

"""simple docstring"""





from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Optional[Any]						,    __lowercase  :Collection[float] | None = None			):
											if components is None:
																		__lowerCamelCase      :      str         =[]
											__lowerCamelCase      :      int         =list(__lowercase			)




				def __len__(     self  :Union[str, Any]			):
											return len(self.__components			)




				def __str__(     self  :List[Any]			):
											return "(" + ",".join(map(__lowercase						,    self.__components			)			) + ")"




				def __add__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											__lowerCamelCase      :      Tuple         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      int         =[self.__components[i] + other.component(__lowercase			) for i in range(__lowercase			)]
																		return Vector(__lowercase			)
											else:
																		raise Exception('''must have the same size'''			)




				def __sub__(     self  :int						,    __lowercase  :Vector			):
											__lowerCamelCase      :      int         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      Union[str, Any]         =[self.__components[i] - other.component(__lowercase			) for i in range(__lowercase			)]
																		return Vector(__lowercase			)
											else:  # error case
																		raise Exception('''must have the same size'''			)




				@overload
				def __mul__(     self  :List[str]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :List[Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    (float, int)			):
																		__lowerCamelCase      :      Optional[int]         =[c * other for c in self.__components]
																		return Vector(__lowercase			)
											elif isinstance(__lowercase						,    __lowercase			) and len(self			) == len(__lowercase			):
																		__lowerCamelCase      :      Dict         =len(self			)
																		__lowerCamelCase      :      Union[str, Any]         =[self.__components[i] * other.component(__lowercase			) for i in range(__lowercase			)]
																		return sum(__lowercase			)
											else:  # error case
																		raise Exception('''invalid operand!'''			)




				def 					__lowercase  (     self  :Optional[Any]			):
											return Vector(self.__components			)




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :int			):
											if isinstance(__lowercase						,    __lowercase			) and -len(self.__components			) <= i < len(self.__components			):
																		return self.__components[i]
											else:
																		raise Exception('''index out of range'''			)




				def 					__lowercase  (     self  :int						,    __lowercase  :int						,    __lowercase  :float			):
											assert -len(self.__components			) <= pos < len(self.__components			)
											__lowerCamelCase      :      int         =value




				def 					__lowercase  (     self  :Union[str, Any]			):
											if len(self.__components			) == 0:
																		raise Exception('''Vector is empty'''			)
											__lowerCamelCase      :      Tuple         =[c**2 for c in self.__components]
											return math.sqrt(sum(__lowercase			)			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :Vector						,    __lowercase  :bool = False			):
											__lowerCamelCase      :      Tuple         =self * other
											__lowerCamelCase      :      str         =self.euclidean_length() * other.euclidean_length()
											if deg:
																		return math.degrees(math.acos(num / den			)			)
											else:
																		return math.acos(num / den			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							return Vector([0] * dimension   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) and (isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ))
							__lowerCamelCase      :      List[Any]         =[0] * dimension
							__lowerCamelCase      :      Union[str, Any]         =1
							return Vector(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							float    ,						SCREAMING_SNAKE_CASE       :							Vector    ,						SCREAMING_SNAKE_CASE       :							Vector   ):


							'''simple docstring'''







							assert (
							    isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    and isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    and (isinstance(SCREAMING_SNAKE_CASE    ,						(int, float)   ))
							)
							return x * scalar + y



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =[random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )]
							return Vector(SCREAMING_SNAKE_CASE   )
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Tuple						,    __lowercase  :list[list[float]]						,    __lowercase  :int						,    __lowercase  :int			):
											__lowerCamelCase      :      Optional[Any]         =matrix
											__lowerCamelCase      :      int         =w
											__lowerCamelCase      :      str         =h




				def __str__(     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         =''''''
											for i in range(self.__height			):
																		ans += "|"
																		for j in range(self.__width			):
																									if j < self.__width - 1:
																																ans += str(self.__matrix[i][j]			) + ","
																									else:
																																ans += str(self.__matrix[i][j]			) + "|\n"
											return ans




				def __add__(     self  :Any						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      Optional[int]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Optional[int]         =[
																									    self.__matrix[i][j] + other.component(__lowercase						,    __lowercase			)
																									    for j in range(self.__width			)
																									]
																									matrix.append(__lowercase			)
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											else:
																		raise Exception('''matrix must have the same dimension!'''			)




				def __sub__(     self  :Tuple						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      List[str]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Dict         =[
																									    self.__matrix[i][j] - other.component(__lowercase						,    __lowercase			)
																									    for j in range(self.__width			)
																									]
																									matrix.append(__lowercase			)
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											else:
																		raise Exception('''matrices must have the same dimension!'''			)




