code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def __UpperCAmelCase ( __A = 5_0_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
UpperCAmelCase__ = set()
UpperCAmelCase__ = int((limit - 2_4) ** (1 / 2) )
UpperCAmelCase__ = set(range(3 , prime_square_limit +... | 475 |
'''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 __UpperCAmelCase ( unittest.TestCase ):
'''... | 366 | 0 |
'''simple docstring'''
from __future__ import annotations
class lowercase :
def __init__( self : List[Any] , _lowercase : list[list[int]] ):
SCREAMING_SNAKE_CASE__ : List[str] = TypeError(
'''Matrices must be formed from a list of ze... | 701 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_avail... | 250 | 0 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
__a = [int(SCREAMING_SNAKE_CASE__ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(SCREAMING_SNAKE_CASE__ ) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE__ ) <= 25... | 225 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 289 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_f... | 704 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED... | 397 | 0 |
from math import sqrt
def UpperCamelCase_ ( __a = 1_000_000 ) -> int:
a__ : int = 0
a__ : int = 0
a__ : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
... | 37 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 0 |
"""simple docstring"""
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class __lowerCAmelCase ( _lowercase , unittest.TestCase ):
"""simple docstring"""
... | 482 |
"""simple docstring"""
def A_ (__a , __a , __a ):
'''simple docstring'''
A_ = len(__a )
A_ = [[0] * n for i in range(__a )]
for i in range(__a ):
A_ = y_points[i]
for i in range(2 , __a ):
for j... | 482 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_back... | 274 | '''simple docstring'''
from __future__ import annotations
from typing import Any
def snake_case_ ( __snake_case : list[Any]) -> None:
create_state_space_tree(__snake_case , [] , 0)
def snake_case_ ( __snake_case : list[Any] , __snake_case : list... | 274 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise Optional... | 704 | '''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE_ = numpy.array([0, 0])
SCREAMING_SNAKE_CASE_ = numpy.array([0.5, 0.866_0254])
SCREAMING_SNAKE_CASE_ = numpy.array([1, 0]... | 466 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, Flax... | 17 |
"""simple docstring"""
import argparse
import json
import subprocess
def UpperCamelCase ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Dict ... | 238 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
... | 704 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE_... | 114 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] ) -> str:
if not (isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(__UpperCAmelCase , __UpperCAmelCase )):
... | 13 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase ) -> str:
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 wo... | 299 | 0 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 90 |
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,
rescale,
resize,
to_channel_di... | 90 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> ... | 193 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __snake_case ( SCREAMING_SNAKE_CASE ):
def SCREAMING_SNAKE_CASE_ ( self ,a_=None ,a_=None ,a_=None ,**a_ ):
"""simple docstring"""
if tokenize_kwargs is None:
lowerCAmelCase_... | 193 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
snake_case_ : int = "docs/source/en/_toctree.yml"
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> str:
UpperCAmelCase_ : List[Any] = ... | 705 |
'''simple docstring'''
class __a :
def __init__( self : List[Any] , __magic_name__ : int ) -> None:
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = size
UpperCAmelCase_... | 644 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__SCREAMING_SNAKE_CASE : List[str] =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[str] ={
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
... | 135 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipel... | 135 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a__ = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
... | 706 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
a__ = logging.getLogger()
def _UpperCAmelCase ( ):
... | 99 | 0 |
from __future__ import annotations
from PIL import Image
# Define glider example
_lowerCAmelCase : int = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],... | 246 |
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 from_byt... | 246 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A =(3, 9, -11, 0, 7, 5, 1, -1)
A =(4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _a :
__a : int
_... | 711 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 358 | 0 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=lowerCAmelCase ):
__lowerCamelCase : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
requires... | 345 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case_ ( lowerCAmelCase , unittest.TestCase ):
__lowerCamelCase : Any ... | 345 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ( lower... | 620 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __snake_case :
def __init__( self , _A , _A , _A , _A , _A , _A=0.2 , _A=0.2):
SCREAMING_SNAKE_CASE_ = bp_numa
SCREAMING_SNAKE_CASE_ = bp_numa
... | 620 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class snake_case ( UpperCamelCase_ ):
def __init__( self : Optional[Any] , a_ : List[str] , a_ : List[str] )-> List[Any]:
"""simple docstring"""
SCREA... | 85 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def __lowercase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : str , ... | 335 | 0 |
'''simple docstring'''
from __future__ import annotations
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ) -> Dict:
lowerCAmelCase__ : Any = TypeError(
"""Matrices must be formed from a list of zero or mo... | 718 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCAmelCase = datasets.logging.get_logger(__name__)
_lowerCAmelCase = '''\
@InProceedings{moosavi2019m... | 160 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,... | 213 | """simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> str:
