""Enter key [alphanumeric]: """ )
snake_case_ = input("""Encrypt/... | 347 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 347 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ... | 352 | import datasets
from .evaluate import evaluate
lowercase = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},... | 35 | 0 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = 0, lowerCAmelCase_ = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE =right or len(lowerCAmelCase_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
r... | 334 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
l... | 334 | 1 |
snake_case_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase__ ( snake_case_ : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case_ , snake_case_ ):
__snake... | 238 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 238 | 1 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
SCREAMING_SNAKE_CASE : str = """src/transformers"""... | 102 | def UpperCamelCase ( __lowercase : list[list[int]] ,__lowercase : int ,__lowercase : int ,__lowercase : list[int] ):
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in... | 140 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a , a , a , a , a ) -> List[Any]:
'''simple docstring'''
if index == r:
for j in range(a ):
print(data[j] , end=''' ''' )
print(''' ''' )
return
... | 98 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a__ : List[Any] = logging.get_logger(__name__)
a__ : str = OrderedDict(
[
... | 313 |
'''simple docstring'''
from PIL import Image
def __lowercase ( __lowercase , __lowercase ) -> Image:
'''simple docstring'''
_A = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__lowercase ) -> int:
return int(128 + fa... | 79 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilB... | 26 |
from __future__ import annotations
def _a ( a :dict , a :str ) -> set[str]:
a , a = set(a ), [start]
while stack:
a = stack.pop()
explored.add(a )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add ad... | 26 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : list ) -> list:
if len(_UpperCAmelCase ) < 2:
return collection
def circle_sort_util(_UpperCAmelCase : list ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> bool:
... | 276 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e... | 276 | 1 |
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 ...t... | 42 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ... | 42 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_UpperCAmelCase : Any =logging.get_logger(__name__)
_UpperCAmelCase : Tuple =OrderedDict(
[
... | 262 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Optional[int] = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git... | 265 | 0 |
from __future__ import annotations
_snake_case = list[tuple[int, int]]
_snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, ... | 343 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCAmelCase_ ( snake_case_,snake_case_=None ):
_A : Any = None
if token is not None:
_A : ... | 343 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def UpperCamelCase_ ( lowerCAmelCase__ ... | 224 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Opti... | 126 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
... | 54 | """simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCAmelCase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0,... | 54 | 1 |
"""simple docstring"""
import argparse
import os
import re
_UpperCamelCase : Any = "src/transformers"
# Pattern that looks at the indentation in a line.
_UpperCamelCase : List[Any] = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_UpperCamelCase ... | 77 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a = logging... | 35 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
"""simple doc... | 356 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ , A__ , A__ , A__ , A__ ) -> int:
"""simple docstring"""
if index == r:
for j in range(A__ ):
print(data[j] , end=' ' )
print(' ' )
r... | 249 | 0 |
"""simple docstring"""
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_commo... | 238 |
"""simple docstring"""
def snake_case__ ( ):
"""simple docstring"""
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_lowercase : str = generate_large_matrix()
_lowercase : str = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1... | 238 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include ... | 359 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokeni... | 252 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : List[Any] = logging.get_logger(__name__)
lowerCAmelCase__ : Tuple = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config... | 98 | """simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 98 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( _a ... | 367 |
"""simple docstring"""
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
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCA... | 340 | 0 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_m... | 26 |
def lowerCAmelCase_ ( snake_case_ ):
if n_term == "":
return []
_A : list = []
for temp in range(int(snake_case_ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
_sna... | 26 | 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,
BertEncode... | 358 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__A = get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( enum.Enum ):
'''simple docstring'''
lowercase_ = "all_checks"
lowercase_ = ... | 273 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> int:
if not isinstance(__A , __A ):
_snake_case = F'Input value of [number={number}] must be an integer'
raise TypeError(__A )
if number < 1:
_snake_case = F'Input value of [number={number}] must be > 0'
... | 42 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase : List[str] = logging.get_logger("transformers.models.speecht5")
def SCREAMING_SNAKE_CASE__ ( ... | 42 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class __a :
'''simple docstring'''
def __init__( self , _a , _a ) -> None:
"""simple docstring"""
if len(_a ) != degree + 1:
... | 56 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a :List[Any] =... | 56 | 1 |
"""simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class a :
@property
def UpperCamelCase_ ... | 220 |
"""simple docstring"""
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 u... | 220 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : List[str] = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json"... | 197 | from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase( __lowerCamelCase ):
for param in module.parameters():
__a = False
def lowerCAmelCase( ):
__a = 'cuda' if torch.cuda.is_available() else 'cpu'
if to... | 197 | 1 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
a__ : Optional[Any] = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
j... | 54 |
"""simple docstring"""
from PIL import Image
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = image.size
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = image.load(... | 54 | 1 |
from math import pi, sqrt
def lowerCAmelCase__ ( a__ ) ->float:
'''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, 0.5):
raise NotImplementedError("num mus... | 63 | import inspect
import unittest
from transformers import DecisionTransformerConfig, 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 i... | 63 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/... | 16 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MA... | 249 | 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 UpperCamelCase ( __magic_name__ : Optional[Any... | 146 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common i... | 146 | 1 |
'''simple docstring'''
import argparse
import os
import re
_SCREAMING_SNAKE_CASE : List[Any] = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_SCREAMING_SNAKE_CASE : Un... | 85 |
from __future__ import annotations
def __lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
if b == 0:
return (1, 0)
((lowerCamelCase) , (lowerCamelCase)) = extended_euclid(lowerCamelCase__ , a % b... | 252 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers impo... | 356 | """simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :int ) -> bool:
'''simple docstring'''
lowercase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cub... | 32 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfi... | 279 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a_ = '''src/transformers'''
a_ = '''docs/source/en/tasks'''
def ... | 340 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class A__ ( snake_case__ ):
"""simple docstring"""
def __init__( self , *__snake_case... | 213 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A__ :
"""simple docstring"""
def __init__( self , __snake_case ):
