code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 81 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__A = end or len(a_ )
for i in range(a_ , a_ ):
__A = i
__A = array[i]
while temp_index != start and temp_index_value < array[temp_inde... | 15 | 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_squeezebert import SqueezeBertTokenizer
A__ = logging.get_logger(__name__)
A__ = {"""voc... | 82 |
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, NewType, Optiona... | 15 | 0 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ ):
if num < 0:
return False
_UpperCamelCase : int = num
_UpperCamelCase : int = 0
while num > 0:
_UpperCamelCase : str = rev_num * 1_0 + (num % 1_0)
num //= 1_0... | 83 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
... | 15 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _SCREAMING_SNAKE_CASE :
UpperCAmelCase_... | 84 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
... | 15 | 0 |
'''simple docstring'''
def UpperCamelCase_( snake_case : Optional[Any] ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
snake_case_ , snake_case_ = head.next, head
while fast and fas... | 85 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
... | 15 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A__ ( _lowerCamelCase , un... | 86 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.mod... | 15 | 0 |
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int):
return int((input_a, input_a).count(0) == 0)
def lowercase_ ( ):
assert and_gate(0 , 0) == 0
assert and_gate(0 , 1) == 0
assert and_gate(1 , 0) == 0
assert and_gate(1 , 1) == 1
... | 87 |
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
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SN... | 15 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization... | 88 |
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'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampl... | 15 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolv... | 89 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 15 | 0 |
import argparse
import gc
import json
import os
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 accelerat... | 90 |
# Copyright 2021 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-2.0
#
# Unless required by app... | 15 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ ... | 91 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
... | 15 | 0 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCamelCase__ = 1.054571817E-34 # unit of ℏ : J * s
UpperCamelCase__ = 3E8 # unit of c : m * s^-1
def _a ( SCREAMING_SNAK... | 92 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum... | 15 | 0 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
_lowercase : int = "naver-clova-ix/donut-base"
class lowerCAmelCase__ ( unittest.TestCase ):
def _snake_case ( self ):
"""simple docstrin... | 93 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 15 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __lowerCamelCase ( ):
"""simple docstring"""
print('''Making key files...''' )
make_key_files('''rsa''' , 1024 )
... | 94 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , ... | 15 | 0 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
... | 95 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return lst
__A = 1
while i < len(a_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__A , __A = lst[i], lst[i - 1]
i -= ... | 15 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ = 100 , ):
_lowerCamelCase : Any = x... | 96 |
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
@dataclass
class UpperCAmelCase ( _... | 15 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_availabl... | 97 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE... | 15 | 0 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
... | 98 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 15 | 0 |
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 A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self ... | 99 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes... | 15 | 0 |
"""simple docstring"""
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 imp... | 100 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 15 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class lowercase :
def __init__( self):
lowercase = []
lowercase = 0
lowercase = 0
def A__ ( self):
return s... | 101 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
... | 15 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@may... | 102 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWith... | 15 | 0 |
def UpperCamelCase( __UpperCamelCase : str ):
return " ".join(
''''''.join(word[::-1] ) if len(__UpperCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
... | 103 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__A = end or len(a_ )
for i in range(a_ , a_ ):
__A = i
__A = array[i]
while temp_index != start and temp_index_value < array[temp_inde... | 15 | 0 |
'''simple docstring'''
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_CHEC... | 104 |
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, NewType, Optiona... | 15 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import T... | 105 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
... | 15 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : Optional[Any] = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
... | 106 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
... | 15 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__lowerCAmelCase : int = logging.get_logger(__name__)
# TODO: upload to AWS
__lowerCAmelCase : Union[str, Any] = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retr... | 107 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
... | 15 | 0 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acc... | 108 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.mod... | 15 | 0 |
"""simple docstring"""
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 SCREAMING_SNAKE... | 109 |
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
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SN... | 15 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer... | 110 |
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'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampl... | 15 | 0 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class a ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE : List[Any] = """M-CLIP"""
def __init__( self : ... | 183 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 15 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) ==... | 215 |
# Copyright 2021 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-2.0
#
# Unless required by app... | 15 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase__ ( ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = ArgumentParser(
description=(
... | 329 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
... | 15 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_UpperCAmelCase : O... | 174 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum... | 15 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ : Optional[int] = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHI... | 112 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 15 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 50 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , ... | 15 | 0 |
