code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
class lowercase_ :
def __init__( self) -> str:
a__ =0
a__ =0
a__ ={}
def __UpperCamelCase ( self , lowercase_) -> Dict:
if vertex not in self.adjacency:
a__ ={}
self... | 20 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr... | 21 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_... | 22 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 0 |
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _snake_case (__lowercase , __lowercase , __lowercase):
UpperCamelCase_ ... | 23 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 0 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def _UpperCamelCase (_lowerCamelCase : Union[str, Any] )-> Dict:
'''simple do... | 24 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 0 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : List[Any] = torch.load(_a ... | 25 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 0 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common im... | 26 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 0 |
__A : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
_A = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
... | 27 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 0 |
'''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/li... | 28 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
lowerCamelCase_ = str(bin(lowerCAmelCase__ ) )
binary_number += "0" * shift_amount
return bi... | 29 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 0 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Any = Stabl... | 30 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 0 |
import numpy as np
def UpperCAmelCase_ ( __UpperCAmelCase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 31 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDC... | 32 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCamelCase__ : Any = """\
"""
lowerCamelCase__ : List[str] = """
Perpl... | 33 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 0 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = [0 for i in range(len(_lowercase ) )]
# initialize interval's left pointer and right pointer
UpperCamelCase , UpperCamelCase = 0, 0
... | 34 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 54 | 0 |
from __future__ import annotations
from typing import Any
class lowercase :
def __init__( self : int , _lowercase : int ):
SCREAMING_SNAKE_CASE__ : List[str] = num_of_nodes
SCREAMING_SNAKE_CASE__ : list[list[int]] ... | 35 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 36 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Optional[Any] = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextCo... | 37 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : int ) -> bool:
'''simple docstring'''
if not isinstance(__magic_name__ , __magic_name__ ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
snake_case__ : List[s... | 38 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 54 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __SCREAMING_SNAKE... | 39 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) == 0:
return False
UpperCAmelCase_ =len(lowercase__ ) // 2
if a_list[midpoint] == item:
return True
... | 54 | 0 |
from __future__ import annotations
import queue
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : Optional[Any] = data
UpperCamelCase : Any = None
UpperC... | 40 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""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 ... | 54 | 0 |
'''simple docstring'''
from typing import Any
import numpy as np
def _A ( A__ ):
"""simple docstring"""
return np.array_equal(A__ , matrix.conjugate().T )
def _A ( A__ , A__ ):
"""simple docstring"""
__lowercase = v.conjugate().T
__lower... | 41 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase = 1_00_00_00 ) -> int:
lowerCamelCase_ = 1
lowerCamelCase_ = 1
lowerCamelCase_ = {1: 1}
for inputa in range(2 ,__UpperCamelCase ):
lowerCamelCase_ = 0
lower... | 42 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 43 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 0 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
if len(_lowerCAmelCase ) < 2:
return collection
def circle_sort_util(_lowerCAmelCase : list , _lowerCAmelCase : int , _lowerCAmelCase : int ... | 44 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 45 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 0 |
"""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... | 46 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 0 |
def UpperCAmelCase__ ( lowerCamelCase_ : int = 1_0 ):
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or n < 0:
raise ValueError('Invalid input' )
__a : Optional[Any] = 1_0**n
__a : Union[str, Any] = 2_8_4_3_... | 47 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 0 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedI... | 48 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 0 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format,... | 49 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 0 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int ):
lowerCamelCase__ = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return tot... | 50 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 51 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 0 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def __A ( a_ :int , a_ :Dict , a_ :str , a_ :Optional[int]=None) -> List[str]:
__a : Any = (path or []) + [u]
for v in graph[u]:
if visited_e... | 52 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_lo... | 53 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCAmelCase ( __SCR... | 55 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...... | 56 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
A_ : Union[str, Any] = 'Usage of script: script_name <size_of_canvas:int>'
A_ : str = [0] * 100 + [1] * 10
random.shu... | 57 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 54 | 0 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
__lowerCAmelCase : List[str] = '''__DUMMY_TRANSFORMERS_USER__'''
__lowerCAmelCase ... | 58 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 0 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from... | 59 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 0 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
... | 60 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 0 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase ... | 61 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 54 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""tokenization_biogpt""": ["""BioGptTok... | 62 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) == 0:
return False
UpperCAmelCase_ =len(lowercase__ ) // 2
if a_list[midpoint] == item:
return True
... | 54 | 0 |
import math
import sys
import cva
import numpy as np
def lowerCamelCase__ ( __lowerCamelCase : np.ndarray , __lowerCamelCase : float ):