return "".join(sorted(__UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE ... | 159 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import ... | 192 | """simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : str ) -> list[int]:
'''simple docstring'''
__snake_case : Union[str, Any] = int(UpperCAmelCase_ )
# Initialize R... | 192 | 1 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 7 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _a :
'''simple docstring'''
def __init__( self ,__a ,__a ,__a ) -> Tuple:
if dst_width < 0 or... | 116 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class a_ ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Optional[int] ,snake_case : Union[str, Any] ,snake_case : Any ,snake_case : Any ,snake_case : Li... | 252 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = 'encoder-decoder'
__UpperCAmelCase = True
... | 252 | 1 |
'''simple docstring'''
_lowercase = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowercase ... | 5 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
... | 329 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_config... | 682 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A = logging.getLogger(__name__)
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self ) ->... | 682 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_nump... | 371 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
A : List[str] = logging.get_logger... | 371 | 1 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_=0.2 , snake_case_=0.2... | 87 |
"""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 impor... | 87 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase : Any = logging.get_logger("""transformers.models.speecht5""")
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE... | 336 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avai... | 195 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase : List[Any] = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:... | 715 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCAmelCase : Any = 1.054571817E-34 # unit of ℏ : J * s
lowerCAmelCase : List[str] = 3E8 # uni... | 425 | 0 |
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> list:
'''simple docstring'''
__lowercase = len(_UpperCAmelCase )
__lowercase = [[0] * n for i in range(_UpperCAmelCase )]
for i in range(_UpperCAmelCase ):
__lowercase = y_point... | 321 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case ( __snake_case ):
"""simple doc... | 321 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDe... | 109 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 109 | 1 |
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_... | 121 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMi... | 502 | 0 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> int:
if not numbers:
return 0
if not isinstance(SCREAMING_SNAKE_CASE , (list, tuple) ) or not all(
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for number in numbers ):
raise ValueError('number... | 700 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__: Dict = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if no... | 311 | 0 |
'''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/licen... | 90 | """simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = False ) -> list[float]:
'''simple doc... | 530 | 0 |
"""simple docstring"""
from torch import nn
def __magic_name__ ( _lowerCamelCase : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
... | 63 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __magic_name__ ( ):
__a : Dict = {
"""repo_name""": ["""test_repo1""", """test_rep... | 63 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig''... | 4 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-k... | 4 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, B... | 334 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 334 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
A__ : Union[str, Any] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.linear_1.w... | 233 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' ,['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' ,['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.pa... | 233 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __snake_case ( lowerCamelCase__ ):
'''simple docstring'''
def UpperCAmelCase__ ( self : List[str] , A : Dict ):
... | 702 |
def A__ ( SCREAMING_SNAKE_CASE__ = 1000) -> int:
__snake_case , __snake_case: Dict = 1, 1
__snake_case: int = 2
while True:
__snake_case: str = 0
__snake_case: Any = fa + fa
__snake_case , __snake_case: Tuple ... | 155 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_pro... | 469 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 469 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( _UpperCAmelCase ):
"""simple docstring"""
lowercase__ : Union[str, Any] = (E... | 715 |
UpperCamelCase__ = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
... | 634 | 0 |
'''simple docstring'''
from typing import Any
class lowerCAmelCase_ :
def __init__( self , _UpperCamelCase )-> Union[str, Any]:
_A = data
_A = None
def __repr__( self )-> str:
... | 292 |
'''simple docstring'''
def lowerCamelCase_ ( __UpperCamelCase : list , __UpperCamelCase : int , __UpperCamelCase : int = 0 , __UpperCamelCase : int = 0 ) -> int:
"""simple docstring"""
_A = right or le... | 292 | 1 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def UpperCamelCase_( __magic_name__ : str ):
"""simple docstring"""
return getitem, k
def UpperCamelCase_( __magic_na... | 382 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTCo... | 382 | 1 |
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
_lowercase = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.Image.Re... | 91 |
from __future__ import annotations
import numpy as np
def __a ( __lowerCAmelCase ) -> Optional[Any]:
return np.maximum(0 , __lowerCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 352 | 0 |
import re
import string
import numpy as np
import datasets
A : List[Any] = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
A : int = "\nArgs:\n predictions: List... | 718 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : str = {
"configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 356 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 469 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : str ) -> List[str]:
"""si... | 469 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def lowerC... | 709 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
def wrapper(*__snake_case ,**__snake_case ):
lo... | 29 | 0 |
import string
def SCREAMING_SNAKE_CASE_ ( _snake_case :str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
_A = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
_A = string.ascii_uppercase.find(_... | 2 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE ... | 579 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _lowerCAmelCase ( __magic_name__ :str , __magic_name__ :str , __magic_name__ :Optional[str] = None ):
if version.parse(hfh.__version__ ... | 407 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _lowerCAmelCase ( __magic_name__ :list , __magic_name__ :list , __magic_name__ :list , ... | 407 | 1 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
fr... | 50 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Tuple = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
... | 50 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
... | 716 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_availab... | 351 | 0 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE__ : int = 4_000_000 ) -> int:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = [0, 1]
_UpperCAmelCase : Union[str, Any] = 0
while fib[i] <= ... | 289 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class Upp... | 289 | 1 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable... | 439 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class a__ ( UpperCamelCase_ ):
snake_case__ = '''bert-generation'''
def __init__( self : Dict ,a__ : str=5_0358 ,a__ : List[str]=1024 ,a__ : int=24 ... | 439 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Tuple = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '... | 493 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( A__ , unittest.TestCase ):
"""simple docstring"""
a = TransfoXLTo... | 493 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiua... | 328 |
from __future__ import annotations
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> set[str]:
"""simple docstring"""
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = set(_SCREAMING_SNAKE_CASE ), [start]
... | 328 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[Any] = logging.get_logger(__name__)
_snake_case : str = {
'xlm-mlm-e... | 53 |
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 a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : Optio... | 53 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : int ):
'''simple docstring'''
while second != 0:
lowerCamelCase_ = first & second
first ^= second
lowerCamelCase_ = c << 1
return first
if ... | 720 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _SCREAMING_SNAKE_CASE ... | 651 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
... | 114 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .atte... | 389 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_senten... | 389 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
A_ : str = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wa... | 265 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( __A : list[int] ) -> list[int]:
'''simple docstring'''
if len(__A ) == 0:
return array
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = ... | 265 | 1 |
from __future__ import annotations
def lowerCAmelCase ( UpperCamelCase__ : tuple[int, int] , UpperCamelCase__ : int ) -> list[tuple[int, int]]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE: int = pos... | 146 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
fro... | 146 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'... | 24 |
"""simple docstring"""
import argparse
import collections
import os
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_table.py
__SCREAMING_SNAKE_CASE = """... | 388 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 662 |
from __future__ import annotations
import time
import numpy as np
__lowerCAmelCase : List[str] = [8, 5, 9, 7]
__lowerCAmelCase : str = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__lowerCAmelCase : Optional[Any] = [
[3, 2, 1, 4]... | 662 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCAmelCase_ ( lowerCamelCase ):
return (dat... | 21 |
import heapq
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with... | 21 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def SCREAMING_SNAKE_CASE__ ... | 713 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Sear... | 534 | 0 |
def a ( A__ : int = 10 , A__ : int = 1000 , A__ : bool = True ) -> int:
"""simple docstring"""
assert (
isinstance(A__ , A__ )
and isinstance(A__ , A__ )
and isinstance(A__ , A__ )
), "Invalid type of value(s) sp... | 291 |
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
fro... | 291 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_m... | 297 |
def _lowerCamelCase ( _a ):
"""simple docstring"""
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
_lowerCamelCase = gray_code_sequence_string(_a )
#
# convert them to integers
for i in range(len(_a ... | 297 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__snake_case = logging.get_logger(__name__)
... | 1 | """simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transforme... | 359 | 0 |
from __future__ import annotations
import time
a_ : Tuple = list[tuple[int, int]]
a_ : Optional[int] = [
[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,... | 444 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a_ : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_ : list[int] = [ord(letter) for letter in string.ascii_lowe... | 444 | 1 |
'''simple docstring'''
import os
def _UpperCamelCase ()-> Union[str, Any]:
'''simple docstring'''
__snake_case = os.path.dirname(os.path.realpath(_lowerCamelCase ) )
__snake_case = os.path.join(_lowerCamelCase , '''triangle.txt''' ... | 24 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : int) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0) != 0)
def SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstrin... | 125 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 |
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ... | 32 | 1 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
cla... | 96 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
war... | 96 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageRes... | 584 | import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelera... | 584 | 1 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase = " " ) ->list:
"""simple docstring"""
lowercase : List[str] = []
lowercase : Union[str, Any] = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
... | 319 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'''vocab_file''': '''vocab.txt'''}
__a = {
'''vocab_file''': ... | 319 | 1 |
"""simple docstring"""
import sys
a :Dict = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856... | 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> ... | 12 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = [
"encoder... | 44 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCamelCase__ : Union[str, Any] = {
"... | 12 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase_ ( __UpperCAmelCase ) -> List[str]:
return x + 2
class _lowerCamelCase ( unittest.TestCase ):
def _lowe... | 715 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate... | 507 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import Stabl... | 202 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase : ... | 202 | 1 |
import os
UpperCAmelCase_ = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
def __magic_name__ ( lowercase ) -> int:
"""simple docstring"""
lowercase_ : Optional[Any] ... | 436 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundf... | 436 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: float , _lowerCamelCase: int ):
if digit_amount > 0:
return round(number - int(_lowerCamelCase ) , _lowerCamelCase )
return number - int(_lowerCamelCase )
if __name__ == "__main__":
print(decimal_isolate(1.5... | 578 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
UpperCamelCase_... | 578 | 1 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils... | 706 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ : Optional[Any] = {
"configuration_funnel": ["FUNNEL_PRETRAINED... | 18 | 0 |
import os
from collections.abc import Iterator
def UpperCAmelCase_ ( __UpperCAmelCase : str = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(__UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ = [d for d in dir_names if d != 'scripts' and d[0] not ... | 31 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _lowerCAmelCase : int ):
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE_CASE_ ... | 31 | 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 _lowercase ( lowerCAmel... | 497 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
ren... | 497 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate... | 394 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : int = {
'vocab_file': '... | 394 | 1 |
"""simple docstring"""
from collections.abc import Generator
from math import sin
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->bytes:
if len(SCREAMING_SNAKE_CASE_ ) != 32:
raise ValueError('''Input must be of length 32''' )
_lowerCamelCase : Dict = B''''''
... | 707 | """simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _UpperCAmelC... | 558 | 0 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
lowerCAmelCase_ = False
try:
lowerCAmelCase_ ... | 39 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_ut... | 310 | 0 |
from statistics import mean
import numpy as np
def __UpperCAmelCase ( a_ , a_ , a_ , a_):
snake_case_ = 0
# Number of processes finished
snake_case_ = 0
# Displays the finished process.
# If it is 0, the performance is co... | 607 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_b... | 607 | 1 |
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 require_vision
from transformers.ut... | 559 | import math
import sys
def lowerCAmelCase( __lowerCamelCase ):
if number != int(__lowerCamelCase ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the value of input must not be a negative number' )
if number == 0:... | 559 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _A ( UpperCAmelCase_ , UpperCAmelCase_ ):
@register_to_config
def __init__( self : str , *,
lowerCamelCase__ : int = 4 ... | 515 |
import fire
from utils import calculate_rouge, save_json
def __lowerCamelCase ( __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any]=None , **__lowerCAmelCase : Union[str, Any] ) ... | 515 | 1 |
'''simple docstring'''
import random
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = num - 1
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
while s % 2 == 0:
SCREAMING_SNAKE_CASE : List[str] = s // 2
t += 1
for _ in range(5 ):
... | 379 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class... | 379 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
def ... | 206 |
_SCREAMING_SNAKE_CASE : dict[tuple[int, int, int], int] = {}
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
... | 206 | 1 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvisio... | 7 |
import requests
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = {'Content-Type': 'application/json'}
UpperCamelCase_ = requests.post(__lowercase , json={'text': message_body} , headers=__lowercase)
if response.status_code != 20... | 23 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperC... | 552 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
... | 552 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 72 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface... | 72 | 1 |
import math
def A__ ( SCREAMING_SNAKE_CASE__ = 100) -> int:
__snake_case: Tuple = sum(i * i for i in range(1 , n + 1))
__snake_case: List[Any] = int(math.pow(sum(range(1 , n + 1)) , 2))
return square_of_sum - sum_of_squares
i... | 155 |
def A__ ( SCREAMING_SNAKE_CASE__ = 100) -> int:
__snake_case: str = 0
__snake_case: int = 0
for i in range(1 , n + 1):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(f'{so... | 155 | 1 |
"""simple docstring"""
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_SCREAMING_SNAKE_CASE = abspath(join(dirnam... | 163 |
"""simple docstring"""
import argparse
import json
import subprocess
def __a ( a, a ):
"""simple docstring"""
_a = []
_a = (
F'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://... | 388 | 0 |
from __future__ import annotations
from random import choice
def a_ ( _A ) -> List[Any]:
"""simple docstring"""
return choice(_A )
def a_ ( _A , _A ) -> int:
"""simple docstring"""
snake_case__ = random_... | 372 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 372 | 1 |
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