code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
... | 684 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils impo... | 684 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
f... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list:
snake_case : str = len(lowercase )
snake_case : Tuple = []
for i in range(len(lowercase ) - pat_len + 1 ):
snake_case : str = True
for j in range(lowercase ):
... | 684 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_availabl... | 684 |
import numpy as np
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availab... | 684 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Tuple = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if... | 684 | 1 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCamelCase : int = logging.get_logger(__name__)
class __lowercase :
"""simple docstring"""
... | 684 |
lowerCamelCase : Union[str, 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'
lowerCamelCase : ... | 684 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Tuple = logging.get_logger(__name__)
lowerCamelCase : Any = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-... | 684 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {'vocab_... | 684 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transf... | 684 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class ... | 684 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __lowercase (UpperCamelCase__ ):
"""... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Optional[Any] = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
snake_case : Any = hex_num[0] == """-"""
if is_negative:
snake_case ... | 684 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case : Union[str, Any] = str(bin(lowercase ) )[2:] # remove the leading "0b"
snake_case : Union[str... | 684 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 684 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __lowerca... | 684 |
import inspect
import unittest
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperC... | 684 | 1 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCamelCase : int = '<<<<<<< This should probably be modified because it mentions: '
lowerCamelCase : Tuple = '=... | 684 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from... | 684 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase__ )
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_... | 684 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolv... | 684 | 1 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch... | 684 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto imp... | 684 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase=1024 ) -> Dict:
snake_case , snake_case : Union[str, Any] = [], []
sn... | 684 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401... | 684 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Optional[int] ... | 684 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> Tuple:
# Initialise PyTorch model
... | 684 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : str = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG... | 684 |
from ..utils import DummyObject, requires_backends
class __lowercase (metaclass=UpperCamelCase__ ):
"""simple docstring"""
_snake_case = ["""flax"""]
def __init__( self , *A , **A ) -> Tuple:
requires_backends(self , ["""fl... | 684 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
'funnel-transf... | 684 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase : List[str] = 3
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
print("""Generating primitive root of p""" )
while True:
snake_case : O... | 684 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase = 1 / sqrt(2 ) ) -> IIRFilter:
snake_case : Dict = tau * frequency / samplerate
snake_case : int = sin(lowe... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case : Dict = _modexpt(lowercase ,exponent // 2 ,lowercase ) % modulo_value
return (x * x) % modulo_value
else:
... | 684 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> List[Any]: # noqa: E741
snake_case : str = len(lowercase )
snake_case : Optional[Any] = 0
snake_case : Union[str, Any] = [0] * n
snake_case : List[Any] = [False] * n
snake_case ... | 684 |
from itertools import product
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list[int]:
snake_case : Tuple = sides_number
snake_case : List[str] = max_face_number * dice_number
snake_case : Any = [0] * (max_total + 1)
snake_ca... | 684 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> list:
snake_case : Union[str, Any] = len(lowercase )
for i in range(1 ,lowercase ):
snake_case : Union[str, Any] = collection[i]
snake_case : List[Any] = 0
snake_case : Opti... | 684 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailabl... | 684 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
def UpperCAmelCase ( self , A ) -> float:
return 0.0... | 684 |
import os
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
with open(os.path.dirname(lowercase ) + """/grid.txt""" ) as f:
snake_case : Tuple = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowercase ) for x in f.readline().split()] )
snake_cas... | 684 | 1 |
import datasets
from .evaluate import evaluate
lowerCamelCase : Union[str, Any] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint ... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> list:
for i in range(len(lowercase ) - 1 ,0 ,-1 ):
snake_case : Any = False
for j in range(lowercase ,0 ,-1 ):
if unsorted[j] < unsorted[j - 1]:
snake_case , snake_case : Option... | 684 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[str]:
sna... | 684 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils impo... | 684 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Tuple:
