import functools





def 		__lowerCamelCase							(UpperCAmelCase__       :							list[int] ,	UpperCAmelCase__       :							list[int]					):

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

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

					if len(UpperCAmelCase__					) == 0:
										return 0

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

					if max(UpperCAmelCase__					) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"					)

					SCREAMING_SNAKE_CASE           =    set(UpperCAmelCase__					)

					@functools.cache
					def dynamic_programming(UpperCAmelCase__       :							int					) -> int:
										if index > 3_6_5:
															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 + 3_0					) ,	)

					return dynamic_programming(1					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





# 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.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
							from PIL import Image


class   lowercase      (       a       ):
							lowercase__  :    Optional[Any]									=							(
							    """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
							    """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
							    """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
							)
							lowercase__  :    Optional[int]									=							"""CIDAS/clipseg-rd64-refined"""
							lowercase__  :    Tuple									=							"""image_segmenter"""
							lowercase__  :    Optional[Any]									=							CLIPSegForImageSegmentation

							lowercase__  :    int									=							["""image""", """text"""]
							lowercase__  :    List[str]									=							["""image"""]




							def __init__(  self				:   str		,							*_UpperCamelCase				:   str		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												requires_backends(self		,							["vision"] )
												super().__init__(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   "Image"		,							_UpperCamelCase				:   str )   ->			Optional[int]:



												'''simple docstring'''

												return self.pre_processor(text=[label]		,							images=[image]		,							padding=_UpperCamelCase		,							return_tensors="pt" )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    self.model(**_UpperCamelCase ).logits
												return logits





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    outputs.cpu().detach().numpy()
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    1
												return Image.fromarray((array * 255).astype(np.uinta ) )