snake_case = list_of_points
# Degree determines the flexibility of the curve.
# De... | 213 | 1 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( a , a , a , a , a ) -> int:
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if len(a ) == 0:
raise ValueError('Scores cannot be e... | 280 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase : Any = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available(... | 280 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCamelCase ( self : List[str] ) -> T... | 353 | """simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
UpperCAmelCase = """\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex... | 54 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = [0] * len(__UpperCAmelCase )
snake_case_ = []
snake_case_ = [1] * len(__UpperCAmelCase )
for values in graph.values():
for i ... | 56 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x... | 56 | 1 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_... | 168 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from trans... | 168 | 1 |
"""simple docstring"""
from math import sqrt
def UpperCAmelCase__ ( lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
lowercase = 0
for i in range(1 , int(sqrt(lowerCAmelCase__ ) + 1 ) ):
if n % i == 0 and i != sqrt(lo... | 197 | """simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[Any] ) -> int:
'''simple docstring'''
lowercase = [0 for i in range(r + 1 )]
# nc0 = 1
lowercase = 1
for i in range... | 197 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
... | 205 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bar... | 205 | 1 |
'''simple docstring'''
import math
def _lowerCamelCase ( lowercase : int ) -> list[int]:
_a = []
_a = 2
_a = int(math.sqrt(lowercase ) ) # Size of every segment
_a = [True] * (end + 1)
_a = []
while start <= end... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int ) -> bool:
_a = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 63 | 1 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__Uppe... | 363 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transfo... | 168 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __magic_name__ ( __lowerCAmelCase):
def __init__( self : List[str] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Tuple ... | 146 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmelCase) , "Tatoeba directory ... | 146 | 1 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A_ :Optional[Any] = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
... | 245 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ :Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHI... | 245 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
lowerCAmelCase__ = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies ... | 72 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
... | 32 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acceler... | 304 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase = logging.... | 304 | 1 |
"""simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : int = 10_00 ):
lowercase_ , lowercase_ : List[str] = 1, 1
lowercase_ : int = 2
while True:
lowercase_ : int = 0
lowercase_ : Tuple ... | 213 | """simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_t... | 213 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_SCREAMING_SNAKE_CASE = get_tests_d... | 165 | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import Mo... | 165 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is... | 23 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 54 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE = loggin... | 230 |
"""simple docstring"""
import random
from .binary_exp_mod import bin_exp_mod
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=10_00 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
A__ = n - 1
A__ = 0
while... | 230 | 1 |
'''simple docstring'''
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 AddedToke... | 168 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return int(input_a == input_a == 0 )
def _A () -> None:
'''simple docstring'''
print(... | 168 | 1 |
"""simple docstring"""
import cva
import numpy as np
class a_ :
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
if k in (0.04, 0.06):
lowerCamelCase__ : Tuple = k
lower... | 316 |
"""simple docstring"""
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
fro... | 316 | 1 |
def a ( A__ : int , A__ : int , A__ : int ) -> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
_lowercase =_modexpt(A__ , exponent // 2 , A__ ) % modulo... | 205 |
# 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 ... | 205 | 1 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowercase ( lowerCAmelCase__ : Callable , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float ) -> np.ndar... | 371 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def lowercase ( lowerCAmelCase__ : ... | 11 | 0 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = "▁"
__A = {"vocab_file": "v... | 177 |
'''simple docstring'''
def _A (lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str ) -> List[Any]:
'''simple docstring'''
if height >= 1:
move_tower(height -... | 168 | 0 |
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 290 |
import inspect
import unittest
from transformers import MobileViTConfig
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... | 290 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCAmelCase__ : Union[str, Any] = TypeVar("""KEY""")
UpperCAmelCase__ : Any = TypeVar("""VAL""")
@dataclass(frozen=UpperCAmelCase , slots=Uppe... | 245 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowercase ( _A ) -> List[Tuple[int, ...]]:
SCREAMING_SNAK... | 245 | 1 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCAmelCase__ = 0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_... | 40 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visio... | 40 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Norma... | 304 |
'''simple docstring'''
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 Option... | 304 | 1 |
