

"""simple docstring"""




# Copyright 2023 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 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 ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
						from PIL import Image





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		List[str]       		=				'Salesforce/blip-image-captioning-base'
			lowerCamelCase__ :		Dict       		=				(
			    'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
			    'image to caption, and returns a text that contains the description in English.'
			)
			lowerCamelCase__ :		Optional[Any]       		=				'image_captioner'
			lowerCamelCase__ :		List[Any]       		=				AutoModelForVisionaSeq

			lowerCamelCase__ :		int       		=				['image']
			lowerCamelCase__ :		Union[str, Any]       		=				['text']

			def __init__(       self					:    str     ,					*__UpperCAmelCase					:    Optional[Any]     ,					**__UpperCAmelCase					:    Tuple							) ->     List[str]:
						requires_backends(self     ,					["""vision"""]							)
						super().__init__(*__UpperCAmelCase     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    "Image"							) ->     Optional[Any]:
						return self.pre_processor(images=__UpperCAmelCase     ,					return_tensors="""pt"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    Optional[int]							) ->     Dict:
						return self.model.generate(**__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    Dict							) ->     List[str]:
						return self.pre_processor.batch_decode(__UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase							)[0].strip()





"""simple docstring"""




from __future__ import annotations

def 						A							(  snake_case__     ):




			'''simple docstring'''


			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()


