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 |
|---|---|---|---|---|
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ (UpperCamelCase_ ):
def __init__( self : Optional[Any] , __lowerCamelCase : List[str] , __lowerCamelCase : List[str] ):
... | 615 | 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_SNAKE_CASE__ : Opti... | 85 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def UpperCAmelCase_ ( lowerCAmelCase_ ... | 459 |
'''simple docstring'''
from functools import lru_cache
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
lowercase = 2
lowercase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 459 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( _snake_case , unittest.TestCase ):
lowercase = ... | 288 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Dict:
A_ = sorted(zip(UpperCAmelCase__, UpperCAmelCase__ )... | 288 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 40 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 40 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transf... | 31 |
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():
from .tokenization_fnet import F... | 31 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase :Union[str, Any] = logging.get_logger(__name__)
__lowercase :Any = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models a... | 704 |
def UpperCAmelCase ( _lowerCamelCase : int = 4_000_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [0, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 26 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__a = pd.read_csv("sample_data.csv", header=None)
__a ... | 374 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__a = importlib.util.find_spec("s3fs") is not None
if _has_safs:
from .safilesyst... | 374 | 1 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 118 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def snake_case (A_ :int = 8 ):
'''simple docstring'''
a : Tuple = ascii_letters + digits + punctuation
return "".... | 118 | 1 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import loggi... | 58 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transforme... | 633 | 0 |
from __future__ import annotations
from collections import Counter
from random import random
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[int] ) -> Dict:
"""simple docstring"""
__magic_name__... | 704 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def a__ ( A_, A_ ):
'''simple docstring'''
assert i... | 76 | 0 |
def __UpperCAmelCase ( a_):
return 10 - x * x
def __UpperCAmelCase ( a_ , a_):
if equation(_snake_case) * equation(_snake_case) >= 0:
raise ValueError('Wrong space!')
snake_case_ = a
while (b - a) >= 0.01:
# Find middle po... | 198 | """simple docstring"""
import argparse
import json
import subprocess
def snake_case__ ( _snake_case : str , _snake_case : Any ):
"""simple docstring"""
UpperCamelCase__ = []
UpperCamelCase__ = (
F'curl -H "... | 516 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : Optional[int] = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
... | 700 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def snake_case_ ( lowerCAmelCase_ : Dict ):
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowerCAmelCase... | 649 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
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_common import ConfigTester
from ...test_... | 25 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __lowerCAmelCase ( _UpperCamelC... | 266 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A__ ( A__ ):
"""simple docstring"""
def _UpperCamelCase( self : str , lowerCamelCase__ : str ):
with open(lowerCamelCase__ , encodi... | 151 |
def UpperCamelCase_ ( __a = 3 , __a = 7 , __a = 1_000_000 ) -> int:
a__ : List[Any] = 0
a__ : int = 1
for current_denominator in range(1 , limit + 1 ):
a__ : Optional[Any] = current_denominator * numerator /... | 151 | 1 |
"""simple docstring"""
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = None
... | 93 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
fr... | 72 | 0 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = list[list[int]]
# assigning initial values to the grid
__UpperCAmelCase = [
[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, ... | 256 |
"""simple docstring"""
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__":
__UpperCAmelCase = '%20'.join(argv[1:]) if len(argv) > 1 else quot... | 256 | 1 |
def __snake_case ( lowerCAmelCase_ = 1_0_0_0_0_0_0 ) -> int:
SCREAMING_SNAKE_CASE__ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowerCAmelCase_ ):
... | 100 |
"""simple docstring"""
from __future__ import annotations
def A_ ( snake_case_ : list ,snake_case_ : int ):
'''simple docstring'''
# Checks if the entire collection has been sorted
if len(snake_case_ ) <= 1 or n <= 1:
return
insert_next(snak... | 499 | 0 |
import math
import sys
def _a ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
UpperCamelCase__ : Dict = ''''''
try:
with open(SCREAMING_SNAKE_CASE , '''rb''' ) as binary_file:
UpperCamelCase__ : List[Any] = binary_file.read()
for da... | 106 |
__UpperCamelCase : List[Any] = 256
# Modulus to hash a string
__UpperCamelCase : Union[str, Any] = 100_0003
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
