




'''simple docstring'''




from __future__ import annotations

from typing import Any


class   __UpperCamelCase       :






       def __init__(							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase = 0  ):



              """simple docstring"""







              lowerCamelCase_,      lowerCamelCase_							=row, column
              lowerCamelCase_							=[[default_value for c in range(a__  )] for r in range(a__  )]






       def __str__(							self  ):



              """simple docstring"""







              lowerCamelCase_							=f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

              # Make string identifier
              lowerCamelCase_							=0
              for row_vector in self.array:
                     for obj in row_vector:
                            lowerCamelCase_							=max(a__,		len(str(a__  )  )  )
              lowerCamelCase_							=f'''%{max_element_length}s'''

              # Make string and return
              def single_line(lowerCAmelCase  ) -> str:
                     nonlocal string_format_identifier
                     lowerCamelCase_							='''['''
                     line += ", ".join(string_format_identifier % (obj,) for obj in row_vector  )
                     line += "]"
                     return line

              s += "\n".join(single_line(a__  ) for row_vector in self.array  )
              return s






       def __repr__(							self  ):



              """simple docstring"""







              return str(self  )






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              if not (isinstance(a__,		(list, tuple)  ) and len(a__  ) == 2):
                     return False
              elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
                     return False
              else:
                     return True






       def __getitem__(							self,		lowerCAmelCase  ):



              """simple docstring"""







              assert self.validate_indicies(a__  )
              return self.array[loc[0]][loc[1]]






       def __setitem__(							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              assert self.validate_indicies(a__  )
              lowerCamelCase_							=value






       def __add__(							self,		lowerCAmelCase  ):



              """simple docstring"""







              assert isinstance(a__,		a__  )
              assert self.row == another.row and self.column == another.column

              # Add
              lowerCamelCase_							=Matrix(self.row,		self.column  )
              for r in range(self.row  ):
                     for c in range(self.column  ):
                            lowerCamelCase_							=self[r, c] + another[r, c]
              return result






       def __neg__(							self  ):



              """simple docstring"""







              lowerCamelCase_							=Matrix(self.row,		self.column  )
              for r in range(self.row  ):
                     for c in range(self.column  ):
                            lowerCamelCase_							=-self[r, c]
              return result






       def __sub__(							self,		lowerCAmelCase  ):



              """simple docstring"""







              return self + (-another)






       def __mul__(							self,		lowerCAmelCase  ):



              """simple docstring"""







              if isinstance(a__,		(int, float)  ):  # Scalar multiplication
                     lowerCamelCase_							=Matrix(self.row,		self.column  )
                     for r in range(self.row  ):
                            for c in range(self.column  ):
                                   lowerCamelCase_							=self[r, c] * another
                     return result
              elif isinstance(a__,		a__  ):  # Matrix multiplication
                     assert self.column == another.row
                     lowerCamelCase_							=Matrix(self.row,		another.column  )
                     for r in range(self.row  ):
                            for c in range(another.column  ):
                                   for i in range(self.column  ):
                                          result[r, c] += self[r, i] * another[i, c]
                     return result
              else:
                     lowerCamelCase_							=f'''Unsupported type given for another ({type(a__  )})'''
                     raise TypeError(a__  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Matrix(self.column,		self.row  )
              for r in range(self.row  ):
                     for c in range(self.column  ):
                            lowerCamelCase_							=self[r, c]
              return result






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              assert isinstance(a__,		a__  ) and isinstance(a__,		a__  )
              assert self.row == self.column == u.row == v.row  # u, v should be column vector
              assert u.column == v.column == 1  # u, v should be column vector

              # Calculate
              lowerCamelCase_							=v.transpose()
              lowerCamelCase_							=(v_t * self * u)[0, 0] + 1
              if numerator_factor == 0:
                     return None  # It's not invertable
              return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





     def      a_							(      )							->						None:



            """simple docstring"""
            # a^(-1)
            lowerCamelCase_							=Matrix(3   ,       3   ,       0   )
            for i in range(3   ):
                   lowerCamelCase_							=1
            print(F'''a^(-1) is {ainv}'''   )
            # u, v
            lowerCamelCase_							=Matrix(3   ,       1   ,       0   )
            lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=1, 2, -3
            lowerCamelCase_							=Matrix(3   ,       1   ,       0   )
            lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=4, -2, 5
            print(F'''u is {u}'''   )
            print(F'''v is {v}'''   )
            print(F'''uv^T is {u * v.transpose()}'''   )
            # Sherman Morrison
            print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case_   ,       snake_case_   )}'''   )





     def      a_							(      )							->						None:



            """simple docstring"""
            import doctest

            doctest.testmod()
     testa()











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

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



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


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



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


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """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=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict

