






from __future__ import annotations

from typing import Any


class 							__UpperCamelCase     :







	def __init__(	self   :				Optional[Any]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				float = 0      )		->			None:
		"""simple docstring"""







		__lowercase							,    __lowercase   							=	row, column
		__lowercase   							=	[[default_value for c in range(_lowerCAmelCase      )] for r in range(_lowerCAmelCase      )]







	def __str__(	self   :				List[Any]      )		->			str:
		"""simple docstring"""







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

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

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

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







	def __repr__(	self   :				int      )		->			str:
		"""simple docstring"""







		return str(self      )







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				tuple[int, int]      )		->			bool:
		"""simple docstring"""







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







	def __getitem__(	self   :				List[str]							,					_lowerCAmelCase   :				tuple[int, int]      )		->			Any:
		"""simple docstring"""







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







	def __setitem__(	self   :				List[Any]							,					_lowerCAmelCase   :				tuple[int, int]							,					_lowerCAmelCase   :				float      )		->			None:
		"""simple docstring"""







		assert self.validate_indicies(_lowerCAmelCase      )
		__lowercase   							=	value







	def __add__(	self   :				Any							,					_lowerCAmelCase   :				Matrix      )		->			Matrix:
		"""simple docstring"""







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

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







	def __neg__(	self   :				str      )		->			Matrix:
		"""simple docstring"""







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







	def __sub__(	self   :				Tuple							,					_lowerCAmelCase   :				Matrix      )		->			Matrix:
		"""simple docstring"""







		return self + (-another)







	def __mul__(	self   :				Dict							,					_lowerCAmelCase   :				int | float | Matrix      )		->			Matrix:
		"""simple docstring"""







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







	def 			_a (	self   :				Any      )		->			Matrix:
		"""simple docstring"""







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







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				Matrix							,					_lowerCAmelCase   :				Matrix      )		->			Any:
		"""simple docstring"""







		assert isinstance(_lowerCAmelCase							,					_lowerCAmelCase      ) and isinstance(_lowerCAmelCase							,					_lowerCAmelCase      )
		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
		__lowercase   							=	v.transpose()
		__lowercase   							=	(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 					snake_case	(  ):




							'''simple docstring'''







							__lowercase   							=	Matrix(3					,	3					,	0       )
							for i in range(3       ):
								__lowercase   							=	1
							print(F'a^(-1) is {ainv}'       )
							# u, v
							__lowercase   							=	Matrix(3					,	1					,	0       )
							__lowercase							,    __lowercase							,    __lowercase   							=	1, 2, -3
							__lowercase   							=	Matrix(3					,	1					,	0       )
							__lowercase							,    __lowercase							,    __lowercase   							=	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(lowerCamelCase					,	lowerCamelCase       )}'       )





						def 					snake_case	(  ):




							'''simple docstring'''







							import doctest

							doctest.testmod()




						testa()





'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_UpperCamelCase 							=  logging.get_logger(__name__)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							b.T
     __A	:       Optional[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=1							)
     __A	:       List[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=0							)
     __A	:       List[Any]      =							np.matmul(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     __A	:       str      =							aa[:, None] - 2 * ab + ba[None, :]
     return d


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							x.reshape(-1       ,					3							)
     __A	:       Any      =							squared_euclidean_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     return np.argmin(SCREAMING_SNAKE_CASE       ,					axis=1							)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				['pixel_values']


     def __init__(   self			,     lowerCamelCase = None			,     lowerCamelCase = True			,     lowerCamelCase = None			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = True			,     lowerCamelCase = True			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A	:       Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
          __A	:       int      =							get_size_dict(lowerCamelCase		)
          __A	:       str      =							np.array(lowerCamelCase		) if clusters is not None else None
          __A	:       List[Any]      =							do_resize
          __A	:       Dict      =							size
          __A	:       str      =							resample
          __A	:       Optional[Any]      =							do_normalize
          __A	:       Dict      =							do_color_quantize


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[Any]      =							get_size_dict(lowerCamelCase		)
          if "height" not in size or "width" not in size:
               raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}"		)
          return resize(
              lowerCamelCase			,     size=(size["height"], size["width"])			,     resample=lowerCamelCase			,     data_format=lowerCamelCase			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     ):


          '''simple docstring'''





          __A	:       List[str]      =							rescale(image=lowerCamelCase			,     scale=1 / 127.5			,     data_format=lowerCamelCase		)
          __A	:       List[Any]      =							image - 1
          return image


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = ChannelDimension.FIRST			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       List[Any]      =							do_resize if do_resize is not None else self.do_resize
          __A	:       int      =							size if size is not None else self.size
          __A	:       Tuple      =							get_size_dict(lowerCamelCase		)
          __A	:       Tuple      =							resample if resample is not None else self.resample
          __A	:       Union[str, Any]      =							do_normalize if do_normalize is not None else self.do_normalize
          __A	:       Tuple      =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
          __A	:       Any      =							clusters if clusters is not None else self.clusters
          __A	:       Dict      =							np.array(lowerCamelCase		)

          __A	:       Optional[int]      =							make_list_of_images(lowerCamelCase		)

          if not valid_images(lowerCamelCase		):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."		)

          if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

          if do_color_quantize and clusters is None:
               raise ValueError("Clusters must be specified if do_color_quantize is True."		)

          # All transformations expect numpy arrays.
          __A	:       Optional[Any]      =							[to_numpy_array(lowerCamelCase		) for image in images]

          if do_resize:
               __A	:       Dict      =							[self.resize(image=lowerCamelCase			,     size=lowerCamelCase			,     resample=lowerCamelCase		) for image in images]

          if do_normalize:
               __A	:       List[Any]      =							[self.normalize(image=lowerCamelCase		) for image in images]

          if do_color_quantize:
               __A	:       Union[str, Any]      =							[to_channel_dimension_format(lowerCamelCase			,     ChannelDimension.LAST		) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               __A	:       int      =							np.array(lowerCamelCase		)
               __A	:       Any      =							color_quantize(lowerCamelCase			,     lowerCamelCase		).reshape(images.shape[:-1]		)

               # flatten to (batch_size, height*width)
               __A	:       Tuple      =							images.shape[0]
               __A	:       List[Any]      =							images.reshape(lowerCamelCase			,     -1		)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               __A	:       Optional[int]      =							list(lowerCamelCase		)
          else:
               __A	:       Tuple      =							[to_channel_dimension_format(lowerCamelCase			,     lowerCamelCase		) for image in images]

          __A	:       Optional[int]      =							{"input_ids": images}
          return BatchFeature(data=lowerCamelCase			,     tensor_type=lowerCamelCase		)

