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 |
|---|---|---|---|---|
from maths.prime_check import is_prime
def _lowerCAmelCase ( _lowerCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
__snake_case = F'''Input value of [number={number}] must be an int... | 371 |
'''simple docstring'''
from torch import nn
def __lowercase (_lowercase ) -> Union[str, Any]:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
el... | 150 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
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 TokenizerT... | 718 | '''simple docstring'''
from random import randint, random
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = False , UpperCAmelCase = False , UpperCAmelCase = 5 , ):
lowercase__ : Optional[Any] = [[-1] * number_of_cells] # Create a highway w... | 428 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase = 100 ,) -> Optional[Any]:
snake_case : int = x_start
snake_case : List[Any] = fnc(lowerCAmelC... | 587 |
# 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
#
# Unless required by app... | 53 | 0 |
'''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 FlaxStableDiffusionCon... | 411 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow ... | 411 | 1 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCamelCase : Optional[Any] = '<<<<<<< This should probably be modified because it m... | 50 | """simple docstring"""
def _lowerCamelCase( a ):
return " ".join(
"".join(word[::-1] ) if len(a ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 528 | 0 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str = "laptop" ) -> DataFrame:
"""simple docstring"""
A__ : List[Any] = F"https://www.amazon.in/l... | 712 |
import numpy as np
_SCREAMING_SNAKE_CASE : Any = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self... | 55 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowerCAmelCase__ ( unittest.TestCase ):
... | 265 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Optional[int] = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_A... | 265 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase : Any = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 542 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ ... | 542 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 563 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArgument... | 563 | 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, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():... | 704 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_u... | 524 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCamelCase ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase : List[... | 471 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResa... | 471 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,... | 705 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision,... | 92 | 0 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
_a... | 22 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
def __lowerCAmelCase ( self :Union[str, Any] ) -> str:
'''simple doc... | 698 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tran... | 702 |
"""simple docstring"""
def a__ ( lowerCAmelCase : list , lowerCAmelCase : list ):
'''simple docstring'''
_validate_point(lowerCAmelCase )
_validate_point(lowerCAmelCase )
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("Both ... | 660 | 0 |
'''simple docstring'''
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
__magic_name__ : str =logging.get_logger(__name__)
__magic_name__ : List[Any] ... | 664 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except Op... | 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 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBer... | 78 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase_ ( UpperCAme... | 583 | 0 |
"""simple docstring"""
import argparse
import datetime
def snake_case_ ( A_ : str ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
... | 598 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorStat... | 598 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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... | 595 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils im... | 426 | 0 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class snake_case__ (datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : ... | 662 |
import argparse
import os
import re
__lowerCAmelCase : Union[str, Any] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase : Dict = re.compile(r'[A-Z_]+_MAPPING(\... | 662 | 1 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"facebook/encodec_24khz": "https://huggingface.co/facebo... | 517 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( a__) -> float:
"""simple docstring"""
if not nums:
raise ValueError('List is empty')
return sum(a__) / len(a__)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 517 | 1 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download i... | 703 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 10 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , Up... | 87 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
snake_case_ ... | 592 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
... | 13 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a = list[list[float | int]]
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Matrix:
'''simple docstring'''
__SCREAMING_SNAKE_CASE ... | 13 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__magic_name__ = datasets.logging.get_logger(__name__)
__magic_name__ = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sella... | 276 |
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
if any(not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__lowerCAmelCase ) ):
for i, (rod_u... | 276 | 1 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def lowerCamelCase ( UpperCamelCase : jnp.ndarray , UpperCamelCase : int , UpperCamelCase : float = 1 , UpperCamelCase : float = 1 , UpperCamelCase : ... | 700 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 234 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
__lowerCamelCase = TypeVar('''_T''')
class A__ ( Generic[_T] ):
def __init__( self , UpperCamelCase__ = None ) -> None:
'''simple docstring... | 288 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A__ ( _snake_case ):
lo... | 288 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> float:
"""simple docstring"""
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(lowercase_ ) * abs(lowercase_ )
if __name__ == "__main__":
impo... | 177 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> float:
"""simple docstring"""
def get_matched_characters(lowercase_ , lowercase_ ) -> str:
A__ = []
A__ = min(len(_stra ) , len(_stra ) ) // 2
... | 177 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configura... | 502 |
'''simple docstring'''
from __future__ import annotations
SCREAMING_SNAKE_CASE__ : List[Any] = 1.6021e-19 # units = C
def a ( UpperCamelCase_ : float , UpperCamelCase_ : float , UpperCamelCase_ : float , ) -> tuple[str, float]:
if (con... | 538 | 0 |
def __magic_name__ ( __a : int , __a : int ):
'''simple docstring'''
return abs(__a ) if a == 0 else greatest_common_divisor(b % a , __a )
def __magic_name__ ( __a : int , __a : int ):
'''simple docstring'''
while y: # --> when y=0 then loop wi... | 86 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 86 | 1 |
"""simple docstring"""
from __future__ import annotations
a = """#"""
class lowercase_ :
'''simple docstring'''
def __init__( self : Tuple ):
_A = {}
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : str ):
... | 7 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUID... | 548 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipeline... | 614 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , ) -> tuple[float | int, list[tuple[int, int]]]:
"""simp... | 614 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base... | 329 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( __A : Union[str, Any] )... | 329 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalD... | 702 |
'''simple docstring'''
import numpy as np
def __A ( a_ : np.array ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 551 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from trans... | 142 |
"""simple docstring"""
import os
def lowerCamelCase_ ( ):
lowerCamelCase_ = os.path.dirname(os.path.realpath(_lowerCamelCase ) )
lowerCamelCase_ = os.path.join(_lowerCamelCase , '''triangle.txt''' )
with open(_lowerCamelCase ) as f:
... | 142 | 1 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_ava... | 160 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ... | 160 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDoc... | 259 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e im... | 259 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils imp... | 708 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Tuple:
# load bas... | 471 | 0 |
'''simple docstring'''
class lowercase_ :
"""simple docstring"""
def __init__( self : Tuple, UpperCamelCase__ : int ) -> None:
_A = size
_A = [0] * size
_A = [0] * size
@staticmethod
def __UpperCAmelCase ( UpperC... | 107 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, ... | 107 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : int = logging.get_logger(__name__)
__a : Tuple = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert... | 702 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__a : Dict = logging.get_logger(__name__)
class UpperCAmelCase( snake_case_ ):
"""simple docstring"""
def __init__( self , *lowerCamelCase , ... | 298 | 0 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError("""days_between_payme... | 15 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl... | 237 | 0 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=__lowercase ):
A_ = ['note_seq']
def __init__( self : Dict , *_snake_case : Dict , **_snake_case : List[Any] )->List[str]:
'''simple docstring'... | 240 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :int ) -> bool:
if num < 0:
return False
__lowerCAmelCase : int = num
__lowerCAmelCase : int = 0
while num > 0:
__lowerCAmelCase : Any = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_n... | 240 | 1 |
"""simple docstring"""
import re
def a ( __snake_case : str ):
'''simple docstring'''
UpperCAmelCase_ :Optional[Any] = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__snake_case,... | 608 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class _snake_case :
'''simple docstring'''
def __init__( self : Dict , snake_case : int , snake_case : MutableSequence[float] ):
if len(snake_case ) !=... | 608 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list[int] ):
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
lowerCAmelCase : int = sum(_snake_case ) / len(_snake_case ) # Calculate the average
... | 637 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 637 | 1 |
"""simple docstring"""
from math import ceil
def UpperCAmelCase__ (snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] ):
"""simple docstring"""
_snake_case : Union[str, Any] = list(range(0 , snake_case__ ) )
_snak... | 609 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''xlm-roberta-base... | 609 | 1 |
def _snake_case ( __snake_case = 10 ):
if not isinstance(__snake_case , __snake_case ) or n < 0:
raise ValueError('''Invalid input''' )
_UpperCamelCase = 10**n
_UpperCamelCase = 28433 * (pow(2 , 7830457 , __snake_case )) + 1
return str(number % m... | 711 | def _snake_case ( __snake_case ):
if not isinstance(__snake_case , __snake_case ):
raise TypeError('''Input value must be an \'int\' type''' )
_UpperCamelCase = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main_... | 71 | 0 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing... | 664 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : int =logging.get_logger(__name__)
__magic_name__ : List[Any] ={}
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : in... | 664 | 1 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __magic_name__ ( __UpperCAmelCase ):
__A : List[Any] = ""
__A : str = (
... | 475 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
fro... | 475 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CON... | 591 |
'''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
#
# U... | 591 | 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,
... | 720 |
"""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 BasicTra... | 16 | 0 |
'''simple docstring'''
def A ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[int] ) -> int:
'''simple docstring'''
lowerCAmelCase__ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
... | 48 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
"""simple docstring"""
if len(lowercase_ ) <= 1:
return [tuple(lowercase_ )]