UpperCamelCase__ : Optio... | 106 | 1 |
import json
import sys
def lowercase__( A , A ):
with open(A , encoding='utf-8' ) as f:
snake_case__ : Dict = json.load(A )
snake_case__ : List[str] = ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']
... | 170 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case__ ( UpperCamelCase_ ):
@staticmethod
@abstractmethod
def UpperCAmelCase__ ( _lowerCamelCase : ArgumentParser ):
raise NotImplementedError()
@abstractmet... | 170 | 1 |
'''simple docstring'''
lowerCAmelCase__ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCAmelCase__ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCAmelCase__ = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''... | 624 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 624 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_av... | 572 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 619 | 0 |
# Copyright 2021 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 req... | 559 |
from sklearn.metrics import mean_squared_error
import datasets
__a : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blond... | 559 | 1 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator... | 136 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipel... | 136 | 1 |
"""simple docstring"""
def A__ ( A__ , A__ = 0 ) -> list:
'''simple docstring'''
_UpperCAmelCase = length or len(A__ )
_UpperCAmelCase = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
_UpperCAmelCase , _Upper... | 579 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
SCREAMING_SNAKE_CASE_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
... | 579 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_UpperCAmelCase : Optional[Any] = """docs/source/en/_toctree.yml"""
def snake_case__ ( UpperCamelCase ) -> List[str]:
_UpperCamelCase : int = defaultdict(UpperCamelCase )
... | 683 |
'''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,
require_vision,
s... | 683 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIV... | 719 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main... | 340 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_... | 336 |
def _snake_case( SCREAMING_SNAKE_CASE__ = 4_000_000 ) -> int:
lowercase : List[str] = [0, 1]
lowercase : str = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 336 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase( a , a , a ):
if len(a ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(a )
or left < -len(a ... | 702 | """simple docstring"""
def _lowerCamelCase( a ):
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def _lowerCamelCase( a ):
__a = 0
__a = number
while duplicate > 0:
__a , __a = divmod(a , ... | 67 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( _UpperCamelCase , unittest.Test... | 52 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AutoformerConfig",
],
}
... | 386 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script fro... | 701 | '''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowerCAmelCase : Any = ""
__lowerCAmelCase : int = ""
__lowerCAmelCase : Union[str, Any] = ""
__lowerCAmelCase : Any =... | 654 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'vocab_file': 'vocab.json',
... | 421 |
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 ModelTesterMixin, ids_tensor, r... | 219 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transfor... | 721 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase_ ( ... | 573 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embe... | 118 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 295 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __UpperCamelCase( _A : Optional[int] ... | 707 | '''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformer... | 496 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = [
['''att... | 75 |
'''simple docstring'''
import functools
def a_ ( _UpperCAmelCase : list[int] ,_UpperCAmelCase : list[int] ) -> int:
# Validation
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ) or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase ) for day in days )... | 286 | 0 |
def a_ (_lowerCAmelCase : int = 600851475143 )-> int:
try:
snake_case: List[str] = int(_lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("... | 701 | 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'):
__lowerCAmelCase : List[Any] = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamp... | 164 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = args.pruning_method
lowercase__ = args.threshold
lowercase__... | 43 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
Albe... | 400 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
def SCREAMING_SNAKE_CASE__ ( ... | 701 |
'''simple docstring'''
import functools
def SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# Validation
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not all(isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_C... | 305 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
# See all Donut models at http... | 62 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : int = 6 ):
SCREAMING_SNAKE_CASE : Node | None = None
SCREA... | 62 | 1 |
import math
from numpy import inf
from scipy.integrate import quad
def UpperCamelCase__ ( UpperCAmelCase ) -> float:
if num <= 0:
