code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
a__ : str = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ... | 714 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ ):
if b == 0:
return (1, 0)
((lowercase__), (lowercase__)) = extended_euclid(A__ , a % b )
lowercase__ = a // b
return (y, x - k * y)
def _lowerCAmelCase ( A__ , A__... | 642 | 0 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
a__ = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"self.proj": "output.dense",
"attention.s... | 715 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[Any] = {
"google/umt5-small": "https://huggingface.co/google... | 642 | 0 |
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
a__ : Dict = logging.get_logger(__name__)
a__ : List[str] =... | 716 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_com... | 642 | 0 |
from __future__ import annotations
from math import gcd
def _lowerCAmelCase ( A__ , A__ = 2 , A__ = 1 , A__ = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('The input value cannot be less than 2' )
... | 717 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase : str) -> Optional[int]:
"""simple docstring"""
lowercase__ ... | 642 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ... | 718 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
a__ ... | 642 | 0 |
import math
import sys
def _lowerCAmelCase ( A__ ):
lowercase__ = ''
try:
with open(A__ , 'rb' ) as binary_file:
lowercase__ = binary_file.read()
for dat in data:
lowercase__ = F'''{dat:08b}'''
r... | 719 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : str = (DDIMParallelScheduler,)
A : Any = (("eta", 0.0), ("num_inference_step... | 642 | 0 |
def _lowerCAmelCase ( A__ = 1_000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 720 |
import cva
import numpy as np
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase : float , lowerCAmelCase : int) -> Dict:
"""simple docstring"""
if k in (0.04, 0.06):
lowercase__ ... | 642 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, tor... | 721 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
),
... | 642 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCAmelCase : Optional[Any] = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase : Optional[Any] = loggin... | 643 | 1 |
from PIL import Image
def a ( SCREAMING_SNAKE_CASE_ : Image , SCREAMING_SNAKE_CASE_ : float ):
"""simple docstring"""
def brightness(SCREAMING_SNAKE_CASE_ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)... | 643 |
import requests
from bsa import BeautifulSoup
def a ( SCREAMING_SNAKE_CASE_ : str = "AAPL" ):
"""simple docstring"""
UpperCamelCase : Dict = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Any = ... | 643 | 1 |
import requests
from bsa import BeautifulSoup
def a ( SCREAMING_SNAKE_CASE_ : str = "AAPL" ):
"""simple docstring"""
UpperCamelCase : Dict = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Any = ... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCamelCase : List[str] = len(bin(SCREAMING_SNAKE_CASE_ )[3:] )
... | 643 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from ... | 643 |
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
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : Dict = ... | 643 | 1 |
from math import factorial
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : Dict = real
... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCamelCase : int = st... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCAmelCase : Tuple = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:... | 643 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
"""s... | 643 | 1 |
import pytest
__UpperCAmelCase : Tuple = "__dummy_dataset1__"
__UpperCAmelCase : Tuple = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"v... | 643 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a ( SCREAMING_SNAKE_CASE_ : bool = True , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : Tuple ... | 643 | 1 |
from heapq import heappop, heappush
import numpy as np
def a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : tuple[int, int] , SCREAMING_SNAKE_CASE_ : tuple[int, int] , SCREAMING_SNAKE_CASE_ : bool , ):
"... | 643 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : int = "▁"... | 643 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : int = logging.get_logger(__name__)
__UpperCAmelCase : Any = {
"camembert-base": "https://... | 643 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 643 | 1 |
from math import factorial
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' ... | 643 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__UpperCAmelCase : Optional[int] = 500000
__UpperCAmelCase , __UpperCAmelCase : Any = os.path.split(__file__)
__UpperCAmelCase : int = os.path... | 643 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def a ( SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
"""simple docstring"""
UpperCamelCase : Any = int(SCREAMING... | 643 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 643 | 1 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int ... | 643 |
import torch
from transformers import AutoModel
class UpperCAmelCase_ ( torch.nn.Module):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 643 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mod... | 643 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class Up... | 643 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 643 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def _lowercase ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase : int = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_... | 643 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 643 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Dict = logging.get_logger(__name__)
