code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
class _SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowerCamelCase : int ):
UpperCamelCase :Tuple = order
# a_{0} ... a_{k}
UpperCamelCase :Dict = [1.0] + [0.0] * order
# b_{0} ... b_{k}
UpperCam... | 38 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(__magic_name__ , _... | 38 | 1 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 353 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfi... | 292 | 0 |
from __future__ import annotations
import pandas as pd
def lowerCamelCase__ ( _A , _A , _A ):
'''simple docstring'''
snake_case_ = [0] * no_of_processes
snake_case_ = [0] * no_of_processes
# Copy the burst time into remaining_ti... | 187 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal... | 187 | 1 |
'''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, SwinCon... | 190 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : int ) -> int:
if not isinstance(__snake_case , __snake_case ):
__A : List[Any] = f'Input value of [number={number}] must be an integer'
... | 190 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
__UpperCamelCase : List[str] = """https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__UpperCamelCase : Dict ... | 307 |
import os
def a_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case__ = os.path.join(os.path.dirname(_A ) , 'num.txt' )
with open(_A ) as file_hand:
return str(sum(int(_A ) for line in file_hand ) )[:10... | 307 | 1 |
_snake_case : List[Any] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_snake_case ... | 358 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case : int = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig... | 179 | 0 |
"""simple docstring"""
def A ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
SCREAMING_SNAKE_CASE__ = mf_knapsack(i... | 165 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 0 |
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
_lowerCAmelCase : List[str] = ... | 353 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepie... | 25 | 0 |
def A ( ):
return 1
def A ( _lowercase ):
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def A ( _lowercase ):
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(_lowercase )
def A ( _lowercase ):
return 0 if x <... | 182 |
"""simple docstring"""
def A__ ( UpperCamelCase , UpperCamelCase = False ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
A = F"Expected string as input, found {type(UpperCamelCase )}"
raise ValueError(UpperCamelCase )
... | 292 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
... | 353 |
"""simple docstring"""
def __lowerCamelCase ( a_ : str , a_ : str ) -> str:
__SCREAMING_SNAKE_CASE :int = len(a_ )
__SCREAMING_SNAKE_CASE :int = len(a_ )
__SCREAMING_SNAKE_CASE :int = (
... | 239 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE (a__ , unittest.TestCase ):
lowe... | 190 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_rea... | 190 | 1 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class _A ( unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Optional[int]):
a : Tuple = 0
a ... | 226 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
log... | 226 | 1 |
import torch
from diffusers import DiffusionPipeline
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
super().__init__()
self.r... | 48 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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... | 179 | 0 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
UpperCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase__ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
Uppe... | 369 | """simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blo... | 30 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not... | 69 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, ... | 25 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> list:
UpperCamelCase_: Optional[int] = word.split()
def justify(lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> str:
UpperCamelCase_: Tuple = max_width - width
UpperCamelCase_: ... | 223 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
Ba... | 223 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = None , ) -> np.ndarray:
lowerCamelCase__ : Tuple = np.shape(... | 41 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 239 | 0 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
_lowercase : List[Any] = logging.getLogger(__name__)
... | 367 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import ji... | 91 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mas... | 226 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__A =""
__A =""
__A =""
__A =1 # (0 is vertical, 1 is horizontal)
def a ( ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmel... | 226 | 1 |
import sys
def __lowerCamelCase ( lowerCAmelCase__ ):
lowerCAmelCase__ = len(lowerCAmelCase__ )
lowerCAmelCase__ = [[0 for x in range(lowerCAmelCase__ )] for x in range(lowerCAmelCase__ )]
lowerCAmelCase__ = [[0 for x in range(lowerCAmelCas... | 119 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
],
... | 119 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table impo... | 107 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = 'sshleifer/bart-tiny-random'
__a = 'pa... | 30 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_co... | 193 |
import json
import sys
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
with open(_SCREAMING_SNAKE_CASE , encoding="""utf-8""" ) as f:
SCREAMING_SNAKE_CASE = json.lo... | 193 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_i... | 223 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCAmelCase_ ( __lowerCamelCase : List[Any] ):
return x + 2
class a_ ( unittest.TestCase ):
... | 223 | 1 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCAmelCase :
def __init__( self : Union[str, Any] ):
snake_case_ : Optional[Any] = ''''''
snake_case_ : Union[str, Any] = ''''''
snake_... | 155 |
"""simple docstring"""
def __lowercase ( _a , _a ):