raise ValueError('''math domain error''' )
return quad(__UpperCAmelCase , 0 , __UpperCAmelCase , ... | 713 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {'vocab_file': 'vocab.txt'}
__lowerCamelCase = {
... | 307 | 0 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int = 1000 ) -> int:
UpperCAmelCase : List[str] = -1
UpperCAmelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> list:
UpperCAmelCase : Union[str, Any] = int(_lowerCAmelCase )
if n_element < 1:
UpperCAmelCase : int = ValueError('''a should be a positive num... | 127 | 1 |
import os
def _UpperCAmelCase ( ):
with open(os.path.dirname(SCREAMING_SNAKE_CASE__ ) + '/p022_names.txt' ) as file:
__UpperCamelCase =str(file.readlines()[0] )
__UpperCamelCase =names.replace('"' , '' ).split(',' )
na... | 682 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
P... | 682 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : Dict = "laptop" ) -> DataFrame:
SCREAMING_SNAKE_CASE_ : str =f'https://www.amazon.in/laptop/s?k={product}'
... | 443 |
from __future__ import annotations
from statistics import mean
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
snake_case__ = [0] * no_of_processes
snake_case__ = [0] * no_of_processes
# Initialize ... | 33 | 0 |
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
@require_torch
@require_optimum
@slow
class _lowercase ... | 710 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _lowerCAmelCase (_lowerCAmelCase):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name)... | 504 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__UpperCAmelCase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__Uppe... | 40 |
'''simple docstring'''
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,
... | 507 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCAmelCase__ = ''''''
lowerCAmelCase__ = ''''''
lowerCAmelCase__ = ''''''
lowerCAmelCase__ = 1 # (0 is vertical, 1 is horizontal)
def _A ( ):
... | 624 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowerCAmelCase__ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation... | 624 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ ={
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
tr... | 415 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
return "".join(chr(ord(SCREAMING_SNAKE_CASE__ ) - 32 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 533 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 494 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCa... | 494 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
snake_case_ : List[Any] = {
'''configuration_speech_to_text''': ['''SPEECH_TO_T... | 691 |
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
if is_vision_ava... | 691 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION... | 713 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}
try:
... | 236 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lowerCAmelCase_ ( __snak... | 66 |
"""simple docstring"""
def a_ ( __a ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
A__ = sorted(string.lower() )
return len(__a ) == len(set(__a ) )
if __... | 571 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _A ( lowercase_ ,unitte... | 713 |
"""simple docstring"""
import os
def lowercase ( )-> Optional[Any]:
'''simple docstring'''
a : Optional[int] = os.path.join(os.path.dirname(A_ ) , "num.txt" )
with open(A_ ) as file_hand:
return str(sum(int(A_ ) for line in file_ha... | 135 | 0 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCAmelCase_ ... | 326 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class A :
_SCREAMING_SNAKE_CASE = field(
default="""codeparrot/codeparrot""" ,metadata={"""help""": """Model name or path of model to be trained."""} )
_SCREAMING_SNAKE_CASE = ... | 326 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.... | 710 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
a =logging.get_logger(__name__)
class A_ ( SCREAMING_SNAKE_CASE ):
def __init__( self : Tuple ,*SCREAMING_SNAKE_CASE__ : Any ,**SCREAMING_SNAKE_CASE__ : Li... | 337 | 0 |
from functools import lru_cache
@lru_cache
def __lowercase ( lowerCamelCase : int ):
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 417 | import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a_ = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of... | 417 | 1 |
from __future__ import annotations
import numpy as np
def _UpperCAmelCase (UpperCamelCase_ : np.ndarray ):
'''simple docstring'''
_lowerCAmelCase : int = np.shape(UpperCamelCase_ )
if rows != columns:
_lowerCAmelCase : Optional[int] = ... | 715 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _UpperCAmelCase (UpperCamelCase_ : list[list[float]] ):
'''simple docstring'''
_lowerCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
... | 196 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Tuple =logging.get_logger(__name__)
A_ : List[str] ={
"""xlm-mlm-en-2048""": ... | 483 | import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
... | 613 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _UpperCamelCase (a__ :Union[str, Any] , a__ :Optional[Any]=1 ):
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return "."... | 548 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _UpperCamelCase ():
"""simple docstring"""
UpperCamelCase__ = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