__UpperCAmelCase : Dict = {
"vocab_file": "vocab.jso... | 643 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_... | 643 | 1 |
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,
PixaStruct... | 643 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__UpperCAmelCase : Dict = False
class UpperCAmelCase_ ( unittest.TestCase):
... | 643 | 1 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__UpperCAmelCase : List[str] = "\\n@misc{chen2021evaluating,\n title={Evalua... | 643 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import ... | 643 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__Upper... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
f... | 643 | 1 |
from random import randint, random
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_C... | 643 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : list[int] ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return array
UpperCamelCase , UpperCamelCase : Union[str, Any] = min(S... | 643 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__UpperCAmelCase : Tuple = TypeVar("T")
__UpperCAmelCase : Dict = TypeVar("U")
class UpperCAmelCase_ ( Generic[T, U]):
'''simple docstring'''
... | 643 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : List[An... | 643 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__UpperCAmelCase : Any = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={... | 643 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int ... | 643 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__UpperCAmelCase : str = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase : Optional[Any] = loggin... | 643 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
"""s... | 643 |
import requests
from bsa import BeautifulSoup
def a ( SCREAMING_SNAKE_CASE_ : str = "AAPL" ):
"""simple docstring"""
UpperCamelCase : Dict = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Any = ... | 643 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCamelCase : List[str] = len(bin(SCREAMING_SNAKE_CASE_ )[3:] )
... | 643 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase_ ( _a, unittest.TestCase):
... | 643 |
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
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : Dict = ... | 643 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCamelCase : int = st... | 643 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class UpperCAmelCase_ ( unittest.TestCase):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : List[Any] = ... | 643 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
"""s... | 643 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : int = 1_0_0_0 ):
"""simple docstring"""
UpperCamelCase : str = -1
UpperCamelCase : Dict = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**... | 643 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a ( SCREAMING_SNAKE_CASE_ : bool = True , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : Tuple ... | 643 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
__UpperCamelCase : str ... | 643 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : int = "▁"... | 643 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : int = 4_0_0_0_0_0_0 ):
"""simple docstring"""
UpperCamelCase : Dict = [0, 1]
UpperCamelCase : Any = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
i... | 643 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 643 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
... | 643 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__UpperCAmelCase : Optional[int] = 500000
__UpperCAmelCase , __UpperCAmelCase : Any = os.path.split(__file__)
__UpperCAmelCase : int = os.path... | 643 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class UpperCAmelCase_ ( unittest.TestCase):
'''simple docstring'''
__UpperCamelCase : Any = JukeboxTokenizer
__UpperCamelCase : Uni... | 643 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 643 | 1 |
import math
__UpperCAmelCase : List[Any] = 10
__UpperCAmelCase : List[Any] = 7
__UpperCAmelCase : str = BALLS_PER_COLOUR * NUM_COLOURS
def a ( SCREAMING_SNAKE_CASE_ : int = 2_0 ):
"""simple docstring"""
UpperCamelCase : Di... | 643 |
import torch
from transformers import AutoModel
class UpperCAmelCase_ ( torch.nn.Module):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 643 | 1 |
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
UpperCamelCase : Optional[int] = {}
def _lowercase ( self ):
"""simple doc... | 643 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class Up... | 643 | 1 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__UpperCAmelCase : Tuple = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "http... | 643 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def _lowercase ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring... | 643 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str = "cpu" , SCREAMING_SNAKE_CASE_ : Union[str, None] = None ):
"""simple docstring"""
... | 643 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 643 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for... | 643 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__UpperCAmelCase : int = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT... | 643 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__UpperCAmelCase : Dict = False
class UpperCAmelCase_ ( unittest.TestCase):
... | 643 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__UpperCAmelCase : Any = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
... | 643 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import ... | 643 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import ... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
f... | 643 | 1 |
