code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance ... | 290 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ ... | 290 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __snake_case ( unittest.TestCase , __lowerCAmelCase ):
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
... | 290 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_avai... | 290 | 1 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class __snake_case :
def __init__( self , lowercase , lowercase , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: ... | 290 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTest... | 290 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.4... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attent... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/mai... | 290 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hoppe... | 290 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixi... | 290 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[int]:
a__: List[str] = int(_SCREAMING_SNAKE_CASE )
# Initialize Result
a__: int = []
# Traverse through all denomination
for denomination in reversed(_SCREAMING_SNAKE_CASE ):
... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SN... | 290 | 1 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowercase__ ... | 290 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 1 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "cpu" , _SCREAMING_SNAKE_CASE = None ) ->None:
a__: List[str] = torch.load(_SCREAMING_SNAKE_CASE , map_location=_SCREAM... | 290 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow,... | 290 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import Seque... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
def __a ( _SCREAMING_SNAKE_CASE ) ->list[int]: # This function is recursive
a__: Optional[int] = len(_SCREAMING_SNAKE_CASE )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hoppe... | 290 | 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-2.0
#
# Unles... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAM... | 290 | 1 |
"""simple docstring"""
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 __snake_case ( unittest.TestCase ):
@re... | 290 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase__ = logging.getLogger(__name__)
class __snake_case :
def __init__( self) -> ... | 290 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_avai... | 290 | """simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: int = None
if token is not Non... | 290 | 1 |
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blen... | 290 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All prime... | 290 | 1 |
"""simple docstring"""
from maths.prime_factors import prime_factors
def __a ( _SCREAMING_SNAKE_CASE ) ->int:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Input value of [number={number}] must be an integer'
raise TypeError(_SCR... | 290 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule... | 290 | 1 |
"""simple docstring"""
import operator as op
lowercase__ = 'scaler.pt'
lowercase__ = 'pytorch_model'
lowercase__ = 'random_states'
lowercase__ = 'optimizer'
lowercase__ = 'scheduler'
lowercase__ = 'pytorch_model.bin'
lowercase__ = 'pytorch_m... | 290 | """simple docstring"""
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.... | 290 | 1 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowercase__ ... | 290 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolv... | 290 | 1 |
"""simple docstring"""
lowercase__ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_dis... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
lowercase__ = 8.988E9 # units = N * m^s * C^-2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->dict[str, float]:
a__: List[str] = abs(chargea ... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCRE... | 290 | 1 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowercase__ = logging.get_logger(__name__)
class __sn... | 290 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any ... | 290 | 1 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
s... | 290 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import fr... | 290 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->tuple[float, float]:
# Check if the input is valid
if not len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1... | 290 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ ... | 290 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {'configuration_mbart': ['MBA... | 290 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_avai... | 290 | 1 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
a__: Tuple = 0
if start < end:
a__: Dict = randint(_SCREAMING_SNA... | 290 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor... | 290 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.4... | 290 | 1 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowercase__ = '__DUMMY_TRANSFORMERS_USER__'
lowercase__ = 'Dummy User'
lowercase__ = 'hf_hZEmnoOEYISjraJt... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/mai... | 290 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->str:
a__: Any = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __a ( _SCREAMING_SNAKE_CASE ) ->dict[str, str]:
a__: str = ... | 290 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixi... | 290 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
a__: Any = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SN... | 290 | 1 |
"""simple docstring"""
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 #... | 290 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 1 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 290 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __lowerCAmelCase ):
... | 290 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import Seque... | 290 | 1 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->np.array:
a__: Union[str, Any] = F'{sampling_rate}'
a__: str = '1'
a__:... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hoppe... | 290 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDisc... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAM... | 290 | 1 |
"""simple docstring"""
lowercase__ = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
... | 290 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase__ = logging.getLogger(__name__)
class __snake_case :
def __init__( self) -> ... | 290 | 1 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=7 ) ->Any:
a__: Union[str, Any] = None
if token is not None:
a__: Optional[in... | 290 | """simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: int = None
if token is not Non... | 290 | 1 |
"""simple docstring"""
from math import factorial
def __a ( _SCREAMING_SNAKE_CASE = 100 ) ->int:
return sum(map(_SCREAMING_SNAKE_CASE , str(factorial(_SCREAMING_SNAKE_CASE ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 290 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All prime... | 290 | 1 |
"""simple docstring"""
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def __a ( _SCREAMING_S... | 290 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule... | 290 | 1 |
"""simple docstring"""
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... | 290 | """simple docstring"""
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.... | 290 | 1 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowercase__ = logging.get_logger(__name__)
def __a ( _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: Tuple ... | 290 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolv... | 290 | 1 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __a ( _SCREAMING_SNAKE_CASE ) ->List[str]:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https:/... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_dis... | 290 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowercase__ = logging.getLogger()
def __a ( _SCREAMIN... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCRE... | 290 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface i... | 290 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any ... | 290 | 1 |
"""simple docstring"""
import os
import re
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__)
lowercase__ ... | 290 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import fr... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list:
if len(_SCREAMING_SNAKE_CASE ) != 2 or len(a[0] ) != 2 or len(_SCREAMING_SNAKE_CASE ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x... | 290 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ ... | 290 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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_torc... | 290 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_avai... | 290 | 1 |
"""simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
... | 290 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __snake_case ( __lowerCAmelCase ):
def __init__( self , lowercase , lowercase , lowercase) -> Tuple:
... | 290 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.4... | 290 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_availa... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/mai... | 290 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'Br... | 290 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixi... | 290 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase__ = get_tests_d... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SN... | 290 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