A__ = []
def generate(lowercase_ , lowercase_ ):
if k == 1:
res.append... | 87 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a__: List[str] = {
'configuration_layoutlmv3': [
'LAYOU... | 212 |
from __future__ import annotations
import math
from collections.abc import Callable
def UpperCamelCase__( UpperCamelCase__ : Callable[[int | float], int | float] , UpperCamelCase__ : int | float , UpperCamelCase__ : int | float , UpperCamelCase__ : int = 1_00 , )->flo... | 212 | 1 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils i... | 118 |
from manim import *
class _A ( _lowerCamelCase ):
def __a ( self : Dict ) -> Any:
"""simple docstring"""
lowercase : Tuple = Rectangle(height=0.5 , width=0.5 )
lowercase : str = ... | 217 | 0 |
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 im... | 712 |
import argparse
import struct
import unittest
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : Any , a_ : bytes ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = data
# Initializ... | 241 | 0 |
UpperCamelCase = range(2, 20 + 1)
UpperCamelCase = [10**k for k in range(ks[-1] + 1)]
UpperCamelCase = {}
def _A ( lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[Any] ):
... | 61 |
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case__ : Optional[int] = '''src/transformers'''
# Matches is_xxx_available()
snake_case__ : Union[str, Any] = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a o... | 392 | 0 |
def lowerCamelCase__ ( UpperCamelCase__ : Union[str, Any] ) -> str:
'''simple docstring'''
_snake_case = len(SCREAMING_SNAKE_CASE__ )
while cur > 1:
# Find the maximum number in arr
_snake_case = arr.index(max(arr[0:... | 704 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import W... | 541 | 0 |
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"):
a_ :Optional[int] = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic"... | 478 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and ... | 478 | 1 |
import math
import unittest
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
assert isinstance(_A , _A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0... | 705 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
# `task` is not a ClassVar ... | 152 | 0 |
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)
__A : List[Any... | 27 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ = (7_20, 12_80) # Height, Width
A_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ =... | 609 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCAmelCase_ : List[str] = TypeVar('T')
UpperCAmelCase_ : int = TypeVar('U')
class SCREAMING_SNAKE_CASE__ ( Generic[T, U] ):
def __init__... | 443 |
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
UpperCAmelCase_ : int = datasets.logging.get_logger(__name__)
UpperCAmelCase_ : Dict = '\\n@InProceedi... | 443 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
... | 270 |
'''simple docstring'''
def UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ) -> int:
if n == 1 or not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return 0
elif n == 2:
return 1
else:
snake_case__ : int = ... | 270 | 1 |
'''simple docstring'''
import operator as op
def __UpperCamelCase ( _lowercase ) -> Optional[int]:
_lowercase : Optional[Any] = []
_lowercase : Any = lambda _lowercase, _lowercase : int(x / y ) # noqa: E731 integer division operation
_low... | 4 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __UpperCamelCase ( _lowercase ) -> None:
_lowercase , _lowercase : List[Any] = analyze_text(_lowercase )
_lowercase ... | 4 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCamelCase : Optional[Any] = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''... | 184 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return []
snake_case_ , snake_case_ : Dict = min(_UpperCamelCase ), max(_UpperCamelCase ... | 60 | 0 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class lowerCamelCase__ ( ... | 225 | import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | 225 | 1 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transforme... | 450 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__UpperCamelCase : List[Any] = loggin... | 450 | 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.utils import I... | 707 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 71 | 0 |
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 lowerCAmelCase_ ( __snake_case , unittest.TestCase ):
_U... | 66 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ :Tuple = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiT... | 618 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __magic_name__ ( unittest.TestCase):
... | 89 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __magic_name__ ( __UpperCAmelCase):
'''simple docs... | 89 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a :List[Any] = ""
a :Union[str, Any] = ""
a :List[str] = ""
a :str = 1 # (0 is vertical, 1 is horizontal)
def _lowercase ( ) -> ... | 680 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
a :Optional[Any] = "<<<<<<< This should probably be modified because it mentions: "
a :Tupl... | 680 | 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,
)
SCREAMING_SNAKE_CASE__ = {"configu... | 393 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError("""Input value must be an 'int' type""" )
lowerCAmelCa... | 393 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMasked... | 529 |
from __future__ import annotations
def a__ ( A_, A_ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((__magic_name__) , (__magic_name__)) = extended_euclid(A_, a % b )
__magic_name__ = a // b
return (y, x - k * y)
def a__ ( ... | 529 | 1 |
def UpperCAmelCase ( lowercase__ : int ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
a__ = 0
while number:
# This way we arrive at next set ... | 718 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 412 | 0 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase : Dict =lo... | 228 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartT... | 228 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}
class __magic_name__... | 264 | from __future__ import annotations
UpperCAmelCase_ = 1.6_0_2_1E-1_9 # units = C
def lowerCAmelCase_ ( lowercase: float , lowercase: float , lowercase: float , ) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobi... | 264 | 1 |
'''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,
)