__UpperCAmelCase : 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"
__UpperCAmelCase : List[An... | 643 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : list[int] ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return array
UpperCamelCase , UpperCamelCase : Union[str, Any] = min(S... | 643 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTes... | 643 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : List[An... | 643 | 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 ... | 643 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int ... | 643 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : int = logging.get_logger(__name__)
__UpperCAmelCase : Optional[Any] = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsof... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase : Optional[Any] = loggin... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__UpperCAmelCase : List[str] = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not ... | 643 |
import requests
from bsa import BeautifulSoup
def a ( SCREAMING_SNAKE_CASE_ : str = "AAPL" ):
"""simple docstring"""
UpperCamelCase : Dict = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Any = ... | 643 | 1 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCamelCase : List[str] = len(bin(SCREAMING_SNAKE_CASE_ )[3:] )
... | 643 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_a), "Tatoeba directory... | 643 |
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
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : Dict = ... | 643 | 1 |
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_ ( _a, ... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCamelCase : int = st... | 643 | 1 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : list[int] ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return array
UpperCamelCase , UpperCamelCase : Union[str, Any] = min(S... | 643 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
"""s... | 643 | 1 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 1_0, '''max_num_jobs''': 1}, ... | 643 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a ( SCREAMING_SNAKE_CASE_ : bool = True , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : Tuple ... | 643 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCAmelCase : str = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xln... | 643 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : int = "▁"... | 643 | 1 |
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_available
from ...test_backbone_common... | 643 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 643 | 1 |
# 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 require... | 643 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__UpperCAmelCase : Optional[int] = 500000
__UpperCAmelCase , __UpperCAmelCase : Any = os.path.split(__file__)
__UpperCAmelCase : int = os.path... | 643 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__UpperCAmelCase : Optional[int] = datasets.logging.get_logger(__name__)
__UpperCAmelCase : List[str] = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for... | 643 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 643 | 1 |
from math import pow, sqrt
def a ( *SCREAMING_SNAKE_CASE_ : float ):
"""simple docstring"""
UpperCamelCase : Tuple = len(SCREAMING_SNAKE_CASE_ ) > 0 and all(value > 0.0 for value in values )
return result
def a ... | 643 |
import torch
from transformers import AutoModel
class UpperCAmelCase_ ( torch.nn.Module):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 643 | 1 |
# coding=utf-8
# Copyright 2023 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 require... | 643 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class Up... | 643 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__UpperCAmelCase : Dict = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def __init__( self , *__SCREA... | 643 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def _lowercase ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring... | 643 | 1 |
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 transformers imp... | 643 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 643 | 1 |
from PIL import Image
def a ( SCREAMING_SNAKE_CASE_ : Image , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : Tuple = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(SCREAMING... | 643 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_... | 643 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
""... | 643 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__UpperCAmelCase : Dict = False
class UpperCAmelCase_ ( unittest.TestCase):
... | 643 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say ... | 643 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import ... | 643 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
'''s... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
f... | 643 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 643 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : list[int] ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return array
UpperCamelCase , UpperCamelCase : Union[str, Any] = min(S... | 643 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Dict = logging.get_logger(__name__)
__UpperCAmelCase : Dict = {
"xlm-mlm-en-2048": "https... | 643 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : List[An... | 643 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
U... | 643 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int ... | 643 | 1 |
# 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 require... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase : Optional[Any] = loggin... | 643 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : str = len(SCREAMING_SNAKE_CASE_ ) + 1
UpperCamelCase : Tuple = len(SCREAMING_SNAKE_CASE_ ) + 1
... | 643 |
import requests
from bsa import BeautifulSoup
def a ( SCREAMING_SNAKE_CASE_ : str = "AAPL" ):
"""simple docstring"""