# A local function to see if a dot lands in the circle.
def is_in_circle(lowercase ,lowercase ) -> bool:
s... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list:
snake_case : str = len(lowercase )
snake_case : Tuple = []
for i in range(len(lowercase ) - pat_len + 1 ):
snake_case : str = True
for j in range(lowercase ):
... | 684 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case : Dict = _modexpt(lowercase ,exponent // 2 ,lowercase ) % modulo_value
return (x * x) % modulo_value
else:
... | 684 |
import numpy as np
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,... | 684 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Tuple = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if... | 684 | 1 |
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> tuple:
snake_case : Tuple = namedtuple("""result""" ,"""name value""" )
if (voltage, current, power).count(0 ) != 1:
raise... | 684 |
lowerCamelCase : Union[str, 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'
lowerCamelCase : ... | 684 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase : Optional[Any] = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 684 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {'vocab_... | 684 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers... | 684 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class ... | 684 | 1 |
import requests
lowerCamelCase : int = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> None:
# fetching a list of articles in json format
snake_case : Optional[int] = requests.get(_NEWS_API + bbc_... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Optional[Any] = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
snake_case : Any = hex_num[0] == """-"""
if is_negative:
snake_case ... | 684 | 1 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __l... | 684 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 684 | 1 |
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
snake_case : Optional[Any] = []
snake_case : Optional[int] = 1
while len(lowercase ) < 1E6:
constant.append(str(lowercase ) )
i += 1
snake_case : Optional[Any] = """""".join(lower... | 684 |
import inspect
import unittest
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperC... | 684 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 684 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from... | 684 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : List[str] = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
'Sque... | 684 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolv... | 684 | 1 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowerCamelCase : Optional[int] = TypeVar('T')
class __lowercase (Generic[T] ):
"""simple docstring"""
_snake_case = 42 # Cache store of keys
_sn... | 684 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto imp... | 684 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
... | 684 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401... | 684 | 1 |
import math
class __lowercase :
"""simple docstring"""
def __init__( self , A=0 ) -> Tuple: # a graph with Node 0,1,...,N-1
snake_case : int = n
snake_case : Optional[int] = [
[math.inf for j in range(0 ,... | 684 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> Tuple:
# Initialise PyTorch model
... | 684 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Optional[int] = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertCo... | 684 |
from ..utils import DummyObject, requires_backends
class __lowercase (metaclass=UpperCamelCase__ ):
"""simple docstring"""
_snake_case = ["""flax"""]
def __init__( self , *A , **A ) -> Tuple:
requires_backends(self , ["""fl... | 684 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set... | 684 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase : List[str] = 3
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
print("""Generating primitive root of p""" )
while True:
snake_case : O... | 684 | 1 |
from ..utils import DummyObject, requires_backends
class __lowercase (metaclass=UpperCamelCase__ ):
"""simple docstring"""
_snake_case = ["""flax""", """transformers"""]
def __init__( self , *A , **A ) -> List[str]:
requires_backen... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case : Dict = _modexpt(lowercase ,exponent // 2 ,lowercase ) % modulo_value
return (x * x) % modulo_value
else:
... | 684 | 1 |
import os
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
with open(os.path.dirname(lowercase ) + """/grid.txt""" ) as f:
snake_case : Tuple = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowercase ) for x in f.readline().split()] )
snake_cas... | 684 |
from itertools import product
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list[int]:
snake_case : Tuple = sides_number
snake_case : List[str] = max_face_number * dice_number
snake_case : Any = [0] * (max_total + 1)
snake_ca... | 684 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Optional[int] = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAI... | 684 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailabl... | 684 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> Tuple:
# Initialise PyTorch model
... | 684 |
import os
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
with open(os.path.dirname(lowercase ) + """/grid.txt""" ) as f:
snake_case : Tuple = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowercase ) for x in f.readline().split()] )
snake_cas... | 684 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __lowercase (unittest.TestCase , UpperCamelCase__ ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> Union[str, Any]:
snake_case : str ... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> list:
for i in range(len(lowercase ) - 1 ,0 ,-1 ):
snake_case : Any = False
for j in range(lowercase ,0 ,-1 ):
if unsorted[j] < unsorted[j - 1]:
snake_case , snake_case : Option... | 684 | 1 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> list[int]:
snake_case : str = [0] * no_of_processes
snake_case : Optional[Any] = [0] * no_of_processes
# Initialize rema... | 684 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils impo... | 684 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelC... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list:
snake_case : str = len(lowercase )
snake_case : Tuple = []
for i in range(len(lowercase ) - pat_len + 1 ):
snake_case : str = True
for j in range(lowercase ):
... | 684 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCamelCase : Optional[Any] = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation... | 684 |
import numpy as np
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Tuple = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if... | 684 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Tuple = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if... | 684 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Tuple = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.jso... | 684 |
