


"""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

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
		from PIL import Image




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						int					      =    "dandelin/vilt-b32-finetuned-vqa"
	snake_case__       :						str					      =    (
	    "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
	    "image containing the information, as well as a `question` which should be the question in English. It "
	    "returns a text that is the answer to the question."
	)
	snake_case__       :						Dict					      =    "image_qa"
	snake_case__       :						Optional[int]					      =    AutoProcessor
	snake_case__       :						Union[str, Any]					      =    AutoModelForVisualQuestionAnswering

	snake_case__       :						Dict					      =    ["image", "text"]
	snake_case__       :						Union[str, Any]					      =    ["text"]




	def __init__(				self    :       Any    ,    *UpperCAmelCase__    :       Union[str, Any]    ,    **UpperCAmelCase__    :       Any     ) ->			int:
						requires_backends(self    ,    ["vision"]     )
						super().__init__(*UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       "Image"    ,    UpperCAmelCase__    :       str     ) ->			Optional[Any]:
						return self.pre_processor(UpperCAmelCase__    ,    UpperCAmelCase__    ,    return_tensors="pt"     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Tuple     ) ->			Optional[Any]:
						with torch.no_grad():
											return self.model(**UpperCAmelCase__     ).logits




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       Optional[Any]     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       outputs.argmax(-1     ).item()
						return self.model.config.idalabel[idx]




"""simple docstring"""







import functools




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					if not isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for day in days     ):
										raise ValueError("The parameter days should be a list of integers"     )

					if len(lowerCAmelCase_     ) != 3 or not all(isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) for cost in costs     ):
										raise ValueError("The parameter costs should be a list of three integers"     )

					if len(lowerCAmelCase_     ) == 0:
										return 0

					if min(lowerCAmelCase_     ) <= 0:
										raise ValueError("All days elements should be greater than 0"     )

					if max(lowerCAmelCase_     ) >= 366:
										raise ValueError("All days elements should be less than 366"     )

					__SCREAMING_SNAKE_CASE		     =       set(lowerCAmelCase_     )

					@functools.cache
					def dynamic_programming(lowerCAmelCase_     ) -> int:
										if index > 365:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1     )

										return min(
										    costs[0] + dynamic_programming(index + 1     )						,		costs[1] + dynamic_programming(index + 7     )						,		costs[2] + dynamic_programming(index + 30     )						,		)

					return dynamic_programming(1     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
