code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
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
__UpperCamelCase : str = False
class __lowerCAmelC... | 228 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 1 |
def __A ( __lowerCamelCase ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
__UpperCamelCase : Union[str, Any] ... | 228 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 1 |
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ... | 228 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self :Union[str, Any] , __magic_name__ :Optional[int]... | 228 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__UpperCamelCase : str = 500_000
__UpperCamelCase , __UpperCamelCase : Tuple = os.path.split(__file__)
__Upper... | 228 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 1 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
S... | 228 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 1 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__UpperCame... | 228 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 228 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenize... | 228 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 1 |
import numpy as np
def __A ( __lowerCamelCase ) -> np.ndarray:
return 1 / (1 + np.exp(-vector ))
def __A ( __lowerCamelCase ) -> np.ndarray:
return vector * sigmoid(__lowerCamelCase )
if __name__ == "__main__":
import doctest
docte... | 228 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_... | 228 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if... | 228 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 1 |
import argparse
import json
import subprocess
def __A ( __lowerCamelCase , __lowerCamelCase ) -> Any:
a = []
a = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
""" https://api.github.co... | 228 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 228 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_roberta... | 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 |
class __lowerCAmelCase :
def __init__( self :Dict , __magic_name__ :Union[str, Any] , __magic_name__ :List[str] ):
'''simple docstring'''
a = name
a = val
def __str__( ... | 228 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 1 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 228 |
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ... | 228 | 1 |
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,
)
... | 228 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = Dis... | 228 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : Optional[int]... | 228 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCa... | 228 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def __A ( __lowerCamelCase = 200_0000 ) -> int:
a = [0]
a = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_... | 228 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str... | 228 | 1 |
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)
warnings.warn(
"The conv... | 228 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __A ( ) -> Any:
a = ArgumentParser(
description=(
"""PyTorch TPU distributed training ... | 228 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__UpperCamelCase : Optional[Any] = Mapping[str, np.ndarray]
__UpperCamelCase : List[str... | 228 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pi... | 228 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str... | 228 |
import argparse
import os
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 impor... | 228 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_e... | 228 |
__UpperCamelCase : Optional[int] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builde... | 228 | 1 |
def __A ( __lowerCamelCase = 5000_0000 ) -> int:
a = set()
a = int((limit - 24) ** (1 / 2) )
a = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 ... | 228 |
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,
)
... | 228 | 1 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 1 |
from collections.abc import Iterable
from typing import Any
class __lowerCAmelCase :
def __init__( self :List[str] , __magic_name__ :int | None = None ):
'''simple docstring'''
a = value
a = ... | 228 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
@register_to_config
def __init__( self :int , *,
__... | 228 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self :Union[str, Any] , __magic_name__ :Optional[int]... | 228 | 1 |
from __future__ import annotations
from math import pi
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0"... | 228 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 1 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCa... | 228 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 1 |
def __A ( __lowerCamelCase ) -> Union[str, Any]:
a = len(__lowerCamelCase )
for i in range(length - 1 ):
a = i
for k in range(i + 1 , __lowerCamelCase ):
if collection[k] < collection[least]:
a = k
if least !=... | 228 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 228 | 1 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
a = [1]
for i in range(2 , __lowerCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
a = []
a = ... | 228 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
__UpperCamelCase : Optional[int] = get_logger(__name__)
__UpperCamelCase : Tuple = R"\n Ar... | 228 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_... | 228 | 1 |
from math import factorial
def __A ( __lowerCamelCase = 100 ) -> int:
return sum(map(__lowerCamelCase , str(factorial(__lowerCamelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 228 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 1 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main_... | 228 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 228 | 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 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having... | 228 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 1 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_... | 228 |
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ... | 228 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Union[str, Any] = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MC... | 228 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import... | 228 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 1 |
def __A ( __lowerCamelCase ) -> str:
return "".join(chr(ord(__lowerCamelCase ) - 32 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 228 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCa... | 228 | 1 |
__UpperCamelCase : Tuple = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import Strea... | 228 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str... | 228 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_speech_to_text": ["SPEECH_TO_T... | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __A ( ) -> Any:
a = ArgumentParser(
description=(
"""PyTorch TPU distributed training ... | 228 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE_: List[str] ={0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
SCREAMING_SNAKE_CASE_: Dict ={0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def lowerCAmelCase_ ( snake_case_ : dict[int, list[int]] , snake_case_ : int , sn... | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pi... | 228 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCamelCase : Optional[int] ... | 2 |
import argparse
import os
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 impor... | 228 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : Any = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 3 |
__UpperCamelCase : Optional[int] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builde... | 228 | 0 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class UpperCAmelCase_ ( unittest.TestCase ):
def __UpperCAmelCase ( self : List[str] ... | 4 |
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,
)
... | 228 | 0 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAm... | 5 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 0 |
import os
import numpy
import onnx
def __lowerCAmelCase ( a__ , a__ ) -> Optional[int]:
__a = a.name
__a = b.name
__a = ''''''
__a = ''''''
__a = a == b
__a = name_a
__a = name_... | 6 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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,
)
... | 7 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self :Union[str, Any] , __magic_name__ :Optional[int]... | 228 | 0 |
lowerCAmelCase_ = 2_56
# Modulus to hash a string
lowerCAmelCase_ = 1_00_00_03
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = len(SCREAMING_SNAKE_CASE__ )
snake_case_ = len(SCREAMING_SNAKE_CASE__ )
... | 8 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Confi... | 9 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__A = logging.get_logger(__name__)
def lowerCAmelCase_ ( __a , __a ) -> str:
"""simple docstring"""
lowerCamelCase__: Li... | 10 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 228 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowerCAmelCase__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), ... | 11 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 0 |
import numpy as np