lowerCamelCase : Union[str, 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'
lowerCamelCase : ... | 684 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __lowercase (nn.Module ):
"""simple docstring"""
_snake_case = 42
_snake_case ... | 684 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {'vocab_... | 684 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase = False ) -> str:
if not isinstance(lowercase ,lowercase ):
snake_case : str = f"""Expected string as input, found {type(lowercase )}"""
raise ValueError(lowercase )
if not isinstance(lowercase ,lowercase ... | 684 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class ... | 684 | 1 |
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,
)
from .test_p... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Optional[Any] = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
snake_case : Any = hex_num[0] == """-"""
if is_negative:
snake_case ... | 684 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils im... | 684 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 684 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_ut... | 684 |
import inspect
import unittest
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperC... | 684 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder impor... | 684 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from... | 684 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Tuple = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
'facebook/xmod-base': 'https://... | 684 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolv... | 684 | 1 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
lowerCamelCase : List[Any] = 'scheduler_config.json'
class __lowercase (UpperCamelCase__ ):
"""simple docstr... | 684 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto imp... | 684 | 1 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __lowercase (UpperCamel... | 684 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401... | 684 | 1 |
from itertools import permutations
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
snake_case : Dict = [7, 11, 13, 17]
... | 684 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> Tuple:
# Initialise PyTorch model
... | 684 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 684 |
from ..utils import DummyObject, requires_backends
class __lowercase (metaclass=UpperCamelCase__ ):
"""simple docstring"""
_snake_case = ["""flax"""]
def __init__( self , *A , **A ) -> Tuple:
requires_backends(self , ["""fl... | 684 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> Union[str, Any]:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
snake_case : Any = (boundary[1] - boundary[0]) / steps
snake_case : Optional[Any] = boundary[0]
snake_case ... | 684 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase : List[str] = 3
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
print("""Generating primitive root of p""" )
while True:
snake_case : O... | 684 | 1 |
import os
from collections.abc import Iterator
def SCREAMING_SNAKE_CASE__ ( lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(lowercase ):
snake_case : Any = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case : Dict = _modexpt(lowercase ,exponent // 2 ,lowercase ) % modulo_value
return (x * x) % modulo_value
else:
... | 684 | 1 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
... | 684 |
from itertools import product
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list[int]:
snake_case : Tuple = sides_number
snake_case : List[str] = max_face_number * dice_number
snake_case : Any = [0] * (max_total + 1)
snake_ca... | 684 | 1 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import ... | 684 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailabl... | 684 | 1 |
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... | 684 |
import os
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
with open(os.path.dirname(lowercase ) + """/grid.txt""" ) as f:
snake_case : Tuple = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowercase ) for x in f.readline().split()] )
snake_cas... | 684 | 1 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impor... | 684 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> list:
for i in range(len(lowercase ) - 1 ,0 ,-1 ):
snake_case : Any = False
for j in range(lowercase ,0 ,-1 ):
if unsorted[j] < unsorted[j - 1]:
snake_case , snake_case : Option... | 684 | 1 |
from __future__ import annotations
lowerCamelCase : List[Any] = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
lowerCamelCase : int = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> list[float]:
... | 684 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils impo... | 684 | 1 |
import requests
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> None:
snake_case : Any = {"""Content-Type""": """application/json"""}
snake_case : Optional[int] = requests.post(SCREAMING_SNAKE_CASE_ ,json={"""text""": message_body} ,headers=SCREA... | 700 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list:
snake_case : str = len(lowercase )
snake_case : Tuple = []
for i in range(len(lowercase ) - pat_len + 1 ):
snake_case : str = True
for j in range(lowercase ):
... | 684 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> Dict:
if number > 0:
raise ValueError("""input must be a negative integer""" )
snake_case : List[str] = len(bin(__A )[3:] )
snake_case : Optional[Any] = bin(abs(__A ) - (1 << binary_number_length) )[3:]... | 701 |
import numpy as np
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 702 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Tuple = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if... | 684 | 0 |
class __lowercase :
"""simple docstring"""
def __init__( self , A , A , A ) -> Union[str, Any]:
snake_case : Any = None
snake_case : List[str] = None
snake_case : str = graph
self... | 703 |
lowerCamelCase : Union[str, 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'
lowerCamelCase : ... | 684 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCamelCase : List[str] = logging.getLogger(__name__)
class __lowercase (UpperCamelCase__ ... | 704 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {'vocab_... | 684 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowercase = 3 ,lowercase = 7 ,lowercase = 1000000 ) -> Union[str, Any]:
snake_case : List[str] = 0
snake_case : List[str] = 1
for current_denominator in range(1 ,limit + 1 ):
snake_case : Optional[Any] =... | 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class ... | 684 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> tuple[float, float]:
# Check if the input is valid
if not len(lowerCamelCase_ ) == len(lowerCamelCase_ ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] == equationa[1] == equationa[0] == equat... | 706 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Optional[Any] = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
snake_case : Any = hex_num[0] == """-"""