# For applying gaussian function for each element in matrix.
__UpperCAmelCase : int = math.sqrt(__lo... | 63 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""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 ... | 54 | 0 |
def A__ ( snake_case_ : int ):
return str(snake_case_ ) == str(snake_case_ )[::-1]
def A__ ( snake_case_ : int ):
return int(snake_case_ ) + int(str(snake_case_ )[::-1] )
def A__ ( snake_case_ : int = 10_000 ):
SCREAMING_SNAKE_CASE__: Dict... | 64 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 0 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = """"""
for word_or_phrase in separated:
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
rai... | 65 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 0 |
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> int:
assert column_title.isupper()
_lowercase : Optional[Any] = 0
_lowercase : Optional[Any] = len(SCREAMING_SNAKE_CASE ) - 1
_lowercase : Optional[int] = 0
whi... | 66 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 0 |
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 ... | 67 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 0 |
from ...configuration_utils import PretrainedConfig
__A = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas-base-finetuned-wtq/reso... | 68 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIG... | 69 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 0 |
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.testin... | 70 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
class _snake_case (__SCREAMING_SNAKE_CASE):
__A : Optional[int] ="timm_backbone"
def __init__( self ,_snak... | 71 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 0 |
'''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 six # noqa: F401... | 72 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class _snake_case :
def __init__(... | 73 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 0 |
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = [0 for i in range(len(snake_case ) )]
# initialize interval's left pointer and right pointer
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Optional[int] = 0, 0
... | 74 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 0 |
'''simple docstring'''
from itertools import product
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> list[int]:
UpperCAmelCase__ : Optional[Any] = sides_number
UpperCAmelCase__ : Optional[Any] = max_face_number * dice_number
... | 75 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 0 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : Union[str, Any] = list(range(len(__UpperCamelCase ) ) )
__lowercase : List[Any] = ... | 76 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 0 |
"""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_sentencepiece_available, ... | 77 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> Union[str, Any]:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(snake_case_ , int(b / 2 ) ) *... | 78 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 0 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoMod... | 79 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 54 | 0 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import C... | 80 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 0 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_snake_case : Tuple = 4
_snake_case : Tuple = 3
class a (_lowerCAme... | 81 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 0 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowerCamelCase = get_logger(__name__)
lowerCamelCase = r"""
Args:
input_ids (`jnp.nda... | 82 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
def snake_case_ ( A_ : str, A_ : str, A_ : int ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = Path(A_ )
_lowerCamelCase : int ... | 83 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 54 | 0 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Traini... | 84 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) == 0:
return False
UpperCAmelCase_ =len(lowercase__ ) // 2
if a_list[midpoint] == item:
return True
... | 54 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if... | 85 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""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 ... | 54 | 0 |
__a :Tuple = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
... | 86 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"""configuration_whisper""": ["""WHISPER_PRETRAINED_CON... | 87 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
... | 88 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 0 |
SCREAMING_SNAKE_CASE : Tuple = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_b... | 89 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 0 |
'''simple docstring'''
def _snake_case ( A , A ) -> int:
lowerCAmelCase__ = [0 for i in range(r + 1 )]
# nc0 = 1
lowerCAmelCase__ = 1
for i in range(1 , n + 1 ):
# to compute current row from previou... | 90 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 0 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
def __init__( self : ... | 91 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 0 |
'''simple docstring'''
import math
import sys
def _lowerCAmelCase ( __magic_name__ : str ) -> str:
lowercase : Tuple =''''''
try:
with open(__magic_name__ , '''rb''' ) as binary_file:
lowercase : Dict =binary_f... | 92 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 0 |
"""simple docstring"""
from PIL import Image
def __A (_SCREAMING_SNAKE_CASE ) ->Image:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ :Dict = image.size
lowerCAmelCase__ :Dict = 0
