
# 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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


A		:   Any         =    {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Union[str, Any]         =    [
		    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TvltModel''',
		    '''TvltForPreTraining''',
		    '''TvltForAudioVisualClassification''',
		    '''TvltPreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Union[str, Any]         =    ['''TvltImageProcessor''']


if TYPE_CHECKING:
		from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
		from .processing_tvlt import TvltProcessor
		from .feature_extraction_tvlt import TvltFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tvlt import (
				    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TvltForAudioVisualClassification,
				    TvltForPreTraining,
				    TvltModel,
				    TvltPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_tvlt import TvltImageProcessor


else:
		import sys

		A		:   str         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

from math import sqrt




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    list[int]:
						_lowercase									=				[1]
						for i in range(2		,      int(sqrt(SCREAMING_SNAKE_CASE_					)					) + 1		,      1					):
												if number % i == 0:
																		values.append(SCREAMING_SNAKE_CASE_					)
																		if int(number // i					) != i:
																								values.append(int(number // i					)					)
						return sorted(SCREAMING_SNAKE_CASE_					)




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    bool:
						return sum(factors(SCREAMING_SNAKE_CASE_					)					) > n




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    bool:
						_lowercase									=				factors(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				len(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				[[0 for i in range(number + 1					)] for j in range(r + 1					)]
						for i in range(r + 1					):
												_lowercase									=				True

						for i in range(1		,      number + 1					):
												_lowercase									=				False

						for i in range(1		,      r + 1					):
												for j in range(1		,      number + 1					):
																		if j < values[i - 1]:
																								_lowercase									=				subset[i - 1][j]
																		else:
																								_lowercase									=				subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

						return subset[r][number] != 0




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int					)     ->    bool:
						return abundant(SCREAMING_SNAKE_CASE_					) and not semi_perfect(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		for number in (69, 70, 71):
				print(F'{number} is {"" if weird(number) else "not "}weird.')