return base * power(_a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
lowercase__ : Optional[Any] = int(input('''Enter... | 155 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvaila... | 130 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _A (__a , __a , __a ) -> Dict:
"""simple d... | 91 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ... | 210 |
def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__UpperCAmelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctest').testmod() | 210 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, 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, rand... | 119 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__UpperCAmelCase =... | 119 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 355 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
... | 208 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def Upper... | 193 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = ['''keras_nlp''']
def __init__( self,*__lowerCamelCase,**__lowerCamelCase ):
requires_backends(self,['''keras_... | 193 | 1 |
# using dfs for finding eulerian path traversal
def a( A : int , A : Optional[Any] , A : Any , A : Optional[int]=None ) -> List[str]:
"""simple docstring"""
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:... | 358 |
def a( A : int = 200 ) -> int:
"""simple docstring"""
a = [1, 2, 5, 10, 20, 50, 100, 200]
a = [0] * (pence + 1)
a = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(A , pence + 1 , 1 ):
... | 71 | 0 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowercase (snake_case__ : bytes , snake_case__ : int ) -> np.array:
'''simple docstring'''
lowerC... | 155 |
"""simple docstring"""
def lowercase (snake_case__ : list ) -> list:
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowerCAmelCase = []
def generate(snake_case__ : int ... | 155 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillatio... | 194 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowercase_ = 5_0_0_0_0_0
lowercase_ ,lowercase_ = os.path.split(__file__)
lowercase_ = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py... | 194 | 1 |
import torch
from torch import nn
class _UpperCamelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=1 , lowerCAmelCase__=False ) ... | 210 | import requests
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
__lowercase = {'''Content-Type''': '''application/json'''}
__lowercase = requests.post(lowercase , json={'''text''': message_body} , headers=lowerca... | 210 | 1 |
"""simple docstring"""
from math import ceil
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int = 1001 ):
'''simple docstring'''
lowercase_ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
lowercase_ = 2 * i + 1
lowercase_ = 2... | 352 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
def A__ ( self , UpperCAmelCase ) -> float:... | 297 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import ... | 336 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_... | 208 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"junnyu/roforme... | 362 |
'''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,
res... | 246 | 0 |
'''simple docstring'''
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
UpperCAmelC... | 346 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.enviro... | 71 | 0 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
_snake_case = math.sqrt(__A )
_snake_case = 1 / ... | 160 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 160 | 1 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def lowerCamelCase__ ( __snake_case = "", ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(''' ''', '''''' ).lower() ).values() ) <... | 194 |
"""simple docstring"""
import datasets
_a = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk,... | 194 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase ={
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLIPConfig",
... | 237 | '''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase =get_tests_dir... | 237 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case_ : str = '\\n\n'
snake_case_ : str = '\nPerplexity (PPL) is one of the most common m... | 83 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Tuple ):
a : Optional[int] = ''
a : Optional[Any] = ''
a : str = ... | 297 | 0 |
UpperCamelCase_ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCamelCase_ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> list[int]:
'''simple docstring'''
U... | 358 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase_ = [ord(letter) for letter in string.ascii_lowercase]
UpperCam... | 344 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''... | 41 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
lowerCamelCase__ : List[str] = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and ... | 246 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"""google/bit-50... | 351 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNo... | 160 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_co... | 160 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ... | 7 |
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 OptionalDependencyNotAvail... | 7 | 1 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accele... | 237 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp... | 237 | 1 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __lowerCAmelCase ( UpperCamelCase__ = "isbn/0140328726" ) -> dict:
__lowerCamelCase = olid.strip().strip('''/''' ) # Remove leading/trailing whi... | 362 | '''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class a__ :
def __init__( self : List[Any] , a : Tuple , a : int , a : int ):
"""simple docstring"""
if dst_width < 0 or dst_height < 0:
... | 237 | 0 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A_ ( __A , unittest.TestCase ):
'''simple docstring''... | 195 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCAmelCase ( a_ , a_ ) -> tuple:
"""simple docstring"""
if inductance <= 0:
raise ValueError("""Inductance cannot be 0 or negative""" )
... | 344 | 0 |
import mpmath # for roots of unity
import numpy as np
class __UpperCAmelCase :
def __init__( self : Dict, __A : Optional[int]=None, __A : Any=None ):
# Input as list
UpperCAmelCase : Union[str, Any] = list(poly_a or [0] )[:]
... | 364 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 99 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase , UpperCAmelCase : Any = np.shape(UpperCamelCase__ )
if rows != columns:
UpperCAmelCase : Any ... | 265 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int = 10_00 ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ : str = 3
UpperCAmelCase_ : Tuple = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 ... | 353 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING... | 67 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( _UpperCAmelCase ):... | 7 |
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ) -> float:
'''simple docstring'''
A__ ... | 7 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
UpperCamelCase : List[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 Thirio... | 370 | '''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case : int | float | str , snake_case : int | float | str ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
a : ... | 345 | 0 |
"""simple docstring"""
from __future__ import annotations
class __snake_case :
def __init__( self : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase ... | 72 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testi... | 237 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils impor... | 367 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visio... | 40 | 0 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModul... | 78 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
lowercase : Tup... | 99 | 0 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
return base * power(lowercase_ ,(exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
lowerCamelCase_ : List[Any] = int(input(''... | 362 |
"""simple docstring"""
from math import factorial
lowerCamelCase_ = {str(d): factorial(d) for d in range(10)}
def snake_case ( A__ ):
return sum(DIGIT_FACTORIAL[d] for d in str(A__ ) )
def snake_case ( ):
UpperCAmelCase_ : int = 7 * fac... | 253 | 0 |
import math
import os
import sys
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> str:
"""simple docstring"""
__lowerCamelCase = ''
try:
with open(UpperCamelCase__ , 'rb' ) as binary_file:
... | 90 | '''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class a__ ( UpperCAmelCase__ ):
lowerCamelCase : Dict ="M-CLIP"
def __init__( self : Tuple , a : Optional[int]=10_24 , a : Tuple=7_68 , **a : ... | 67 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, 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_... | 253 |
"""simple docstring"""
from math import factorial
lowerCamelCase_ = {str(d): factorial(d) for d in range(10)}
def snake_case ( A__ ):
return sum(DIGIT_FACTORIAL[d] for d in str(A__ ) )
def snake_case ( ):
UpperCAmelCase_ : int = 7 * fac... | 253 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 344 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def lowerCamelCase_ ( _a : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = [
"""decoder.version""",
... | 345 | 0 |
'''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIV... | 135 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import co... | 135 | 1 |
from collections.abc import Generator
from math import sin
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
if len(__magic_name__ ) != 32:
raise ValueError("""Input must be of length 32""" )
UpperCamelCase :int =... | 38 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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.or... | 40 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : str ) ->Any:
'''simple docstring'''
__A : Any = 0
for ch in input_str:
__A : Any = ord(A__ )
__A : Optional[int] = pow(2 ,A__ ... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> float:
"""simple docstring"""
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(A__ ) * abs(A__ )
if __name__ == "__main__":
... | 28 |
import math
def A_ ( a , a = 0 , a = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = end or len(a )
for i in range(a , a ):
SCREAMING_SNAKE_CASE_ : List[Any] = i
SCREAMING_SNA... | 253 | 0 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowercase__ =logging.get_logger(__name__)
class UpperCam... | 90 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 90 | 1 |
from jiwer import compute_measures
import datasets
lowerCAmelCase : Tuple = '\\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: improved evaluation... | 253 |
import math
def A_ ( a , a = 0 , a = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = end or len(a )
for i in range(a , a ):
SCREAMING_SNAKE_CASE_ : List[Any] = i
SCREAMING_SNA... | 253 | 1 |
'''simple docstring'''
import random
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = a[left_index]
_UpperCAmelCase : Optional[int] = ... | 353 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 0 , lowerCAmelCase_ = 0 )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = right or len(lowerCAmelCase_ ) - 1
if left > right:... | 349 | 0 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta impor... | 135 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''camembert-base''': '''https://huggingface.co/camembert-... | 135 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'''junnyu/roformer_chinese_small''': '''https://huggingfac... | 216 |
import math
import sys
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE_ ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the valu... | 216 | 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_utils import requ... | 82 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ ) -> bool:
if len(snake_case__ ) == 0:
return False
lowerCamelCase = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a... | 291 | 0 |
def __lowercase ( a__ ) -> int:
__SCREAMING_SNAKE_CASE = [[0 for _ in range(a__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__SCREAMING_SNAKE_CASE = 1
for n in range(m + 1 ):
for k in range(1 ... | 118 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = ['''speech''']
def __init__( self , *_A , **_A ):
'''sim... | 118 | 1 |
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()
... | 90 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''onnx''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) ... | 90 | 1 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils impor... | 293 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .model... | 293 | 1 |
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase_ ):
de... | 155 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from un... | 349 | 0 |
"""simple docstring"""
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _a ( _snake_case , _snake_case=0 ):
"""simple docstring"""
return sorted(_sn... | 234 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" ... | 234 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ ={'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_torch_available():
raise... | 216 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase__ ={
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
'U': 2.76,
'M': 2.41,
... | 216 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
fr... | 303 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class snake_case ( unittest.TestCase ... | 303 | 1 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
imp... | 118 | 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 lowerCamelCase (SCREAMING_SNAKE_CASE__ ,... | 118 | 1 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: List[str] ,__UpperCamelCase: Union[str, Any] ,__UpperCamelCase: Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : ... | 369 |
'''simple docstring'''
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_sentencepi... | 246 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_to... | 293 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_op... | 293 | 1 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTe... | 231 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 231 | 1 |
'''simple docstring'''
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,
PixaStru... | 234 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available(... | 234 | 1 |
from __future__ import annotations
lowerCamelCase_ = list[list[int]]
# assigning initial values to the grid
lowerCamelCase_ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, ... | 178 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase_ = '''src/transformers'''
# This is to make sure the transformers module imported is t... | 178 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if ... | 303 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 303 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
snake_case : Optional[Any] = logging.getLogger(__name__)
class snake_case_ (A_ ):
UpperCAmelCase__ : int = "masked_bert"
def __init__( self :str ,__snake_case ... | 360 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
snake... | 109 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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,... | 252 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class _UpperCAme... | 246 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...te... | 313 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = 0
if start < ... | 313 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"... | 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 231 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CA... | 93 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_availa... | 93 | 1 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArgu... | 178 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowercase = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_av... | 178 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _a :
'''simple docstring'''
def __init__( self, A, A, A = 0 ):
'''simple docstring'''
... | 356 |
'''simple docstring'''
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... | 246 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
class A ( _UpperCAmelCase ):
"""simple docstring"""... | 7 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _snake_case ( UpperCamelCase : Callable , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float ):
UpperCAmelCase : Any... | 109 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgP... | 361 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxMode... | 313 |
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 diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_token... | 362 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
_UpperCAmelCase = set()
# Replace all the whitespace in our sentence
_UpperCAmelCase ... | 156 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowercase : Union[str, Any] = "Usage of script: script_name <size_of_canvas:int>"
_lowercase : Union[str, Any] = ... | 93 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCAmelCase__ ( unittest.TestCase ):
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : List[str] ... | 93 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase : Optional[Any] = {"""configuration_vit""": ["""... | 91 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase__:
def __init__( self : Any , lowerCAmelCase : int , lowerCAmelCase : int , lowerCAmelCase : float = 0 )-> None:
"""simple docstring""... | 91 | 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 ConfigTeste... | 53 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( _lowerCAmelCase : List[str] ) -> Any:
_UpperCAmelCase : List[str] = [
"""encoder.version""",
... | 246 | 0 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_UpperCamelCase: str = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9... | 360 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/license... | 53 | 0 |
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