... | 275 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__A : int = pytest.mark.integration
... | 275 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ ={
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 702 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
UpperCAmelCase__ : List[Any] =list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
UpperCAmelCase__ : Dict ... | 269 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTok... | 566 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case : Tuple = logging.get_logger(__... | 566 | 1 |
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
__SCREAMING_SNAKE_CASE = datasets.logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = '\\n@InProceedings{moosavi2019minimum,\n author ... | 153 |
from typing import Dict, Iterable, 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_forma... | 153 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__( metaclass=__lowercase ):
'''simple docstring'''
__snake_case = ['torch']
def __init__( self , *__lowerCamelCase , **__lowerCamelCase ) -> Optional[Any]:... | 249 |
from __future__ import annotations
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase = None, __lowerCamelCase = None, __lowerCamelCase = False, ):
_SCREAMING_SNAKE_CASE : Dict = cipher_alphabet or [chr(__lowerCamelCase ) for i in range... | 249 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
... | 711 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UN... | 92 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
imp... | 34 |
from math import factorial
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> float:
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes... | 397 | 0 |
'''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
if is_torch_available... | 343 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
A : Dict = []
def generate(snake_case__ , snake_case__ ):
if k == 1:
... | 343 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration... | 309 | '''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_... | 309 | 1 |
'''simple docstring'''
def _a ( ):
snake_case : List[str] =[]
snake_case : int =1
while len(lowerCamelCase_ ) < 1e6:
constant.append(str(lowerCamelCase_ ) )
i += 1
snake_case : Tuple =''''''.join(lowerCamel... | 136 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class lowerCAmelCase_ :
def __init__( self : List[str], _snake_case : int ):
'''simple docstring'''
snake_case : Optional[... | 136 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from... | 1 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffuse... | 350 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAIN... | 712 |
from __future__ import annotations
import time
import numpy as np
lowerCamelCase_ : Any = [8, 5, 9, 7]
lowerCamelCase_ : int = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCamelCase_ : int = ... | 345 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class a_ :
def __init__( self : Dict , lowercase : int , lowercase : int , lowercase : float = 0 ):
"""simple docstring"""
... | 172 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCAmelCase_ ( __lowerCamelCase : NDArray[floataa] ,__lowerCamelCase : NDArray[floataa] ,__lowerCamelCase : list[int] ,... | 172 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( _lowerCamelCase = 4 ) ->list[list[int]]:
_UpperCAmelCase =abs(_lowerCamelCase ) or 4
return [[1 + x + y * row_size for x in range(_lowerCamelCase )] for y in range(_lowerCamelCase )]
def lowerCamelCase__ ( _lower... | 592 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _a ( A__ ):
... | 592 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.j... | 301 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversation... | 327 | 0 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def snake_case_ (_a : jnp.ndarray , _a : int , _a : float = 1 , _a : float = 1 , _a : float = 1.0E4 , _a : bool = False ... | 358 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A ={
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerConfig',
'TableTra... | 358 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
A__ : Union[str, Any] = "ClapFeatureExtractor"
... | 104 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Ver... | 215 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
A : Union[str,... | 702 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
req... | 163 | 0 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase ) -> int:
"""simple docstring"""
create_state_space_tree(_lowerCAmelCase , [] , 0 , [0 for i in range(len(_lowerCAmelCase ) )] )
def __UpperCamelCase ( _lowerCAmelC... | 662 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : str , UpperCamelCase : int ):
UpperCAmelCase : List[Any] = word.split()
def justify(UpperCamelCase : list , UpperCamelCase : int , UpperCamelCase : int ) -> str:
UpperCAmelCa... | 160 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 700 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig,... | 239 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Any = ... | 44 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class A ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase : Any = """"""
lowerCamelCase : s... | 226 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( A__ ):
"""simple docstring"""
_lowercase = (UnCLIPScheduler,)
def _UpperCamelCase( self : Any , **lowerCamelCase__ : Tuple ):
a... | 713 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoCo... | 151 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamel... | 698 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
def __lowerCAmelCase ( self :Union[str, Any] ) -> str:
'''simple doc... | 698 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 152 |
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_dim... | 152 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase : Optional[int] = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'... | 511 |
def __lowerCamelCase ( __a :int ) -> Dict:
"""simple docstring"""
A__ = len(__a )
A__ = sum(__a )
A__ = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
A__ ... | 176 | 0 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = list[list[int]]
# assigning initial values to the grid
lowerCAmelCase_ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3,... | 635 | """simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.js... | 635 | 1 |
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