
from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase       :				Optional[Any]       =			logging.get_logger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Union[str, Any]       =					["pixel_values"]



	def __init__(  self	:    str			,  lowercase	:    bool = True			,  lowercase	:    Dict[str, int] = None			,  lowercase	:    PILImageResampling = PILImageResampling.BICUBIC			,  lowercase	:    bool = True			,  lowercase	:    Dict[str, int] = None			,  lowercase	:    bool = True			,  lowercase	:    Union[int, float] = 1 / 255			,  lowercase	:    bool = True			,  lowercase	:    Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN			,  lowercase	:    Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD			,  **lowercase	:    Union[str, Any]			,  ):


				'''simple docstring'''


				super().__init__(**lowercase	)
				_snake_case		=			size if size is not None else {'shortest_edge': 224}
				_snake_case		=			get_size_dict(lowercase			,  default_to_square=lowercase	)
				_snake_case		=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
				_snake_case		=			get_size_dict(lowercase			,  param_name='crop_size'	)

				_snake_case		=			do_resize
				_snake_case		=			size
				_snake_case		=			resample
				_snake_case		=			do_center_crop
				_snake_case		=			crop_size
				_snake_case		=			do_rescale
				_snake_case		=			rescale_factor
				_snake_case		=			do_normalize
				_snake_case		=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				_snake_case		=			image_std if image_std is not None else IMAGENET_DEFAULT_STD



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


				'''simple docstring'''


				_snake_case		=			get_size_dict(lowercase			,  default_to_square=lowercase	)
				# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
				if "shortest_edge" in size:
							_snake_case		=			int((256 / 224) * size['shortest_edge']	)
							_snake_case		=			get_resize_output_image_size(lowercase			,  size=lowercase			,  default_to_square=lowercase	)
							_snake_case		=			{'height': output_size[0], 'width': output_size[1]}
				if "height" not in size_dict or "width" not in size_dict:
							raise ValueError(
							    f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''	)
				return resize(
				    lowercase			,  size=(size_dict['height'], size_dict['width'])			,  resample=lowercase			,  data_format=lowercase			,  **lowercase	)



	def  A							(  self	:    List[str]			,  lowercase	:    np.ndarray			,  lowercase	:    Dict[str, int]			,  lowercase	:    Optional[Union[str, ChannelDimension]] = None			,  **lowercase	:    Dict			,  ):


				'''simple docstring'''


				_snake_case		=			get_size_dict(lowercase	)
				if "height" not in size or "width" not in size:
							raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
				return center_crop(lowercase			,  size=(size['height'], size['width'])			,  data_format=lowercase			,  **lowercase	)



	def  A							(  self	:    str			,  lowercase	:    np.ndarray			,  lowercase	:    Union[int, float]			,  lowercase	:    Optional[Union[str, ChannelDimension]] = None			,  **lowercase	:    Dict			,  ):


				'''simple docstring'''


				return rescale(lowercase			,  scale=lowercase			,  data_format=lowercase			,  **lowercase	)



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


				'''simple docstring'''


				return normalize(lowercase			,  mean=lowercase			,  std=lowercase			,  data_format=lowercase			,  **lowercase	)





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


				'''simple docstring'''


				_snake_case		=			do_resize if do_resize is not None else self.do_resize
				_snake_case		=			resample if resample is not None else self.resample
				_snake_case		=			do_center_crop if do_center_crop is not None else self.do_center_crop
				_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(lowercase			,  default_to_square=lowercase	)
				_snake_case		=			crop_size if crop_size is not None else self.crop_size
				_snake_case		=			get_size_dict(lowercase			,  param_name='crop_size'	)

				_snake_case		=			make_list_of_images(lowercase	)

				if not valid_images(lowercase	):
							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:
							raise ValueError('Size must be specified if do_resize is True.'	)

				if do_center_crop and crop_size is None:
							raise ValueError('Crop size must be specified if do_center_crop is True.'	)

				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(lowercase	) for image in images]

				if do_resize:
							_snake_case		=			[self.resize(lowercase			,  lowercase			,  lowercase	) for image in images]

				if do_center_crop:
							_snake_case		=			[self.center_crop(lowercase			,  lowercase	) for image in images]

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

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

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

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

from __future__ import annotations

from typing import Any




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    float = 0	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			row, column
				_snake_case		=			[[default_value for c in range(lowercase	)] for r in range(lowercase	)]



	def __str__(  self	:    int	):


				'''simple docstring'''


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

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

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

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



	def __repr__(  self	:    Dict	):


				'''simple docstring'''


				return str(self	)



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


				'''simple docstring'''


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



	def __getitem__(  self	:    Dict			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


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



	def __setitem__(  self	:    str			,  lowercase	:    tuple[int, int]			,  lowercase	:    float	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				_snake_case		=			value



	def __add__(  self	:    str			,  lowercase	:    Matrix	):


				'''simple docstring'''


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

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



	def __neg__(  self	:    Tuple	):


				'''simple docstring'''


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



	def __sub__(  self	:    List[str]			,  lowercase	:    Matrix	):


				'''simple docstring'''


				return self + (-another)



	def __mul__(  self	:    Dict			,  lowercase	:    int | float | Matrix	):


				'''simple docstring'''


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



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


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





	def  A							(  self	:    List[Any]			,  lowercase	:    Matrix			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	) and isinstance(lowercase			,  lowercase	)
				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		=			v.transpose()
				_snake_case		=			(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:
						# a^(-1)
						_snake_case		=			Matrix(3       ,					3       ,					0 )
						for i in range(3 ):
									_snake_case		=			1
						print(f'''a^(-1) is {ainv}''' )
						# u, v
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			1, 2, -3
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			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(__lowercase       ,					__lowercase )}''' )






			def        a_		(       ) ->  None:
						import doctest

						doctest.testmod()

			testa()