code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCamelCase( ):
UpperCAmelCase : Optional[Any] = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
UpperCAmelCase : List[str] = ... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase_ , int(b / 2 ) ) * actual_power(UpperCAmelCase_ , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase_ , in... | 280 | 1 |
'''simple docstring'''
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, USE... | 280 |
'''simple docstring'''
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
... | 280 | 1 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 280 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowercase__ = re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__ = re.compile(r"([a-z\d])([A-Z])")
lowercase__ = re.compile(r"(?<!_)_(?!_)")
lowercase__ = re.compile(r"(_{2... | 280 | 1 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowercase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_to... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm"... | 280 | 1 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
lowercase__ = ""
lowercase__ = ""
lowercase__ = ""
lowercase__ = ""
def UpperCamelCase( UpperCAmelCase_ ):
# authorize twitter, initialize tweepy
UpperC... | 280 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase__ = logging.get_logger("transformers.models.speecht5")
def UpperCamelCase( UpperCAmelCa... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class A_ :
'''simple docstring'''
def __init__( self : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : List[str] = ... | 280 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase( ):
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=UpperCAmelCase_ , default='biencoder-nq-dev.json' ... | 280 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae im... | 280 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ ... | 280 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_time... | 280 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dim... | 280 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.j... | 280 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase__ = TypeVar("KEY")
lowercase__ = TypeVar("VAL")
@dataclass(frozen=_snake_case , slots=_snake_case )
cla... | 280 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=_snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Dict = ["""torch""", """scipy"""]
def __init__( self : Any , *lowercase_... | 280 |
'''simple docstring'''
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... | 280 | 1 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.p... | 280 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list... | 280 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark... | 280 | 1 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowercase__ = logging.get_logger(__name__)
class A_ :
... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
while a != 0:
UpperCAmelCase , UpperCAmelCase : Tuple = b % a, a
return b
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if gcd(UpperCAmelCase_ , UpperCAmelCase_ ... | 280 | 1 |
'''simple docstring'''
import enum
import shutil
import sys
lowercase__ , lowercase__ = shutil.get_terminal_size()
lowercase__ = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class A_ ( enum.Enum ):
'''simple docstring'''
... | 280 |
'''simple docstring'''
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
re... | 280 | 1 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just fo... | 280 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A_ ( unittest.TestCase ... | 280 | 1 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
lowercase__ = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say the... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = (
first_str_length if first_str_length > second_str... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Dict = [0 for i in range(len(UpperCAmelCase_ ) )]
# initialize interval's left pointer and right pointer
UpperCAmelCase , UpperCAmelCase : Tuple = 0, 0
for i in range(1 , len(... | 280 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 280 | 1 |
'''simple docstring'''
import os
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 logging
lowercase__ = logging.get_logger(__name__)
lowe... | 280 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(UpperCAmelCase_ )
or ... | 280 | 1 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolv... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
... | 280 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_... | 280 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase__ = 3
def UpperCamelCase( UpperCAmelCase_ ):
print('Generating primitive root of p' )
while True:
UpperCAmelCase : ... | 280 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = None , ):
UpperCAmelCase : Optional[int] = np.shape(UpperCAmelCase_ )
UpperCAmelCase : Tuple = np.shape(Upp... | 280 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase( UpperCAmelCase_ , UpperCAm... | 280 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELA... | 280 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"facebook/maskformer-s... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = (
first_str_length if first_str_length > second_str... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase_ , int(b / 2 ) ) * actual_power(UpperCAmelCase_ , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase_ , in... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class A_ :
'''simple docstring'''
def __init__( self : Tuple ) -> None:
UpperCAmelCase : list[Any] = []
UpperCAmelCas... | 280 |
'''simple docstring'''
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
... | 280 | 1 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipe... | 280 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowercase__ = re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__ = re.compile(r"([a-z\d])([A-Z])")
lowercase__ = re.compile(r"(?<!_)_(?!_)")
lowercase__ = re.compile(r"(_{2... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase , UpperCAmelCase : Tuple = position
UpperCAmelCase : List[Any] = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2)... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm"... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
return " ".join(
''.join(word[::-1] ) if len(UpperCAmelCase_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef s... | 280 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase__ = logging.get_logger("transformers.models.speecht5")
def UpperCamelCase( UpperCAmelCa... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(UpperCAmelCase_ )
or ... | 280 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase( ):
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=UpperCAmelCase_ , default='biencoder-nq-dev.json' ... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if index == r:
for j in range(UpperCAmelCase_ ):
print(data[j] , end=' ' )
print(' ' )
return
# When no more elem... | 280 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ ... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase_ , int(b / 2 ) ) * actual_power(UpperCAmelCase_ , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase_ , in... | 280 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dim... | 280 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...scheduler... | 280 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase__ = TypeVar("KEY")
lowercase__ = TypeVar("VAL")
@dataclass(frozen=_snake_case , slots=_snake_case )
