



"""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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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


snake_case   					=				logging.get_logger(__name__)



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Optional[Any]           =  ['''pixel_values''']



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	bool = True							,       __lowerCamelCase    :	Dict[str, int] = None							,       __lowerCamelCase    :	float = None							,       __lowerCamelCase    :	PILImageResampling = PILImageResampling.BILINEAR							,       __lowerCamelCase    :	bool = True							,       __lowerCamelCase    :	Union[int, float] = 1 / 2_5_5							,       __lowerCamelCase    :	bool = True							,       __lowerCamelCase    :	Optional[Union[float, List[float]]] = None							,       __lowerCamelCase    :	Optional[Union[float, List[float]]] = None							,       **__lowerCamelCase    :	Union[str, Any]							,       ):



     """simple docstring"""
     super().__init__(**__lowerCamelCase				)
     _snake_case   	=      size if size is not None else {'''shortest_edge''': 3_8_4}
     _snake_case   	=      get_size_dict(__lowerCamelCase							,       default_to_square=__lowerCamelCase				)

     _snake_case   	=      do_resize
     _snake_case   	=      size
     # Default value set here for backwards compatibility where the value in config is None
     _snake_case   	=      crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
     _snake_case   	=      resample
     _snake_case   	=      do_rescale
     _snake_case   	=      rescale_factor
     _snake_case   	=      do_normalize
     _snake_case   	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _snake_case   	=      image_std if image_std is not None else IMAGENET_STANDARD_STD



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	np.ndarray							,       __lowerCamelCase    :	Dict[str, int]							,       __lowerCamelCase    :	float							,       __lowerCamelCase    :	PILImageResampling = PILImageResampling.BICUBIC							,       __lowerCamelCase    :	Optional[Union[str, ChannelDimension]] = None							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      get_size_dict(__lowerCamelCase							,       default_to_square=__lowerCamelCase				)
     if "shortest_edge" not in size:
       raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""				)
     _snake_case   	=      size['''shortest_edge''']

     if shortest_edge < 3_8_4:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       _snake_case   	=      int(shortest_edge / crop_pct				)
       _snake_case   	=      get_resize_output_image_size(__lowerCamelCase							,       size=__lowerCamelCase							,       default_to_square=__lowerCamelCase				)
       _snake_case   	=      resize(image=__lowerCamelCase							,       size=__lowerCamelCase							,       resample=__lowerCamelCase							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=__lowerCamelCase							,       size=(shortest_edge, shortest_edge)							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           __lowerCamelCase							,       size=(shortest_edge, shortest_edge)							,       resample=__lowerCamelCase							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	np.ndarray							,       __lowerCamelCase    :	Union[int, float]							,       __lowerCamelCase    :	Optional[Union[str, ChannelDimension]] = None							,       **__lowerCamelCase    :	int							,       ):



     """simple docstring"""
     return rescale(__lowerCamelCase							,       scale=__lowerCamelCase							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	np.ndarray							,       __lowerCamelCase    :	Union[float, List[float]]							,       __lowerCamelCase    :	Union[float, List[float]]							,       __lowerCamelCase    :	Optional[Union[str, ChannelDimension]] = None							,       **__lowerCamelCase    :	Optional[int]							,       ):



     """simple docstring"""
     return normalize(__lowerCamelCase							,       mean=__lowerCamelCase							,       std=__lowerCamelCase							,       data_format=__lowerCamelCase							,       **__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	ImageInput							,       __lowerCamelCase    :	bool = None							,       __lowerCamelCase    :	Dict[str, int] = None							,       __lowerCamelCase    :	float = None							,       __lowerCamelCase    :	PILImageResampling = None							,       __lowerCamelCase    :	bool = None							,       __lowerCamelCase    :	float = None							,       __lowerCamelCase    :	bool = None							,       __lowerCamelCase    :	Optional[Union[float, List[float]]] = None							,       __lowerCamelCase    :	Optional[Union[float, List[float]]] = None							,       __lowerCamelCase    :	Optional[Union[str, TensorType]] = None							,       __lowerCamelCase    :	ChannelDimension = ChannelDimension.FIRST							,       **__lowerCamelCase    :	Optional[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      do_resize if do_resize is not None else self.do_resize
     _snake_case   	=      crop_pct if crop_pct is not None else self.crop_pct
     _snake_case   	=      resample if resample is not None else self.resample
     _snake_case   	=      do_rescale if do_rescale is not None else self.do_rescale
     _snake_case   	=      rescale_factor if rescale_factor is not None else self.rescale_factor
     _snake_case   	=      do_normalize if do_normalize is not None else self.do_normalize
     _snake_case   	=      image_mean if image_mean is not None else self.image_mean
     _snake_case   	=      image_std if image_std is not None else self.image_std

