





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_												=     logging.get_logger(__name__)

@add_end_docstrings(lowercase_   )
class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										super().__init__(*_lowerCAmelCase	,  **_lowerCAmelCase	)
										requires_backends(self	,  "vision"	)
										self.check_model_type(_lowerCAmelCase	)





					def __call__( self	,  _lowerCAmelCase	,  **_lowerCAmelCase	):
										return super().__call__(_lowerCAmelCase	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return {}, {}, {}





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       load_image(_lowerCAmelCase	)
										lowerCamelCase__       =       image.size
										lowerCamelCase__       =       self.image_processor(images=_lowerCAmelCase	,  return_tensors=self.framework	)
										return model_inputs





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self.model(**_lowerCAmelCase	)
										return model_outputs






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








def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [1]
					for i in range(2,     a):
										factorials.append(factorials[-1] * i)
					assert 0 <= k < factorials[-1] * n, "k out of bounds"

					lowerCamelCase__       =       []
					lowerCamelCase__       =       list(range(a))

					# Find permutation
					while factorials:
										lowerCamelCase__       =       factorials.pop()
										lowerCamelCase__    ,		lowerCamelCase__       =       divmod(a,     a)
										permutation.append(elements[number])
										elements.remove(elements[number])
					permutation.append(elements[0])

					return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()