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, ... | 43 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return lst
__A = 1
while i < len(a_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__A , __A = lst[i], lst[i - 1]
i -= ... | 15 | 0 |
"""simple docstring"""
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = {} # Mapping from char to TrieNode
__lowerCAmelCase = False
def snake_case ( self , __a ):
for word in w... | 57 |
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
@dataclass
class UpperCAmelCase ( _... | 15 | 0 |
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[int] )->int:
'''simple docstring'''
if n == 1 or not isinstance(a_ , a_ ):
return 0
elif n == 2:
return 1
else:
snake_case_ = [0, 1]
for i in range(2 , n + 1 ... | 159 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE... | 15 | 0 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_UpperCAmelCase = 0
_UpperCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, ... | 140 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 15 | 0 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = {}
A_ : Union[str, Any] = job['st... | 286 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes... | 15 | 0 |
"""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... | 183 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 15 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : Tuple = {
'roberta-base': 'https://... | 215 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
... | 15 | 0 |
# 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 applic... | 329 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWith... | 15 | 0 |
'''simple docstring'''
from math import ceil
def __magic_name__( lowerCamelCase = 1_0_0_1):
__lowerCAmelCase = 1
for i in range(1, int(ceil(n / 2.0))):
__lowerCAmelCase = 2 * i + 1
__lowerCAmelCase = 2 * i
__lowerCA... | 174 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__A = end or len(a_ )
for i in range(a_ , a_ ):
__A = i
__A = array[i]
while temp_index != start and temp_index_value < array[temp_inde... | 15 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any , lower... | 112 |
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, NewType, Optiona... | 15 | 0 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
_enforce_args(a_ , a_ )
if n == 0:
return 0
lowerCamelCase__ : Union[str, Any] = float('-inf' )
for i in range(1 , n + 1 ):
lowerCamelCase__ : Tuple... | 50 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
... | 15 | 0 |
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 lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docst... | 43 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
... | 15 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
... | 57 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
... | 15 | 0 |
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
SCREAMING_SNAKE_CASE :int = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def _lowerCAmelCase ... | 159 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.mod... | 15 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_UpperCAmelCase = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
_UpperCAmelCase = None
def UpperCamelCase ( ):
'''simple docstring'''
A_ : ... | 140 |
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
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SN... | 15 | 0 |
"""simple docstring"""
import math
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : str = 0
A_ : int = 0
while num > 0:
A_ : Optional[int] = num % 8
A_ : Optional[Any] = octa... | 286 |
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'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampl... | 15 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_SCREAMING_SNAKE_CASE : Dict = True
except (ImportError, ModuleNotFoundError):
_SCREAMING_SNAKE_CASE : int = False
if NLTK_AVAILABLE:
with FileLock('... | 183 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 15 | 0 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
return 1 if input_a == input_a else 0
def snake_case_ ( )-> None:
'''simple docstring'''
assert xnor_gate(0 , 0 )... | 215 |
# Copyright 2021 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-2.0
#
# Unless required by app... | 15 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAm... | 329 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
... | 15 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
... | 174 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum... | 15 | 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 import AutoProcessor, ... | 112 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 15 | 0 |
class lowerCAmelCase :
def __init__( self : int , UpperCAmelCase : int ) -> Union[str, Any]:
lowerCamelCase__ : Any = n
lowerCamelCase__ : str = [None] * self.n
lowerCamelCase__ : List[str] = 0 # index of the first elemen... | 50 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , ... | 15 | 0 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCamelCase :Tuple = str(bin(a_ ) )[2:] # remove the leading "0b"
__UpperCamelCase :Op... | 43 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return lst
__A = 1
while i < len(a_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__A , __A = lst[i], lst[i - 1]
i -= ... | 15 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self ):
debug_launcher(... | 57 |
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
@dataclass
class UpperCAmelCase ( _... | 15 | 0 |
SCREAMING_SNAKE_CASE :int = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def _lowerCAmelCase ( )->None:
'''simple docstring'''
snake_case_ = input("Enter message: " )
snake_case_ = input("Enter key [alphanumeric]: " )
snake_case_ = inp... | 159 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE... | 15 | 0 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_UpperCAmelCase = ['small', 'medium', 'large']
_UpperCAmelCase = 'lm_head.decoder.weight'
_UpperCAmelCase = 'lm_head.weight'
def UpperCamelCase ( __lowercase : int ,__lo... | 140 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 15 | 0 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should... | 286 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes... | 15 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils... | 183 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 15 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_t... | 215 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
... | 15 | 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_distilbert': [
'DISTILBERT_PRETRAINED_C... | 329 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWith... | 15 | 0 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase):
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''')
return 0.5 * mass * abs(a_) * abs(a_)
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True... | 174 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__A = end or len(a_ )
for i in range(a_ , a_ ):
__A = i
__A = array[i]
while temp_index != start and temp_index_value < array[temp_inde... | 15 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperC... | 112 |