     _snake_case   	=      size if size is not None else self.size
     _snake_case   	=      get_size_dict(__lowerCamelCase							,       default_to_square=__lowerCamelCase				)

     _snake_case   	=      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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
       raise ValueError('''crop_pct must be specified if size < 384.'''				)

     if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

     # All transformations expect numpy arrays.
     _snake_case   	=      [to_numpy_array(__lowerCamelCase				) for image in images]

     if do_resize:
       _snake_case   	=      [self.resize(image=__lowerCamelCase							,       size=__lowerCamelCase							,       crop_pct=__lowerCamelCase							,       resample=__lowerCamelCase				) for image in images]

     if do_rescale:
       _snake_case   	=      [self.rescale(image=__lowerCamelCase							,       scale=__lowerCamelCase				) for image in images]

     if do_normalize:
       _snake_case   	=      [self.normalize(image=__lowerCamelCase							,       mean=__lowerCamelCase							,       std=__lowerCamelCase				) for image in images]

     _snake_case   	=      [to_channel_dimension_format(__lowerCamelCase							,       __lowerCamelCase				) for image in images]

     _snake_case   	=      {'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase							,       tensor_type=__lowerCamelCase				)












'''simple docstring'''






from __future__ import annotations

from typing import Any




class  _A :

		def __init__(   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   float = 0			)					->    None:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Optional[Any]							      =     row, column
					__snake_case					:      Dict							      =     [[default_value for c in range(__magic_name__			)] for r in range(__magic_name__			)]

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


					"""simple docstring"""

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

					# Make string identifier
					__snake_case					:      Optional[int]							      =     0
					for row_vector in self.array:
								for obj in row_vector:
											__snake_case					:      Optional[int]							      =     max(__magic_name__  ,      len(str(__magic_name__			)			)			)
					__snake_case					:      str							      =     f'''%{max_element_length}s'''

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

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

		def __repr__(   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					return str(self			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   tuple[int, int]			)					->    bool:


					"""simple docstring"""

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

		def __getitem__(   self					:   int  ,      __magic_name__					:   tuple[int, int]			)					->    Any:


					"""simple docstring"""

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

		def __setitem__(   self					:   List[str]  ,      __magic_name__					:   tuple[int, int]  ,      __magic_name__					:   float			)					->    None:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					__snake_case					:      Optional[int]							      =     value

		def __add__(   self					:   Any  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

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

					# Add
					__snake_case					:      Union[str, Any]							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     self[r, c] + another[r, c]
					return result

		def __neg__(   self					:   Tuple			)					->    Matrix:


					"""simple docstring"""

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

		def __sub__(   self					:   Optional[int]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					return self + (-another)

		def __mul__(   self					:   List[Any]  ,      __magic_name__					:   int | float | Matrix			)					->    Matrix:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   str			)					->    Matrix:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Matrix  ,      __magic_name__					:   Matrix			)					->    Any:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			) and isinstance(__magic_name__  ,      __magic_name__			)
					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
					__snake_case					:      List[str]							      =     v.transpose()
					__snake_case					:      Tuple							      =     (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"""


				__snake_case					:      Tuple							      =     Matrix(3    ,	3    ,	0 )
				for i in range(3 ):
							__snake_case					:      Any							      =     1
				print(F'''a^(-1) is {ainv}''' )
				# u, v
				__snake_case					:      Dict							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Union[str, Any]							      =     1, 2, -3
				__snake_case					:      str							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Tuple							      =     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 					_a						(       ) ->				None:



				"""simple docstring"""


				import doctest

				doctest.testmod()

	testa()