UpperCamelCase : Dict = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Any = ... | 643 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impor... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCamelCase : List[str] = len(bin(SCREAMING_SNAKE_CASE_ )[3:] )
... | 643 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : List[str] = len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = []
for i in range(l... | 643 |
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
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : Dict = ... | 643 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCamelCase : List[str] = len(bin(SCREAMING_SNAKE_CASE_ )[3:] )
... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCamelCase : int = st... | 643 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : int = "▁"... | 643 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
"""s... | 643 | 1 |
import os
import sys
import unittest
__UpperCAmelCase : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_te... | 643 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a ( SCREAMING_SNAKE_CASE_ : bool = True , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : Tuple ... | 643 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCAmelCase : Optional[Any] = r"\n Args:\n ... | 643 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : int = "▁"... | 643 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from trans... | 643 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 643 | 1 |
import string
from math import logaa
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : int = document.translate(
str.maketrans('''''' , '''''' ... | 643 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__UpperCAmelCase : Optional[int] = 500000
__UpperCAmelCase , __UpperCAmelCase : Any = os.path.split(__file__)
__UpperCAmelCase : int = os.path... | 643 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : Dict ): # noqa: E741
"""simple docstring"""
UpperCamelCase : List[str] = len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 0
UpperCamelCase : Dict = [0] * n
Upper... | 643 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 643 | 1 |
# 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 require... | 643 |
import torch
from transformers import AutoModel
class UpperCAmelCase_ ( torch.nn.Module):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 643 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : int = logging.get_logger(__name__)
__UpperCAmelCase : Tuple = "▁"
__Upper... | 643 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class Up... | 643 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ..... | 643 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def _lowercase ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring... | 643 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__UpperCAmelCase : Union[str, Any] =... | 643 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ... | 643 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
__UpperCamelCase : torch.Tensor # [batch_size x 3]
__UpperCamelCase : torch.Tensor # [batch_size x 3]
... | 643 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_... | 643 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( _a):
'''simple docstring'''
def _lowercase ( self , __SCREAMING_SNAKE_CASE ):
... | 643 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__UpperCAmelCase : Dict = False
class UpperCAmelCase_ ( unittest.TestCase):
... | 643 | 1 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common impo... | 643 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import ... | 643 | 1 |
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
UpperCamelCase : List[Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase : Optional[Any] = ''''''
UpperCamelCase ... | 643 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
f... | 643 | 1 |
import inspect
import unittest
from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
from ... | 643 |
from __future__ import annotations
def a ( SCREAMING_SNAKE_CASE_ : list[int] ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return array
UpperCamelCase , UpperCamelCase : Union[str, Any] = min(S... | 643 | 1 |
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
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : Dict = ... | 643 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : List[An... | 643 | 1 |
import pprint
import requests
__UpperCAmelCase : Union[str, Any] = "https://zenquotes.io/api"
def a ( ):
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def a ( ):
"""sim... | 643 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int ... | 643 | 1 |
import os
import string
import sys
__UpperCAmelCase : Optional[Any] = 1 << 8
__UpperCAmelCase : List[Any] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68... | 643 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase : Optional[Any] = loggin... | 643 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase : Tuple = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvB... | 643 |
import requests
from bsa import BeautifulSoup
def a ( SCREAMING_SNAKE_CASE_ : str = "AAPL" ):
"""simple docstring"""
UpperCamelCase : Dict = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCamelCase : Any = ... | 643 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_... | 643 |
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCamelCase : List[str] = len(bin(SCREAMING_SNAKE_CASE_ )[3:] )
... | 643 | 1 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_a):
'''simple docstring'''
__UpperCamelCase : str = ["torch", "transformers", "onnx"]
def __init__( self , *__SCREAMING_SNAKE_CASE , **__SCREAMIN... | 643 |
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
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : Dict = ... | 643 | 1 |
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