lowerCAmelCase__ :Tuple = imag... | 93 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 0 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_c... | 94 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 0 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" ,[
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.j... | 95 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE_ )
class __A ( SCREAMING_SNAKE_CASE_ ):
... | 96 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not is_torch_available():
... | 97 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 0 |
'''simple docstring'''
import math
def a__ ( lowercase : int = 100 ) -> int:
"""simple docstring"""
_UpperCamelCase = sum(i * i for i in range(1, n + 1 ) )
_UpperCamelCase = int(math.pow(sum(range(1, n + 1 ) ), 2 ) )
... | 98 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 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_pro... | 99 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 0 |
# Function to print upper half of diamond (pyramid)
def __snake_case ( lowerCAmelCase_ ) -> List[Any]:
for i in range(0 , lowerCAmelCase_ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
for _ in range(0 ... | 100 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase__ : List[str] =3_00 # TEMPERATURE (unit = K)
def a__ ( A__, A__, A__, ):
if donor_conc <= 0:
raise ValueError('Donor concentration should be positi... | 101 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 54 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=() ... | 102 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
snake_case = lo... | 103 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None ) -> Optional[Any]:
# Input as... | 104 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 0 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 54 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case :Tuple ={
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', '... | 106 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) == 0:
return False
UpperCAmelCase_ =len(lowercase__ ) // 2
if a_list[midpoint] == item:
return True
... | 54 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : int = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-v... | 107 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase : Any =(
"""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 ... | 54 | 0 |
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 i... | 108 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 54 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__SCREAMING_SNAKE_CASE = [True] * (num + 1)
__SCREAMING_SNAKE_CASE = ... | 109 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fla... | 110 |
def a__ ( lowercase__ = 2_0_0 ):
'''simple docstring'''
UpperCAmelCase_ =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
UpperCAmelCase_ =[0] * (pence + 1)
UpperCAmelCase_ =1 # base case: 1 way to make 0 pence
for coin in coins... | 54 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"""kakaobrain/align-base""": ""... | 322 |
import sys
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
UpperCAmelCase_ =[[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ =[[0 for x in range(lower... | 54 | 0 |
"""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
from... | 353 |
from math import loga
def a__ ( lowercase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(lowercase__ , lowercase__ ):
raise TypeError("Input value must be a 'int' ty... | 54 | 0 |
def lowercase__ ( A_: Dict = 200 ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase =[1, 2, 5, 10, 20, 50, 100, 200]
__UpperCAmelCase =[0] * (pence + 1)
__UpperCAmelCase =1 # base case: 1 way to make 0 pence
f... | 68 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : Union[str, Any] =logging.get_logger(__name__)
def a__ ( lowercase__ ):
... | 54 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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 ..... | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
__lowercase : str ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.... | 54 | 0 |
import re
from filelock import FileLock
try:
import nltk
A_ = True
except (ImportError, ModuleNotFoundError):
A_ = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __UpperCAmelCase ... | 604 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =int(lowercase__ )
if n_element < 1:
UpperCAmelCase_ =ValueError("a should be a positive number" )
raise my_error
UpperCAmelCase_ =[1]
UpperC... | 54 | 0 |
"""simple docstring"""
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_sin... | 624 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowercase : List[Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: List[Any] , *_lowerCAmelCase: Optional[Any] , **_l... | 54 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers i... | 359 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property... | 54 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
A_ : Tuple = """examples/"""
A_ : Optional[Any] = {
"""examples""": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), """check_min_version(\"VERSION\... | 196 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase : Optional[int] ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 54 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
... | 259 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 | 0 |
class __snake_case :
def __init__( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
_lowerCAmelCase : str = {}
def SCREAMING_SNAKE_CASE ( self : str ) -> None:
'''simple docstring'''
... | 429 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 54 | 0 |
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