
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

_lowerCamelCase       :				int       =			logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase__					)
class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase__					):





	'''simple docstring'''



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


				'''simple docstring'''


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



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


				'''simple docstring'''


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



	def  A							(  self	:    Union[str, Any]			,  **lowercase	:    List[Any]	):


				'''simple docstring'''


				return {}, {}, {}



	def  A							(  self	:    List[str]			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			load_image(lowercase_	)
				_snake_case		=			image.size
				_snake_case		=			self.image_processor(images=lowercase_			,  return_tensors=self.framework	)
				return model_inputs



	def  A							(  self	:    int			,  lowercase	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			self.model(**lowercase_	)
				return model_outputs





	def  A							(  self	:    Any			,  lowercase	:    Optional[Any]	):


				'''simple docstring'''


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







"""simple docstring"""






def 					_A			(__a = 50							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		[[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() = }''')