cla... | 280 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-wi... | 280 |
'''simple docstring'''
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... | 280 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 280 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMult... | 280 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark... | 280 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "V... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
while a != 0:
UpperCAmelCase , UpperCAmelCase : Tuple = b % a, a
return b
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if gcd(UpperCAmelCase_ , UpperCAmelCase_ ... | 280 | 1 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase__ = object()
# For specifying empty leaf dict `{}`
lowercase__ = obj... | 280 |
'''simple docstring'''
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
re... | 280 | 1 |
'''simple docstring'''
class A_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ) -> List[Any]:
UpperCAmelCas... | 280 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A_ ( unittest.TestCase ... | 280 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ ... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = (
first_str_length if first_str_length > second_str... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
UpperCAmelCase : Optional[int] = gray_code_seque... | 280 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum(
divisor for divisor in range(1 ... | 280 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(UpperCAmelCase_ )
or ... | 280 | 1 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
UpperCAmelCase , UpperCAmelCase : List[str] ... | 280 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase__ = 3
def UpperCamelCase( UpperCAmelCase_ ):
print('Generating primitive root of p' )
while True:
UpperCAmelCase : ... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ = 1_00 ):
UpperCAmelCase : Any = n * (n + 1) * (2 * n + 1) / 6
UpperCAmelCase : int = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f'''{solution()... | 280 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase( UpperCAmelCase_ , UpperCAm... | 280 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-larg... | 280 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"facebook/maskformer-s... | 280 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase__ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
rai... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase_ , int(b / 2 ) ) * actual_power(UpperCAmelCase_ , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase_ , in... | 280 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenizat... | 280 |
'''simple docstring'''
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
... | 280 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerca... | 280 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowercase__ = re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__ = re.compile(r"([a-z\d])([A-Z])")
lowercase__ = re.compile(r"(?<!_)_(?!_)")
lowercase__ = re.compile(r"(_{2... | 280 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm"... | 280 | 1 |
'''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 ImageProcessingSavingTestM... | 280 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase__ = logging.get_logger("transformers.models.speecht5")
def UpperCamelCase( UpperCAmelCa... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase( UpperCAmelCase_ = 2_00_00_00 ):
UpperCAmelCase : list[int] = [0]
UpperCAmelCase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) )... | 280 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase( ):
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=UpperCAmelCase_ , default='biencoder-nq-dev.json' ... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class A_ :
'''simple docstring'''
UpperCAmelCase_ : float
UpperCAmelCase_ : TreeNode | None = None
UpperCAmelCase_ : Tree... | 280 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ ... | 280 | 1 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 280 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dim... | 280 | 1 |
'''simple docstring'''
import operator as op
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Tuple = []
UpperCAmelCase : Any = lambda UpperCAmelCase_ , UpperCAmelCase_ : int(x / y ) # noqa: E731 integer division operation
UpperCAmelCase : T... | 280 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase__ = TypeVar("KEY")
lowercase__ = TypeVar("VAL")
@dataclass(frozen=_snake_case , slots=_snake_case )
cla... | 280 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torc... | 280 |
'''simple docstring'''
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... | 280 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = ["""image_processor""", """tokenizer"""]
Upp... | 280 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 | 1 |
'''simple docstring'''
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase__ = "src/transformers"
... | 280 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark... | 280 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
while a != 0:
UpperCAmelCase , UpperCAmelCase : Tuple = b % a, a
return b
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if gcd(UpperCAmelCase_ , UpperCAmelCase_ ... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif stress < 0:
raise... | 280 |
'''simple docstring'''
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
re... | 280 | 1 |
'''simple docstring'''
class A_ :
'''simple docstring'''
def __init__( self : str , lowercase_ : Tuple , lowercase_ : Optional[Any] ) -> Optional[Any]:
UpperCAmelCase : int = name
UpperCAmelCase : Optiona... | 280 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A_ ( unittest.TestCase ... | 280 | 1 |
'''simple docstring'''
import copy
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
lowercase__ = ... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = (
first_str_length if first_str_length > second_str... | 280 | 1 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowercase__ = (
"This metric will be removed from t... | 280 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 280 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(UpperCAmelCase_ )
or ... | 280 | 1 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def UpperCamelCase( UpperCAmelCase_ ):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() )
@pytest.fixture
def ... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Union[str, Any] = 1
for n in range(m + 1 ):
for... | 280 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase__ = 3
def UpperCamelCase( UpperCAmelCase_ ):
print('Generating primitive root of p' )
while True:
UpperCAmelCase : ... | 280 | 1 |
'''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowercase__ = 299792458
# Symbols
lowercase__ , lowercase__ , lowercase__ , lowercase__ = symbols("ct x y z")
def UpperCamelC... | 280 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase( UpperCAmelCase_ , UpperCAm... | 280 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowercase__ = ... | 280 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"facebook/maskformer-s... | 280 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase_ , int(b / 2 ) ) * actual_power(UpperCAmelCase_ , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase_ , in... | 280 | 1 |
'''simple docstring'''
import math
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : str = []
UpperCAmelCase : int = 2
