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 ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_to... | 631 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _UpperCAmelCase ( unittest.TestCase ):
def _snake_case ( self : Union[str, Any]):
SCREAMING... | 631 | 1 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
... | 718 |
def _a ( __lowercase , __lowercase = 0 ) -> list:
"""simple docstring"""
__UpperCamelCase = length or len(__lowercase )
__UpperCamelCase = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 567 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
... | 259 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_alig... | 259 | 1 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowercase_ ( _UpperCAmelCase , _Upper... | 361 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class lowercase :
def __init__( self : Dict ):
"""simple docstring"""
A_ : Tuple = {}
... | 361 | 1 |
from manim import *
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase_ = Rectangle(height=0.2_5 , width=0.2_5 )
... | 463 |
from math import isqrt
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCamelCase__ , lower... | 463 | 1 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase = HUGGINGFACE_HUB_CACHE
lowerCamelCase = "config.json"
lowerCamelCase = "diffusion_pytorch_model.bin"
lowerCamelCase = "diffusion_flax_model.msgpack"
lowerCamelCase = "model... | 704 |
def SCREAMING_SNAKE_CASE( __UpperCamelCase ) -> float:
a__ : Optional[Any] = 0
while len(__UpperCamelCase ) > 1:
a__ : str = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
a__ : List[str] = file... | 207 | 0 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
... | 1 |
'''simple docstring'''
def __a ( A__ = 1000 ) -> int:
lowerCAmelCase = 3
lowerCAmelCase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__... | 649 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class a_ ( ... | 703 |
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():
import jax.numpy as jnp
from transf... | 252 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 109 |
"""simple docstring"""
def lowercase_ ( _lowercase : int ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError("only integers accepted as input" )
else:
UpperCAmelCase : Optional[int] = ... | 595 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __a ( lowerCAmelCase__ ):
def __init__( self ):
# test for the above condition
self.test()
def snake_case_ ( self ):
_lowerCamelC... | 710 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
A_ : Any =logging.get_logger(__name__)
A_ : Dict ={"""vocab_file""": """vocab... | 222 | 0 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class lowerCAmelCase ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def __A ( lowerCAmelCase__ ) -> Any:
SCREAMING_SNAKE_CASE ... | 247 |
"""simple docstring"""
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_ten... | 247 | 1 |
import math
def lowerCamelCase_ ( UpperCamelCase_ ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
_a : Dict = f"""Input value of [number={number}] must be an integer"""
raise TypeError(UpperCamelCase_ )
if number < 1:
... | 716 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__UpperCAmelCase : Any = 0B1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1... | 249 | 0 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.ge... | 207 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProces... | 207 | 1 |
'''simple docstring'''
import math
import qiskit
def __snake_case ( UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : int = 1 , UpperCAmelCase_ : int = 1 ):
if (
isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
or isinstance(UpperCAmelCase_ ... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ : List[Any] = ... | 445 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 78 | '''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_... | 78 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_lowerCamelCase : List[Any] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Sim... | 718 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simp... | 361 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avail... | 570 |
'''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,
)
_UpperCAmelCase : Optional[int] = pytest.mark.integration
@pytest.mark.para... | 683 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_visio... | 705 | '''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ... | 30 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase ) -> list[int]:
'''simple docstring'''
return [ord(__UpperCAmelCase ) - 96 for elem in plain]
def __magic_name__ ( __UpperCAmelCase ) -> st... | 640 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Any = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
sna... | 640 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_... | 709 |
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
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : List[... | 421 | 0 |
import functools
def __UpperCAmelCase ( lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] ) -> int:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not all(isinstance(a_ , a_ ) for day in days ):
raise ValueErr... | 105 |
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 GenerationTesterMixin
from ...test_configurat... | 318 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextE... | 236 | import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ):
"""simple docs... | 236 | 1 |
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase_ = len(bin(snake_case__ )[3:] )
UpperCAmelCase_ = bin(abs(snake_case__ ) - (1 << binary_number_length) )[3:]
UpperC... | 579 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
'''simple docstring'''
from __future__ import annotations
