




from __future__ import annotations





def 				A__ (snake_case   :		list[list[int]]				)     ->						int:

					# preprocessing the first row
					for i in range(1		,	len(matrix[0]				)				):
										matrix[0][i] += matrix[0][i - 1]

					# preprocessing the first column
					for i in range(1		,	len(snake_case				)				):
										matrix[i][0] += matrix[i - 1][0]

					# updating the path cost for current position
					for i in range(1		,	len(snake_case				)				):
										for j in range(1		,	len(matrix[0]				)				):
															matrix[i][j] += min(matrix[i - 1][j]		,	matrix[i][j - 1]				)

					return matrix[-1][-1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()












# 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


a__		     =     {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     ['''EfficientNetImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__		     =     [
	    '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''EfficientNetForImageClassification''',
	    '''EfficientNetModel''',
	    '''EfficientNetPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_efficientnet import (
	    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    EfficientNetConfig,
	    EfficientNetOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_efficientnet import EfficientNetImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_efficientnet import (
		    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EfficientNetForImageClassification,
		    EfficientNetModel,
		    EfficientNetPreTrainedModel,
		)

else:
	import sys

	a__		     =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)