if is_negative:
snake_case ... | 684 | 0 |
class __lowercase :
"""simple docstring"""
def __init__( self , A ) -> Union[str, Any]:
snake_case : Dict = n
snake_case : Dict = [None] * self.n
snake_case : Tuple = 0 # index of the first element
... | 707 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 684 | 0 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __lowercase (unittest.TestCase ):
"""simple docstring"""
... | 708 |
import inspect
import unittest
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperC... | 684 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...... | 709 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from... | 684 | 0 |
from __future__ import annotations
lowerCamelCase : Optional[Any] = 8.988e9 # units = N * m^s * C^-2
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ) -> dict[str, float]:
snake_case : Optional[int] = abs(chargea * chargea )
if (fo... | 710 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolv... | 684 | 0 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : int = {
'vocab_file': 'vocab.txt',
'merges_fil... | 711 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto imp... | 684 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case : List[Any] = 1
snake_case : int = 1
while repunit:
snake_case : str = (10 * repunit + 1) ... | 712 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401... | 684 | 0 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathL... | 713 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> Tuple:
# Initialise PyTorch model
... | 684 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tor... | 714 |
from ..utils import DummyObject, requires_backends
class __lowercase (metaclass=UpperCamelCase__ ):
"""simple docstring"""
_snake_case = ["""flax"""]
def __init__( self , *A , **A ) -> Tuple:
requires_backends(self , ["""fl... | 684 | 0 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __lowercase :
"""simple docstring"""
_snake_case = None
_snake_case = False
_snake_case = False
_snake_case ... | 715 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCamelCase : List[str] = 3
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
print("""Generating primitive root of p""" )
while True:
snake_case : O... | 684 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/r... | 716 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case : Dict = _modexpt(lowercase ,exponent // 2 ,lowercase ) % modulo_value
return (x * x) % modulo_value
else:
... | 684 | 0 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : List[str] = logging.get_logger(__name__)
# TODO Update this
lowerCamelCase : Dict = {
'facebook/esm-1b': 'https://huggingf... | 717 |
from itertools import product
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list[int]:
snake_case : Tuple = sides_number
snake_case : List[str] = max_face_number * dice_number
snake_case : Any = [0] * (max_total + 1)
snake_ca... | 684 | 0 |
from collections import namedtuple
lowerCamelCase : List[str] = namedtuple('from_to', 'from_ to')
lowerCamelCase : Any = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 1_0_0_0),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.0_0454, 2_6_4.1_7_2),
"cubicyard": from_to(... | 718 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailabl... | 684 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowerCamelCase : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowercase ( _Uppe... | 719 |
import os
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
with open(os.path.dirname(lowercase ) + """/grid.txt""" ) as f:
snake_case : Tuple = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowercase ) for x in f.readline().split()] )
snake_cas... | 684 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> bool:
if not isinstance(lowercase ,lowercase ):
snake_case : Dict = f"""Input value of [number={number}] must be an integer"""
raise TypeError(lowercase )
if number < 0:
return False
snake_case : D... | 720 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> list:
for i in range(len(lowercase ) - 1 ,0 ,-1 ):
snake_case : Any = False
for j in range(lowercase ,0 ,-1 ):
if unsorted[j] < unsorted[j - 1]:
snake_case , snake_case : Option... | 684 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
lowerCamelCase : str = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
lowerCamelCase : int = ... | 721 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils impo... | 684 | 0 |
import math
def SCREAMING_SNAKE_CASE__ ( lowercase = 100 ) -> Tuple:
snake_case : Any = sum(i * i for i in range(1 ,n + 1 ) )
snake_case : Optional[Any] = int(math.pow(sum(range(1 ,n + 1 ) ) ,2 ) )
return square_of_sum - sum_of_squares
if __n... | 700 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> list:
snake_case : str = len(lowercase )
snake_case : Tuple = []
for i in range(len(lowercase ) - pat_len + 1 ):
snake_case : str = True
for j in range(lowercase ):
... | 684 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
... | 701 |
import numpy as np
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 0 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput... | 702 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Tuple = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if... | 684 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
c... | 703 |
lowerCamelCase : Union[str, 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'
lowerCamelCase : ... | 684 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" ,[
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_j... | 704 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {'vocab_... | 684 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_avail... | 705 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class ... | 684 | 0 |
lowerCamelCase : Dict = range(2, 2_0 + 1)
lowerCamelCase : Dict = [1_0**k for k in range(ks[-1] + 1)]
lowerCamelCase : int = {}
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ) -> Dict:
snake_case : Union[str, Any] = sum(a_i[... | 706 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Optional[Any] = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
snake_case : Any = hex_num[0] == """-"""
if is_negative:
snake_case ... | 684 | 0 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
... | 707 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 684 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
Vilt... | 708 |
import inspect
import unittest
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperC... | 684 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowercase (a__ ):
... | 709 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from... | 684 | 0 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCamelCase : str = logging.getLogger(__name__)
class __lowercase (UpperCAmelCase__ ):
"""simple docstrin... | 710 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolv... | 684 | 0 |
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