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Optional[int] = str(__snake_case )
return n == n[::-1]
def A_ ( snake_case = 1000000 ):
SCREAMING_SNAKE_CASE:str = 0
for i in range(1 , __snake_cas... | 712 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=_a ):
_A : Any = ['''torch''', '''torchsde''']
def __init__( self : Any ,*SCREAMING_SNAKE_CASE__ : int ,**SCREAMING_SNAKE_CASE__ ... | 465 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def __lowerCAmelCase ( __UpperCamelCase : Callable ):
'''simple docstring'''
@wraps(__UpperCamelCase )
def _inner_fn(*__UpperCamelCase : ... | 58 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
... | 554 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils ... | 113 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _UpperCamelCase ( UpperCamelCase__ ):
@wraps(UpperCamelCase__ )
def _inner_fn(*UpperCamelCase__ , **UpperCamelCase__ ):
warnings.warn(
(... | 113 | 1 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, ... | 209 | '''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTes... | 209 | 1 |
"""simple docstring"""
import os
def a_ ( ):
'''simple docstring'''
with open(os.path.dirname(_lowerCAmelCase ) + '/p022_names.txt' ) as file:
lowercase__ : Union[str, Any] = str(file.readlines()[0] )
lowercase__ : Tuple = ... | 645 | """simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmel... | 645 | 1 |
'''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_ : Optional[Any] = ... | 212 |
'''simple docstring'''
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 AutoProc... | 212 | 1 |
import numpy as np
import qiskit
def lowerCamelCase_ ( _lowercase = 8 , _lowercase = None ) -> int:
__A : Any = np.random.default_rng(seed=lowercase__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
... | 719 | import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils impo... | 387 | 0 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : Dict):
if any(not isinstance(_UpperCAmelCase, _UpperCAmelCase) or x < 0 for x in sequence):
raise TypeError('''Sequence must be list of non-negative integers''')
for _ in range(len(_UpperCAmelCase)):
for i, (r... | 212 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'huggingface/time-series-transformer-tourism-monthly': (
'htt... | 621 | 0 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common ... | 325 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokeni... | 325 | 1 |
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 Accelerator,... | 600 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__Upp... | 600 | 1 |
'''simple docstring'''
import cmath
import math
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
a__ : Union[str, Any] = math.radians(_lowerCamelCase )
a__ ... | 706 |
'''simple docstring'''
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] , A__ : list[int] ) -> None:
'''simple docstring'''
a__ : Union[str, Any] = len(A__ )
a__ : Tuple = [0] * len_array
... | 340 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = prime_factors(_lowercase )
if is_square... | 462 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase ) -> Opti... | 136 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.be... | 597 |
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 ):
... | 597 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers... | 526 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: List[Any] = {'configuration_reformer': ['REFORMER_PRETRAINED_CONF... | 526 | 1 |
import argparse
from collections import defaultdict
import yaml
__lowerCamelCase = 'docs/source/en/_toctree.yml'
def UpperCamelCase__ ( UpperCAmelCase ) -> List[str]:
"""simple docstring"""
_a : List[str] = defaultdict(UpperCAmelCase )
... | 307 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRC... | 307 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorf... | 58 |
def a ( A__ ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(A__ , A__ ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(A__... | 35 | 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... | 556 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 556 | 1 |
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)
SCREAMING_... | 79 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.t... | 276 | 0 |
from manim import *
class __A ( UpperCamelCase__ ):
def A__ ( self :List[str] ):
'''simple docstring'''
__magic_name__ : Union[str, Any] =Rectangle(height=0.5 , width=0.5 )
__magic_name__ : Dict =Rectangle(height=0.46... | 719 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 367 | 0 |
"""simple docstring"""
snake_case = 6_5_5_2_1
def snake_case ( lowerCAmelCase_ ) -> int:
_snake_case = 1
_snake_case = 0
for plain_chr in plain_text:
_snake_case = (a + ord(lowerCAmelCase_ )) % MOD_ADLER
_snake_case = ... | 103 |
def a__ ( snake_case__ : int , snake_case__ : int ):
return x if y == 0 else greatest_common_divisor(snake_case__ , x % y )
def a__ ( snake_case__ : int , snake_case__ : int ):
return (x * y) // greatest_common_divisor(sna... | 643 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Optional[int] = {
'''kssteven/ibe... | 114 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : str = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''tokenization_biogpt''': ['''... | 114 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE:
def __init__( self : str ) -> None:
SCREAMING_SNAKE_CASE__ :list[Any] = []
SCREAMING_SNAKE_CASE__ ... | 209 | '''simple docstring'''
from __future__ import annotations
UpperCamelCase_ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase ( UpperCAmelCase__ : list[list[int]] , UpperCAmelCase__ : list[int] , UpperCAmelCase__ ... | 209 | 1 |
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__":
_lowerCamelCase ="%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))
print("Googling.....")
