





'''simple docstring'''






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

A   : Union[str, Any]             =			logging.get_logger(__name__)







@add_end_docstrings(__UpperCAmelCase  )
class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def __init__(					self	:  Optional[Any]       ,     *__snake_case	:  List[Any]       ,     **__snake_case	:  int       ):





											'''simple docstring'''





											super().__init__(*__snake_case       ,     **__snake_case       )
											requires_backends(self       ,     'vision'       )
											self.check_model_type(__snake_case       )


					def __call__(					self	:  Optional[Any]       ,     __snake_case	:  Union[str, List[str], "Image.Image", List["Image.Image"]]       ,     **__snake_case	:  Optional[Any]       ):





											'''simple docstring'''





											return super().__call__(__snake_case       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											return {}, {}, {}


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   int  					=		load_image(__snake_case       )
											_snake_case:   Any  					=		image.size
											_snake_case:   Optional[Any]  					=		self.image_processor(images=__snake_case       ,     return_tensors=self.framework       )
											return model_inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.model(**__snake_case       )
											return model_outputs




					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		model_outputs.predicted_depth
											_snake_case:   str  					=		torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1       )       ,     size=self.image_size[::-1]       ,     mode='bicubic'       ,     align_corners=__snake_case       )
											_snake_case:   Optional[Any]  					=		prediction.squeeze().cpu().numpy()
											_snake_case:   Optional[Any]  					=		(output * 2_55 / np.max(__snake_case       )).astype('uint8'       )
											_snake_case:   Optional[Any]  					=		Image.fromarray(__snake_case       )
											_snake_case:   Any  					=		{}
											_snake_case:   Dict  					=		predicted_depth
											_snake_case:   Any  					=		depth
											return output_dict












'''simple docstring'''






def       lowercase_      (		lowercase__ = 50						)   ->int:
						_snake_case:   Union[str, Any]  					=		[[0] * 3 for _ in range(length + 1						)]

						for row_length in range(length + 1						):
												for tile_length in range(2				,  5						):
																		for tile_start in range(row_length - tile_length + 1						):
																								different_colour_ways_number[row_length][tile_length - 2] += (
																								    different_colour_ways_number[row_length - tile_start - tile_length][
																								        tile_length - 2
																								    ]
																								    + 1
																								)

						return sum(different_colour_ways_number[length]						)


if __name__ == "__main__":
		print(F'{solution() = }')