... | 290 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
lowercase__ = list[tuple[int, int]]
lowercase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, ... | 290 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
lowercase__ = logging.get_logger(__name__)
def __a ( _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CA... | 290 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import Seque... | 290 | 1 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
class __snake_case ( __lowerCAmelCase ):
a__ = [... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hoppe... | 290 | 1 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_docst... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAM... | 290 | 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 they saw a cell phone vi... | 290 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase__ = logging.getLogger(__name__)
class __snake_case :
def __init__( self) -> ... | 290 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTeste... | 290 | """simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: int = None
if token is not Non... | 290 | 1 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Optional[Any] = 0
a__: Optional[int] = ... | 290 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All prime... | 290 | 1 |
"""simple docstring"""
lowercase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def __a ( ) ->None:
a__: Optional[Any] = input('Enter message: ' )
a__: List[Any] = input('Enter key [alphanumeric]: ' )
a__: Union[str, Any] = input('Encrypt/Decrypt [e/d]: ' )... | 290 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule... | 290 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 290 | """simple docstring"""
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.... | 290 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase__ = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch... | 290 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolv... | 290 | 1 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContext... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_dis... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class __snake_case :
def __init__( self , lowercase) -> int:
'''simple docstring'''
a__: Dict = list_of_points
# Degree determines th... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCRE... | 290 | 1 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: Optional[int] = (path or []) + [u]
for v in graph[u]:
if visite... | 290 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any ... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowercase__ = 100
lowercase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowercase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
con... | 290 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import fr... | 290 | 1 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metr... | 290 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ ... | 290 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( __lowerCAmelCase ... | 290 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_avai... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->list[tuple[int, int]]:
a__ , a__: Union[str, Any] = position
a__: int = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
... | 290 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixi... | 290 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.4... | 290 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class __snake_case ( ... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/mai... | 290 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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... | 290 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixi... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , ) ->tuple[int, float, str]:
a__: List[Any] = cipher_alphabet or [chr(_SCREAMING_SNAK... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SN... | 290 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolv... | 290 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 1 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - genera... | 290 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTok... | 290 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import Seque... | 290 | 1 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import ... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hoppe... | 290 | 1 |
"""simple docstring"""
lowercase__ = [0, 2, 4, 6, 8]
lowercase__ = [1, 3, 5, 7, 9]
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAM... | 290 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class... | 290 | """simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase__ = logging.getLogger(__name__)
class __snake_case :
def __init__( self) -> ... | 290 | 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 ImageProcessingSavingTestMixin, prepare_im... | 290 | """simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) ->Optional[int]:
a__: int = None
if token is not Non... | 290 | 1 |
"""simple docstring"""
lowercase__ = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609_344,
"knot": 1.852,
}
lowercase__ = {
"km/h": 1.0,
"m/s": 0.277_777_778,
"mph": 0.621_371_192,
"knot": 0.539_956_803,
}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNA... | 290 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All prime... | 290 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokeniza... | 290 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowercase__ = _LazyModule... | 290 | 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():
raise OptionalDepende... | 290 | """simple docstring"""
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.... | 290 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
... | 290 | """simple docstring"""
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolv... | 290 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pi... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_dis... | 290 | 1 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: Optional[int] = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCRE... | 290 | 1 |
"""simple docstring"""
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.... | 290 | """simple docstring"""
class __snake_case :
def __init__( self , lowercase , lowercase=None , lowercase=None) -> List[str]:
'''simple docstring'''
a__: Dict = data
a__: List[Any] = previous
a__: Any ... | 290 | 1 |
"""simple docstring"""
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def ... | 290 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import fr... | 290 | 1 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixi... | 290 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ ... | 290 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ( __lowerCAmelCa... | 290 | """simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_avai... | 290 | 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 import (
DiffusionPipe... | 290 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowercase__ = get_logger(__name__)
lowercase__ = r'\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_siz... | 290 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'andreasmadsen/efficient_mlm_m0.4... | 290 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_util... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/mai... | 290 | 1 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __snake_case ( __lowerCAmelCase... | 290 | """simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixi... | 290 | 1 |
"""simple docstring"""
print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))'))
| 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: int = limit + 1
a__: Optional[int] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SN... | 290 | 1 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ = 'src/transformers'
lowercase__ =... | 290 | """simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase__ = TypeVar('T')
lowercase__ = Union[List[T], Tuple[T, ...]]
lowercase__ = Union[T, List[T], Dict[str, T]]
lowercase__ = Union[str, bytes, os.PathLike]
| 290 | 1 |
"""simple docstring"""
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
... | 290 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
"""simple docstring"""
class __snake_case :
def __init__( self , lowercase) -> Any:
'''simple docstring'''
a__: str = arr.split(',')
def lowerCamelCase_ ( self) -> Tuple:
'''simple docstring'''
a__: Op... | 290 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import Seque... | 290 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'facebook/xmod-base': 'https://hu... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hoppe... | 290 | 1 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowercase__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n... | 290 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
while a != 0:
a__ , a__: List[str] = b % a, a
return b
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
if gcd(_SCREAMING_SNAKE_CASE , _SCREAM... | 290 | 1 |
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