UpperCAmelCase : Any = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
UpperCAmelCase : Tuple ... | 66 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowercase ( pl.LightningModule ):
"""simple docstring"""
def __init__( self , A ) -> Any:
... | 66 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitI... | 165 |
"""simple docstring"""
from math import ceil
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = list(range(0 , snake_case__ ) )
SCREAMING_SNAKE_CASE__ = [item for sublist in list(device_map.value... | 165 | 1 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging impo... | 227 |
'''simple docstring'''
def lowerCAmelCase__ ( lowerCamelCase : int = 10 ):
if not isinstance(lowerCamelCase ,lowerCamelCase ) or n < 0:
raise ValueError('Invalid input' )
_A : Optional[Any] = 10**n
_A : List[str] = 2... | 227 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 I... | 230 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''distilbert-base-uncased''': '''https://huggingfa... | 230 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase = 1_0_0_0 ):
__snake_case , __snake_case : List[str] = 1, 1
__snake_case : List[Any] = 2
while True:
__snake_case : Tuple = 0
__snake_case : ... | 134 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_snake_case : List[str] = namedtuple(
"_TestCommandArgs",
[
"da... | 134 | 1 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.b... | 228 |
from __future__ import annotations
from collections.abc import Iterator
class __lowerCAmelCase :
def __init__( self :Optional[Any] , __magic_name__ :int ):
'''simple docstring'''
a = value
a ... | 228 | 1 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logg... | 212 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
requir... | 212 | 1 |
"""simple docstring"""
from math import factorial, radians
def _snake_case ( lowerCamelCase__ : float , lowerCamelCase__ : int = 18 , lowerCamelCase__ : int = 10 ) -> Optional[int]:
lowerCamelCase_ : str =angle_in_degrees - ((angle_in_degrees //... | 144 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 11 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import ... | 360 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCRE... | 27 | 0 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_model... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCRE... | 290 | 1 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
a_ : Tuple =(DDPMScheduler,)
def UpperCamelCase_ ( self : Optional[int] , **UpperCamelCase : ... | 358 |
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 _PACKAGED_DATAS... | 260 | 0 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Calla... | 40 |
"""simple docstring"""
# 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.or... | 40 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from ... | 110 |
import argparse
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 accelerate import Accelerator, DistributedTyp... | 110 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase ( unittest.TestCase ... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 | 1 |
"""simple docstring"""
import unittest
import numpy as np
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
if is_torch_avail... | 24 |
"""simple docstring"""
from collections.abc import Callable
def lowercase ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) ->float:
"""simple docstring"""
__snake_case : float = a
__sn... | 24 | 1 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 8 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import requests
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_inp... | 367 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
A_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
def __init__( self : Optional[int] , snake_case : List[str]=None , ... | 296 | 0 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def __lowerCamelCase ( __snake_case : list[int], __snake_case : list[int], __snake_case : int ) -> list[int]:
"""simple docstring"""
A__ : Any =[0] * no_... | 134 |
'''simple docstring'''
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
__snake_case : Union[str, Any] ... | 134 | 1 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 6 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
a_ : List[Any] = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={ar... | 6 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCamelCase__ = TypeVar("""T""")
lowerCamelCase__ = TypeVar("""U""")
class A__ ( Generic[T, U] ):
def __init__( self : ... | 212 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
... | 212 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_efficientformer": [
"EFFICIENTFO... | 11 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
... | 11 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transfo... | 32 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : int = int(number**0.5 )
return number == sq * sq
def lowerCamelCase (_SCRE... | 27 | 0 |
import math
def __lowercase ( _UpperCamelCase ) ->list:
"""simple docstring"""
lowercase : Optional[int] = [True] * n
lowercase : Union[str, Any] = False
lowercase : Tuple = False
lo... | 367 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - use... | 173 | 0 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
snake_case_ = f"""Input value of [number={number}] must be an integer"""
raise TypeError(_SCREAMING_SNAKE_CASE )
if nu... | 347 |
"""simple docstring"""
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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.uti... | 347 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ... | 33 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __versi... | 33 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
... | 110 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(SCREAMING_SNAKE_CASE , exponent // 2 , SCREAMI... | 110 | 1 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuratio... | 221 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
... | 221 | 1 |
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