def lowerCamelCase__ ( A__ : np.ndarray ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowerCamelCase__ ( A__ : np.ndarray ):
'''simple docstring'''
return vector * sigmoid(A_... | 12 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_... | 228 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase : List[Any] = {
"""sh... | 13 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
_lowerCamelCase : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
def SCREAMING_S... | 14 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 228 | 0 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def UpperCAmelCase ( a_ ) -> l... | 15 |
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 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
... | 16 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 0 |
"""simple docstring"""
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, BertTok... | 17 |
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ... | 228 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditi... | 18 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 0 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterM... | 19 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_... | 20 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCa... | 228 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 21 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str... | 228 | 0 |
'''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_whitespace,
)
from t... | 22 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __A ( ) -> Any:
a = ArgumentParser(
description=(
"""PyTorch TPU distributed training ... | 228 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class SCREAMING_SNAKE_CASE( datasets.BeamBasedBuilder ):
... | 23 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pi... | 228 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , ... | 24 |
import argparse
import os
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 impor... | 228 | 0 |
"""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 lowercase_ ( _snake_case ,_snake_case ,_snake_case ,... | 25 |
__UpperCamelCase : Optional[int] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builde... | 228 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
_snake_case = 6_3_7_8_1_3_7.0
_snake_case = 6_3_5_6_7_5_2.3_1_4_2_4_5
_snake_case = 6378137
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_... | 26 |
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,
)
... | 228 | 0 |
'''simple docstring'''
import os
import sys
__lowercase : List[Any] = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModel... | 27 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 0 |
'''simple docstring'''
_lowerCamelCase : str = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def __lowerCamelCase ( A__ ) -> bytes:
"""simple docstring"""
# Make sure the supplied data is a bytes-like object
if not is... | 28 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 0 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class lowerCamelCase (_snake_case ):
'''... | 29 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self :Union[str, Any] , __magic_name__ :Optional[int]... | 228 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMTokenizer'],
}
try:
if ... | 30 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( _UpperCAmelCase : list[int | str] ) -> None:
"""simple docstring"""
create_state_space_tree(_UpperCAmelCase , [] , 0 , [0 for i in range(len(_UpperCAmelCase ) )] )
def U... | 31 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 0 |
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> bool:
"""simple docstring"""
a_ : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 32 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 228 | 0 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : Dict = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def lowercase ( __snake_case : List[Any] , __snake_case : Dic... | 33 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin... | 34 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_... | 228 | 0 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokeni... | 35 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 0 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.rob... | 36 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 228 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase = {'''processing_layoutxlm''': ['''La... | 37 |
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 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import Backbone... | 38 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 0 |
from __future__ import annotations
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> Any:
"""simple docstring"""
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(__lowerCAmelCase ):
print(F"""{i}\t\t{d}""" )
... | 39 |
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ... | 228 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class _A :
"""simple docstring"""
def __init__( self : str , __UpperCAmelCase : list[str]):
a : list[dict] = [... | 40 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 0 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> list[int]:
lowerCamelCase__ : Tuple = []
lowerCamelCase__ : int = 2
lowerCamelCase__ : str = int(... | 41 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 0 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class __UpperCAmelCase ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
super().__init__()
self.re... | 42 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCa... | 228 | 0 |
from string import ascii_lowercase, ascii_uppercase
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not sentence:
return ""
__UpperCamelCase :Optional[Any] = dict(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
return lower_to_upper.get(sentenc... | 43 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str... | 228 | 0 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 44 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __A ( ) -> Any:
a = ArgumentParser(
description=(
"""PyTorch TPU distributed training ... | 228 | 0 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iter... | 45 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pi... | 228 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import... | 46 |
import argparse
import os
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 impor... | 228 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : Dict = {
"googl... | 47 |
__UpperCamelCase : Optional[int] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builde... | 228 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : List[Any] = CustomTokenizer
pass
| 48 |
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,
)
... | 228 | 0 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase ):
__a = [True] * limit
__a = False
__a = False
__a = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
__a = i * 2... | 49 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 0 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() an... | 50 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 0 |
from copy import deepcopy
class __snake_case :
def __init__( self : List[str] , _snake_case : list[int] | None = None , _snake_case : int | None = None):
"""simple docstring"""
if arr is None and size is not No... | 51 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self :Union[str, Any] , __magic_name__ :Optional[int]... | 228 | 0 |
__lowerCamelCase : Optional[Any] = tuple[float, float, float]
__lowerCamelCase : int = tuple[float, float, float]
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Vectorad:
UpperCamelCase : Optional[Any] = end_pointa[0] - end_pointa[0]
Upp... | 52 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 0 |
'''simple docstring'''
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils imp... | 53 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
for ch in input_str:
__SCREAMING_SNAKE_CASE = ord(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = pow(2 , lo... | 54 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 228 | 0 |
'''simple docstring'''
import itertools
import math
def __snake_case ( UpperCAmelCase_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are n... | 55 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
while b:
snake_case_ ,snake_case_ = b, a % b
return a
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''s... | 56 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_... | 228 | 0 |
"""simple docstring"""
A : int = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
1_3_3,
1_1_1,
8_8,
6_6,
4_... | 57 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...... | 58 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 228 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __lowerCamelCase : str = "" ):
snake_case : Dict = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
snake_case : Union[str, An... | 59 |
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 | 0 |
"""simple docstring"""
import sys
snake_case__ : str = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 60 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 0 |
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