UpperCamelCase__ ... | 548 | 1 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils impo... | 488 | """simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->Union[str, Any]:
return ConvertCommand(
args.model_type , args.tf_checkpoint ,... | 434 | 0 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {name: g... | 641 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
... | 641 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to... | 364 |
"""simple docstring"""
__UpperCAmelCase = 256
# Modulus to hash a string
__UpperCAmelCase = 1_000_003
def lowercase__ ( lowerCamelCase : str , lowerCamelCase : str ) -> bool:
lowerCAmelCase__ : Optional[Any] = ... | 308 | 0 |
'''simple docstring'''
from math import factorial
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Any ,a__ : List[Any] ,a__ : int ):
a__ = real
if isinstance(a__... | 394 |
'''simple docstring'''
def _lowerCAmelCase (_lowercase ):
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
a__ = gray_code_sequence_string(_lowercase )
... | 394 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> Any:
"""simple docstring"""
snake_case_ : Dict = []
snake_case_ : int = set({'''(''', '''[''', '''{'''} )
snake_case_ : Dict = set({''')''', ''']''', '''}'''} )
... | 60 | """simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _A ( lowerCAmelCase , unittest.TestCase ):
... | 359 | 0 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImageP... | 219 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> float:
"""simple docstring"""
if edge <= 0 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) *... | 219 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ..... | 70 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : int = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
r... | 519 | 0 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _lowercase ( a__ : dict ) -> tuple:
"""simple docstring"""
... | 702 |
import os
from math import logaa
def _lowercase ( a__ : str = "base_exp.txt" ) -> int:
"""simple docstring"""
_UpperCamelCase = 0
_UpperCamelCase = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a__ ) , a__ ) ) ):
_UpperCamelCase ... | 589 | 0 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n... | 85 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_t... | 584 | 0 |
'''simple docstring'''
import os
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: List[Any] = os.path.dirname(os.path.realpath(__UpperCamelCase ) )
snake_case: int = os.path.join(__UpperCamelCase , 'triangle.txt' )
with open(__U... | 711 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase ... | 692 | 0 |
from math import sqrt
def __lowerCAmelCase ( __snake_case ):
assert isinstance(A__ , A__ ) and (
number >= 0
), "'number' must been an int and positive"
__lowerCAmelCase = True
# 0 and 1 are none primes.
if number <= 1:
... | 367 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class UpperCAmelCase__( lowerCamelCase , un... | 622 | 0 |
'''simple docstring'''
def snake_case__ ( _A: int ) -> str:
'''simple docstring'''
lowerCAmelCase = int(_A )
if decimal in (0, 1): # Exit cases for the recursion
return str(_A )
lowerCAmelCase , lowerCAmelCase = divmod(_A , ... | 605 | '''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_co... | 605 | 1 |
'''simple docstring'''
import re
from ..utils import cached_file
# docstyle-ignore
lowerCAmelCase : int = """\nHuman: <<task>>\n\nAssistant: """
lowerCAmelCase : Optional[Any] = """huggingface-tools/default-prompts"""
lowerCAmelCase : Optional... | 444 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
SCREAMING_SNAKE_CASE__ = None
try:
import msvcrt
except ImportError:
SCREAMING_SNAKE_CASE__ = None
try:
import fcntl
except ImportError:... | 267 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ ={'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}
try:
... | 719 |
from collections import deque
class UpperCamelCase__ :
def __init__(self : str , snake_case_ : str , snake_case_ : int , snake_case_ : int ):
__a : Optional[Any] = process_name # process name
__a : Opt... | 326 | 0 |
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
try:
_a : Any = float(UpperCamelCase__ )
except ValueError:
raise ValueError("""Please enter a valid number""" )
_a : Any = decimal... | 389 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num ... | 389 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
fr... | 712 | from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __snake_case :
__lowerCamelCase = field(
metadata={"""help""": ... | 699 | 0 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
UpperCAmelCase : Tuple = namedtuple(
'_TestCo... | 139 |
"""simple docstring"""
import itertools
import math
def lowerCamelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 139 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_... | 704 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCamelCase__ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''token... | 143 | 0 |
"""simple docstring"""
def lowercase__(A ) ->list:
"""simple docstring"""
def merge(A , A ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop... | 218 | 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... | 670 | 0 |
'''simple docstring'''
import math
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