_... | 252 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoa... | 252 | 1 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCamelCase : Union[str, Any] = '''src/transformers'''
# This is to make sure the transformer... | 216 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__lowerCamelCase : int = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except Opti... | 216 | 1 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, ... | 423 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowercase : int = logging.get_logger(__name__)
... | 423 | 1 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import d... | 4 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__a = logging.getLogger(__name__)
... | 374 | 0 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def snake_case ( a_ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
... | 717 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVe... | 543 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_outp... | 585 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _lowerCAmelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] =None
def __lowerCAmelCase ( self : Union[str, Any] ):
... | 282 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extra... | 710 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTeste... | 399 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
... | 253 | from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from... | 520 | 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():
imp... | 720 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A : str = TypeVar('T')
class lowerCamelCase ( Generic[T] ):
_SCREAMING_SNAKE_CASE = 42 # Cache store of keys
_SC... | 273 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__UpperCamelCase : Any = 'scheduler_config.json'
class _Uppe... | 519 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_tex... | 519 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...u... | 353 |
def A_ ( a , a ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 353 | 1 |
"""simple docstring"""
import numpy as np
from PIL import Image
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
UpperCAmelCase__ : Tuple = np.array(_snake_case )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matr... | 110 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = [
'encoder.version',
'decoder.version',
'model.enco... | 110 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils imp... | 476 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'''YituTech/conv-bert-base''': ''... | 476 | 1 |
'''simple docstring'''
lowerCAmelCase__ : List[Any] = 8.3144598
def _a ( __lowerCAmelCase : float , __lowerCAmelCase : float ):
"""simple docstring"""
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
... | 347 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowercase__ ( __snake_case : Optional[Any] ):
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase_ : Optional[int] = orig_key.replace('model.... | 406 | 0 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case__ :
'''simple docstring'''
__A = ... | 700 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _lowerCAmelCase ( __magic_name__ :Optional[int] , __magic_name__ :str , __magic_name__ :str , __magic... | 407 | 0 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
" Distillation"
)
)
pa... | 481 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_a = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, C... | 481 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
... | 407 |
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
from .tokenization_gpta import GPTaTokenizer
... | 407 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( A ,A ) -> int:
# Checks if the entire collection has been sorted
if len(A ) <= 1 or n <= 1:
return
insert_next(A ,n - 1 )
rec_insertion_sort(A ,n - 1 )
def _A ( A ,A ... | 372 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
lowerCAmelCase : Dict = logging.getLogger(__name__)
if is_to... | 372 | 1 |
'''simple docstring'''
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
def __init__( self : Any , A__ : list[list[int]] ):
"""simple docstring"""
__lowerCamelCase : List[str] = TypeError(
... | 483 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCAmelCase__ :List[str] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCAmelCase__ :Optional[int] = typing.Union[... | 483 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils imp... | 202 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase ( UpperCa... | 202 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
def __UpperCamelCase ( self : ... | 716 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCamelCase_ ( lowerCAmelCase__ : int ) ... | 224 | 0 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Optiona... | 143 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from ... | 143 | 1 |
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 TFModel... | 718 |
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 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCAmelCase (__A):
"""simple docstring"""
if num <= 0:
_a = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(__A)
_a = [True] * ... | 11 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 288 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
class __snake_case:
def __init__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Optional[Any] = size
# approximate the overall size ... | 714 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.util... | 237 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in ran... | 46 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__nam... | 99 | 0 |
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : str , __UpperCamelCase : Any ):
"""simple docstring"""
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(__UpperCamelCase ):
for j in ra... | 705 | """simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def... | 296 | 0 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
snake_case_ : Any = logging.get_logger('transformers.models.speecht5')
def A__ ( UpperCAmelCase_ , Uppe... | 195 |
'''simple docstring'''
from __future__ import annotations
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase , _UpperCamelCase : Dict = position
_UpperCamelCase : Any = [
(y + 1, x + 2),
(y - 1, x + 2),
... | 195 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import Sequence... | 288 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : str = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMAL... | 288 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class _... | 398 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SwiftFormerConfig''',
