from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

lowercase__     :							str            =      logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE__		)
class   lowercase_    (    SCREAMING_SNAKE_CASE__		):



	"""simple docstring"""





	def __init__(				self						,     *__SCREAMING_SNAKE_CASE						,     **__SCREAMING_SNAKE_CASE							)		->Dict:
								super().__init__(*snake_case__						,     **snake_case__							)
								requires_backends(self						,     '''vision'''							)
								self.check_model_type(snake_case__							)


	def __call__(				self						,     __SCREAMING_SNAKE_CASE						,     **__SCREAMING_SNAKE_CASE							)		->Optional[Any]:
								return super().__call__(snake_case__						,     **snake_case__							)


	def      SCREAMING_SNAKE_CASE_							(				self						,     **__SCREAMING_SNAKE_CASE							)		->Dict:
								return {}, {}, {}


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->Optional[Any]:
								lowerCAmelCase      						=						load_image(snake_case__							)
								lowerCAmelCase      						=						image.size
								lowerCAmelCase      						=						self.image_processor(images=snake_case__						,     return_tensors=self.framework							)
								return model_inputs


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->Any:
								lowerCAmelCase      						=						self.model(**snake_case__							)
								return model_outputs




	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE							)		->Any:
								lowerCAmelCase      						=						model_outputs.predicted_depth
								lowerCAmelCase      						=						torch.nn.functional.interpolate(
								    predicted_depth.unsqueeze(1							)						,     size=self.image_size[::-1]						,     mode='''bicubic'''						,     align_corners=snake_case__							)
								lowerCAmelCase      						=						prediction.squeeze().cpu().numpy()
								lowerCAmelCase      						=						(output * 255 / np.max(snake_case__							)).astype('''uint8'''							)
								lowerCAmelCase      						=						Image.fromarray(snake_case__							)
								lowerCAmelCase      						=						{}
								lowerCAmelCase      						=						predicted_depth
								lowerCAmelCase      						=						depth
								return output_dict









import functools


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# Validation
		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()