



'''simple docstring'''



# 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 applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_     :       Any								=						{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						['VivitImageProcessor']


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Tuple								=						[
							    'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'VivitModel',
							    'VivitPreTrainedModel',
							    'VivitForVideoClassification',
							]


if TYPE_CHECKING:
							from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vivit import VivitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vivit import (
														    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    VivitForVideoClassification,
														    VivitModel,
														    VivitPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_     :       int								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''



import string
from math import logaa
def 					UpperCAmelCase   (						A  :				str					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			document.translate(
							    str.maketrans(''''''					,		''''''					,		string.punctuation				)				).replace('''\n'''					,		''''''				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			document_without_punctuation.split(''' '''				)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]				)
def 					UpperCAmelCase   (						A  :				str					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      int     					=			corpus.lower().translate(
							    str.maketrans(''''''					,		''''''					,		string.punctuation				)				)  # strip all punctuation and replace it with ''
							SCREAMING_SNAKE_CASE					:      Any     					=			corpus_without_punctuation.split('''\n'''				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			term.lower()
							return (len([doc for doc in docs if term in doc]				), len(A				))
def 					UpperCAmelCase   (						A  :				int					,		A  :				int					,		A  :				str=False				):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''				)
														return round(1 + logaa(n / (1 + df)				)					,		3				)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''				)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''				)
							return round(logaa(n / df				)					,		3				)






def 					UpperCAmelCase   (						A  :				int					,		A  :				int				):
							return round(tf * idf					,		3				)