'''SwiftFormerOnnx... | 157 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : List[str] ) -> Union[str, Any]:
__magic_name__: Optional[Any] = len(__UpperCAmelCase )
while cur > 1:
# Find the maximum number in arr
__magic_name__: str... | 213 |
"""simple docstring"""
from math import factorial
__lowerCamelCase = {str(digit): factorial(digit) for digit in range(10)}
def a ( __UpperCAmelCase : int ) -> int:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
... | 213 | 1 |
"""simple docstring"""
from __future__ import annotations
lowerCamelCase__ : Union[str, Any] = tuple[int, int, int]
lowerCamelCase__ : List[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCamelCase__ : int ... | 238 | '''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 390 | 0 |
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 711 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
S... | 51 | 0 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a_ ( __a ):
return "".join(sorted(__a ) )
def a_ ( __a ):
return word_by_signature[signature(__a )]
__snake_c... | 571 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, 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_a... | 571 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : str = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
excep... | 518 |
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def __magic_name__ ( ) -> Optional[Any]:
lowercase : Optional[Any] = 9
lowercase : str = [
[0, 1, 4],
[0, 7, 8],
[1, 2,... | 518 | 1 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Tuple = 2
snake_case_ : Any = []
while i * i <= n:
if n ... | 480 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a_ = {... | 480 | 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_convbert import ConvBertTokenizer
_lowercase: List[str] = logging.get_logger(__name__)
_lowercase: Union[str... | 710 | import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTest... | 225 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def snake_case ( snake_case : list[float] ) -> Tuple:
"""simple docstring"""
return np.maximum(0 , snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
... | 284 |
'''simple docstring'''
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)
_UpperCa... | 284 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
... | 711 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator... | 376 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : str = "cpu" , __lowerCAmelCase : Union[str, None] = None ):
lowerCamelCase__ = torch.load(__lowerCAme... | 50 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 633 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_... | 633 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class __snake_case ( SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ = field(default='language-modeling' , met... | 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 |
def lowerCAmelCase_ ( snake_case_ : str = 1_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_ = set(range(3 , __lowerCAmelCase , 2 ) )
primes.add(2 )
for p in range(3 , __lowerCAmelCase , 2 ):
... | 712 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_: Dict ={
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Au... | 415 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__a: Any = TypeVar('''T''')
class SCREAMING_SNAKE_CASE__ ( Generic[T] ):
'''simple docstring'''
_lowerCamelCase = 42 # Cache store of keys
_lowerCamelCase ... | 108 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class lowercase_ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
def SCREAMING_SNA... | 41 | 0 |
'''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() and is_transformers_version(">=", "4.25.0")):
raise OptionalDepend... | 712 |
'''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():
f... | 419 | 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 AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase :int = logging.get_logger(__na... | 561 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase :Optional[Any] = loggi... | 561 | 1 |
'''simple docstring'''
from __future__ import annotations
def a_ ( _UpperCAmelCase : list[float] ) -> float:
__snake_case : str = 0.0_0
__snake_case : Union[str, Any] = 0
for resistor in resistors:
if resi... | 703 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common imp... | 124 | 0 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def a ( __UpperCAmelCase : Iterable[str] , __UpperCAmelCase : int ) -> str:
__magic_name__: Optional[int] = iter(__UpperCAm... | 96 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 563 | 0 |
"""simple docstring"""
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 s... | 707 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A__ : Any= """src/diffusers"""
# Matches is_xxx_available()
A__ : Tuple= re.c... | 20 | 0 |
import logging
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,
BertEncod... | 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'ctr... | 68 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
"""configuration_mobilebert""": [
... | 562 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ ( _lowerCAmelCase ... | 562 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
__SCREAMING_SNAKE_CASE : str = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
__SCREAMING_SNAKE_CASE ... | 452 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCamelCase( lowercase__ ) -> List[Any]:
'''simple docstring'''
if not is_accelerate_available():
return method
__lowercase= vers... | 230 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid ... | 35 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_availab... | 35 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/re... | 109 |
from collections import deque
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = len(_A )
lowerCAmelCase_ = deque()
lowerCAmelCase_ = [False for _ in range(_A )]
lowerCAmelCase_ = [-1 for _ in range(_A )]
lowerCAmelCase_ = ind... | 431 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorTyp... | 704 | '''simple docstring'''
import unittest
import numpy as np
def __UpperCamelCase( _A : np.ndarray , _A : np.ndarray , _A : np.ndarray , _A : np.ndarray | None = None , ):
'''simple docstring'''
UpperCAmelCase__ : Any = np.shape(_A )
UpperCAmel... | 496 | 0 |
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