




# 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,
)


UpperCamelCase__						=		{
    '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:
							UpperCamelCase__						=		[
							    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TvltModel',
							    'TvltForPreTraining',
							    'TvltForAudioVisualClassification',
							    'TvltPreTrainedModel',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['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

							UpperCamelCase__						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






UpperCamelCase__						=		[
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]






def    snake_case   (				A		,  A		,  A		,  A     )				->				bool:




	'''simple docstring'''


	return 0 <= row < rows and 0 <= col < cols






def    snake_case   (				A		,  A		,  A		,  A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				len(A     )
	lowerCamelCase_				:						Any				  =				len(mat[0]     )
	if is_safe(A		,  A		,  A		,  A     ) and (row, col) not in seen and mat[row][col] == 1:
		seen.add((row, col)     )
		return (
		    1
		    + depth_first_search(row + 1		,  A		,  A		,  A     )
		    + depth_first_search(row - 1		,  A		,  A		,  A     )
		    + depth_first_search(A		,  col + 1		,  A		,  A     )
		    + depth_first_search(A		,  col - 1		,  A		,  A     )
		)
	else:
		return 0






def    snake_case   (				A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						set				  =				set()

	lowerCamelCase_				:						Optional[Any]				  =				0
	for row, line in enumerate(A     ):
		for col, item in enumerate(A     ):
			if item == 1 and (row, col) not in seen:
				# Maximizing the area
				lowerCamelCase_				:						Optional[Any]				  =				max(A		,  depth_first_search(A		,  A		,  A		,  A     )     )
	return max_area


if __name__ == "__main__":
							import doctest

							print(find_max_area(matrix))  # Output -> 6
							doctest.testmod()