UpperCAmelCase : Any = int(math.sqrt(UpperCAmelCase_ ) ) # Size of every segment
UpperCAmelCase : Tuple ... | 280 |
'''simple docstring'''
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
... | 280 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers impo... | 280 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowercase__ = re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__ = re.compile(r"([a-z\d])([A-Z])")
lowercase__ = re.compile(r"(?<!_)_(?!_)")
lowercase__ = re.compile(r"(_{2... | 280 | 1 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , Up... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm"... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : list[list[str]] = [[] for _ in range(UpperCAmelCase_ )]
UpperCAmelCase : Optional[int] = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negati... | 280 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase__ = logging.get_logger("transformers.models.speecht5")
def UpperCamelCase( UpperCAmelCa... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
if length <= 0 or not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(UpperCAmelCase_ )]
if __name__ == "__main__":
... | 280 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase( ):
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=UpperCAmelCase_ , default='biencoder-nq-dev.json' ... | 280 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from... | 280 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ ... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : str = u
for i in range(1 , UpperCAmelCase_ ):
UpperCAmelCase : Dict = temp * (u - i)
return temp
... | 280 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dim... | 280 | 1 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase( UpperCAmelCase_ ):
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase_ , UpperCAmelC... | 280 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase__ = TypeVar("KEY")
lowercase__ = TypeVar("VAL")
@dataclass(frozen=_snake_case , slots=_snake_case )
cla... | 280 | 1 |
'''simple docstring'''
lowercase__ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAm... | 280 |
'''simple docstring'''
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... | 280 | 1 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowercase__ = re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__ = re.compile(r"([a-z\d])([A-Z])")
lowercase__ = re.compile(r"(?<!_)_(?!_)")
lowercase__ = re.compile(r"(_{2... | 280 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = credit_card_number
UpperCAmelCase : str ... | 280 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark... | 280 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestM... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
while a != 0:
UpperCAmelCase , UpperCAmelCase : Tuple = b % a, a
return b
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if gcd(UpperCAmelCase_ , UpperCAmelCase_ ... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
if any(not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(UpperCAmelCase_ ) ):
for i, (rod_u... | 280 |
'''simple docstring'''
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
re... | 280 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
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...")
lowercase__ = int(input("... | 280 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A_ ( unittest.TestCase ... | 280 | 1 |
'''simple docstring'''
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 parqu... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = len(UpperCAmelCase_ )
UpperCAmelCase : int = (
first_str_length if first_str_length > second_str... | 280 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 280 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.ut... | 280 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(UpperCAmelCase_ )
or ... | 280 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
... | 280 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requir... | 280 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowercase__ = 3
def UpperCamelCase( UpperCAmelCase_ ):
print('Generating primitive root of p' )
while True:
UpperCAmelCase : ... | 280 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
fr... | 280 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase( UpperCAmelCase_ , UpperCAm... | 280 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Gene... | 280 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"facebook/maskformer-s... | 280 | 1 |
'''simple docstring'''
from math import factorial
lowercase__ = {str(d): factorial(d) for d in range(10)}
def UpperCamelCase( UpperCAmelCase_ ):
return sum(DIGIT_FACTORIAL[d] for d in str(UpperCAmelCase_ ) )
def UpperCamelCase( ):
UpperCAmelCase :... | 280 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase_ , int(b / 2 ) ) * actual_power(UpperCAmelCase_ , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase_ , in... | 280 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus... | 280 |
'''simple docstring'''
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
... | 280 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A_ ( unittest.TestCase ... | 280 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowercase__ = re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__ = re.compile(r"([a-z\d])([A-Z])")
lowercase__ = re.compile(r"(?<!_)_(?!_)")
lowercase__ = re.compile(r"(_{2... | 280 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LIC... | 280 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm"... | 280 | 1 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaF... | 280 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase__ = logging.get_logger("transformers.models.speecht5")
def UpperCamelCase( UpperCAmelCa... | 280 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase , UpperCAmelCase : Tuple = set(UpperCAmelCase_ ), [start]
while stack:
UpperCAmelCase : Dict = stack.pop()
explored.add... | 280 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase( ):
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=UpperCAmelCase_ , default='biencoder-nq-dev.json' ... | 280 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase__ = logging.get_logger("transformers.models.speecht5")
def UpperCamelCase( UpperCAmelCa... | 280 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ ... | 280 | 1 |
'''simple docstring'''
import math
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ = 0 , UpperCAmelCase_ = 0 ):
UpperCAmelCase : int = end or len(UpperCAmelCase_ )
for i in range(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : List[Any] = ... | 280 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dim... | 280 | 1 |
'''simple docstring'''
from collections import deque
class A_ :
'''simple docstring'''
def __init__( self : Any , lowercase_ : str , lowercase_ : int , lowercase_ : int ) -> None:
UpperCAmelCase : Op... | 280 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase__ = TypeVar("KEY")
lowercase__ = TypeVar("VAL")
@dataclass(frozen=_snake_case , slots=_snake_case )
cla... | 280 | 1 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDa... | 280 |
'''simple docstring'''
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... | 280 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Cond... | 280 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 280 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ... | 280 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark... | 280 | 1 |
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