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :Union[str, Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    __lowercase			):  # matrix-vector
																		if len(__lowercase			) == self.__width:
																									__lowerCamelCase      :      int         =zero_vector(self.__height			)
																									for i in range(self.__height			):
																																__lowerCamelCase      :      Union[str, Any]         =[
																																    self.__matrix[i][j] * other.component(__lowercase			)
																																    for j in range(self.__width			)
																																]
																																ans.change_component(__lowercase						,    sum(__lowercase			)			)
																									return ans
																		else:
																									raise Exception(
																									    '''vector must have the same size as the '''
																									    '''number of columns of the matrix!'''			)
											elif isinstance(__lowercase						,    (int, float)			):  # matrix-scalar
																		__lowerCamelCase      :      Optional[Any]         =[
																		    [self.__matrix[i][j] * other for j in range(self.__width			)]
																		    for i in range(self.__height			)
																		]
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											return None




				def 					__lowercase  (     self  :Union[str, Any]			):
											return self.__height




				def 					__lowercase  (     self  :Tuple			):
											return self.__width




				def 					__lowercase  (     self  :Any						,    __lowercase  :int						,    __lowercase  :int			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		return self.__matrix[x][y]
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :float			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		__lowerCamelCase      :      int         =value
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :int						,    __lowercase  :int			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											__lowerCamelCase      :      Union[str, Any]         =self.__matrix[:x] + self.__matrix[x + 1 :]
											for i in range(len(__lowercase			)			):
																		__lowerCamelCase      :      Optional[int]         =minor[i][:y] + minor[i][y + 1 :]
											return Matrix(__lowercase						,    self.__width - 1						,    self.__height - 1			).determinant()




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		return (-1) ** (x + y) * self.minor(__lowercase						,    __lowercase			)
											else:
																		raise Exception('''Indices out of bounds'''			)




				def 					__lowercase  (     self  :Optional[Any]			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											if self.__height < 1:
																		raise Exception('''Matrix has no element'''			)
											elif self.__height == 1:
																		return self.__matrix[0][0]
											elif self.__height == 2:
																		return (
																		    self.__matrix[0][0] * self.__matrix[1][1]
																		    - self.__matrix[0][1] * self.__matrix[1][0]
																		)
											else:
																		__lowerCamelCase      :      int         =[
																		    self.__matrix[0][y] * self.cofactor(0						,    __lowercase			) for y in range(self.__width			)
																		]
																		return sum(__lowercase			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      list[list[float]]         =[[0] * n for _ in range(SCREAMING_SNAKE_CASE   )]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      list[list[float]]         =[
							    [random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )] for _ in range(SCREAMING_SNAKE_CASE   )
							]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )











"""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, 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


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		["""pixel_values"""]




				def __init__(     self  :List[str]						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Union[int, float] = 1 / 255						,    __lowercase  :bool = True						,    __lowercase  :bool = True						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    **__lowercase  :Union[str, Any]						,    ):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =size if size is not None else {'''height''': 256, '''width''': 256}
											__lowerCamelCase      :      Union[str, Any]         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      Tuple         =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase      :      int         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      Tuple         =do_resize
											__lowerCamelCase      :      int         =size
											__lowerCamelCase      :      Union[str, Any]         =resample
											__lowerCamelCase      :      Dict         =do_center_crop
											__lowerCamelCase      :      Any         =crop_size
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[Any]         =rescale_factor
											__lowerCamelCase      :      Any         =do_normalize
											__lowerCamelCase      :      int         =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase      :      List[str]         =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[int]						,    ):
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return resize(
											    __lowercase						,    size=(size['''height'''], size['''width'''])						,    resample=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[Any]						,    ):
											__lowerCamelCase      :      Dict         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return center_crop(__lowercase						,    size=(size['''height'''], size['''width'''])						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :int						,    __lowercase  :np.ndarray						,    __lowercase  :Union[int, float]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :List[Any]						,    ):
											return rescale(__lowercase						,    scale=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :np.ndarray						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Union[float, List[float]]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :int						,    ):
											return normalize(__lowercase						,    mean=__lowercase						,    std=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :ImageInput						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Optional[Any]=None						,    __lowercase  :bool = None						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :bool = None						,    __lowercase  :float = None						,    __lowercase  :bool = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[str, TensorType]] = None						,    __lowercase  :ChannelDimension = ChannelDimension.FIRST						,    **__lowercase  :Any						,    ):
											__lowerCamelCase      :      Optional[Any]         =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase      :      List[Any]         =resample if resample is not None else self.resample
											__lowerCamelCase      :      Optional[int]         =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase      :      List[str]         =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase      :      Optional[Any]         =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase      :      Optional[int]         =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase      :      Dict         =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase      :      Union[str, Any]         =image_std if image_std is not None else self.image_std

											__lowerCamelCase      :      Dict         =size if size is not None else self.size
											__lowerCamelCase      :      int         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      List[Any]         =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      int         =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 or resample is None:
																		raise ValueError('''Size and resample 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.
											__lowerCamelCase      :      str         =[to_numpy_array(__lowercase			) for image in images]

											if do_resize:
																		__lowerCamelCase      :      Union[str, Any]         =[self.resize(image=__lowercase						,    size=__lowercase						,    resample=__lowercase			) for image in images]

											if do_center_crop:
																		__lowerCamelCase      :      Optional[Any]         =[self.center_crop(image=__lowercase						,    size=__lowercase			) for image in images]

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

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

											__lowerCamelCase      :      Tuple         =[to_channel_dimension_format(__lowercase						,    __lowercase			) for image in images]

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