'''simple docstring'''
__lowercase = len(_UpperCAmelCase )
__lowercase = int(math.floor(math.sqrt(_UpperCAmelCase ) ) )
__lowercase = 0
while arr[min(_UpperCAmelCase ,... | 719 | from __future__ import annotations
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
'''simple docstring'''
if len(_UpperCAmelCase ) < k or k < 0:
raise ValueError("Invalid Input" )
__lowercase = __lowercase = sum(array[:k] )
for i in range(len(_UpperCAmelCas... | 576 | 0 |
from jiwer import compute_measures
import datasets
_UpperCAmelCase : Optional[int] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: impro... | 668 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
_UpperCAmelCase : int = Mapping[str, np.ndarray]
_UpperCAmelCase : Optional[Any] = Mapping[str, Any] # ... | 668 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_... | 705 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 64 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMSchedule... | 66 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Op... | 668 | 0 |
'''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib im... | 703 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from docte... | 389 | 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, prepare_image... | 104 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
SCREAMING_SNAKE_CASE_ = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = '''\
@InPr... | 465 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutpu... | 701 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that wi... | 71 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_availabl... | 18 |
from __future__ import annotations
_lowercase : Optional[int] =1.6021E-19 # units = C
def lowerCAmelCase_ ( _lowercase : float , _lowercase : float , _lowercase : float , ) -> tuple[str, float]:
"""simple docstring"""
if (condu... | 136 | 0 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase ):
if not nums:
raise ValueError("""List is empty""" )
return sum(lowercase ) / len(lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 1 |
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():
from .tokenization_rembert impor... | 106 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__snake_case :Any =None
try:
import msvcrt
except ImportError:
__snake_case :Union[str, Any] =None
try:
import fcntl
except ImportError:
__snake_case :str ... | 106 | 1 |
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
lowercase = logging.get_logger(__name__)
lowercase ... | 607 |
import unittest
import numpy as np
from transformers import RoFormerConfig, 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 tra... | 607 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Optional[int] = {
'configuration_blend... | 50 |
"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggin... | 353 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( A_ , u... | 173 | """simple docstring"""
from __future__ import annotations
class a :
def __init__( self : List[str] , lowerCamelCase_ : list[list[int]] ) -> Any:
__a = TypeError(
"""Matrices must be formed from a list of zero or more lists containing... | 173 | 1 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A_ : Union[str, Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A_ : Optional... | 57 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo... | 57 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not... | 517 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowerCAmelCase ( ctypes.Structure ):
"""simple docstring"""
snake_case_ = [(... | 517 | 1 |
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 ... | 21 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
A__ : Optional[Any] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trai... | 153 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ : str = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 712 | '''simple docstring'''
import functools
from typing import Any
def __a ( __lowerCamelCase : str , __lowerCamelCase : list[str] ) -> bool:
'''simple docstring'''
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or len(__lowerCamelCase ) == 0:
raise Value... | 461 | 0 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( UpperCamelCase_ : Li... | 632 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
"p... | 632 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageP... | 166 |
def __UpperCAmelCase ( snake_case_ : Any , snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase: Optional[Any] = ""
for i in table:
res += inp[i - 1]
return res
def __UpperCAmelCase ( snake_case_ : O... | 166 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffuse... | 49 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmToken... | 365 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''facebook/s2t-small-librispeech-asr''': (
... | 623 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 623 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTo... | 523 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-sp... | 523 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowerCamelCase ( _lowercase ) -> List[str... | 672 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
a : List[str] = logging.get_logger(__name__)... | 672 | 1 |
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