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, NewType, Optiona... | 15 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : Any = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class... | 50 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
... | 15 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_available():
raise Optional... | 43 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
... | 15 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(a_ , a_ ):
__lowerCAmelCase = f"Input value of [number={number}] must be an integer"
raise TypeError(a_ )
if number < 0:
return False
__lowerCAmelC... | 57 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
... | 15 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
SCREAMING_SNAKE_CASE :int = logging.getLogger(__name__)
class __lowerCAmelCase :
"""simple docst... | 159 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.mod... | 15 | 0 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def UpperCamelCase ( __lowercase : Dict ):
'''simple docstring'''
return getitem, k
def UpperCamelCase ( __lowercase : str ... | 140 |
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
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SN... | 15 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCamelCase_ : List[str] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball}... | 286 |
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'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampl... | 15 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_... | 183 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 15 | 0 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pi... | 215 |
# Copyright 2021 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-2.0
#
# Unless required by app... | 15 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCAmelCase__ :Tuple = {
'n_samples': 6_4,
'horizon': 3_2,
'num_inference_steps': 2_0,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_gr... | 329 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
... | 15 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = 1_0_0, ):
__lowerCAmelCase = x_start
__lowerCAmelCase = fnc(a_)
... | 174 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum... | 15 | 0 |
'''simple docstring'''
UpperCamelCase__ : Tuple = {str(digit): digit**5 for digit in range(10)}
def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def lowerCAmelCase_ ( ):
return sum(
number
... | 112 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 15 | 0 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : Dict = get_tests_dir("""fixtures/spi... | 50 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , ... | 15 | 0 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :str = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
__UpperCamelCase :List[str] = parser.add_... | 43 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return lst
__A = 1
while i < len(a_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__A , __A = lst[i], lst[i - 1]
i -= ... | 15 | 0 |
"""simple docstring"""
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 _UpperCam... | 57 |
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
@dataclass
class UpperCAmelCase ( _... | 15 | 0 |
from __future__ import annotations
from math import pi
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[int] , lowerCAmelCase_ :List[str] , lowerCAmelCase_ :str )->dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance... | 159 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE... | 15 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def UpperCamelCase ( __lowercase : int ):
'''simple docstring'''
A_ : List[str] = int(number**0.5 )
return number == sq * sq
def UpperCamelCase ( ... | 140 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 15 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 286 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes... | 15 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProces... | 183 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 15 | 0 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, 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 ... | 215 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
... | 15 | 0 |
lowerCAmelCase__ :Union[str, Any] = 0 # The first color of the flag.
lowerCAmelCase__ :Optional[int] = 1 # The second color of the flag.
lowerCAmelCase__ :str = 2 # The third color of the flag.
lowerCAmelCase__ :List[Any] = (red, white, blue)
def lowerCAmelC... | 329 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWith... | 15 | 0 |
'''simple docstring'''
_UpperCAmelCase : int = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_UpperCAmelCase : Optional[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmel... | 174 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__A = end or len(a_ )
for i in range(a_ , a_ ):
__A = i
__A = array[i]
while temp_index != start and temp_index_value < array[temp_inde... | 15 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : Dict = {}
__SCREAMING_SNAKE_CASE : List[str] = 2
while True:
__SCREAMING_SNAKE_CASE : Optional[int] ... | 112 |
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, NewType, Optiona... | 15 | 0 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> list:
if len(a_ ) <= 1:
return lst
lowerCamelCase__ : Dict = 1
while i < len(a_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
lowerCamelCase__ , lowerCamelCase__ ... | 50 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
... | 15 | 0 |
from collections.abc import Callable
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase = None) -> Union[str, Any]:
# Stores actual heap items.
__UpperCamelCase :List[Any] = []
# Stores indexes of each item for supporting upd... | 43 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
... | 15 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.ut... | 57 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
... | 15 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 159 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.mod... | 15 | 0 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_UpperCAmelCase = logging.getLogger(__name__)
@datacla... | 140 |
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
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SN... | 15 | 0 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from tra... | 286 |
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'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampl... | 15 | 0 |
"""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 a ( __SCREAMING_SNAKE_CASE ... | 183 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 15 | 0 |
'''simple docstring'''
A_ : Any = 2_5_6
# Modulus to hash a string
A_ : Union[str, Any] = 1_0_0_0_0_0_3
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> bool:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = l... | 215 |
# Copyright 2021 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-2.0
#
# Unless required by app... | 15 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase__ :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or m... | 329 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
... | 15 | 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
@dataclas... | 174 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum... | 15 | 0 |
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