



"""simple docstring"""

from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__ = None       ):
											if components is None:
																snake_case_					:		Any 					=					[]
											snake_case_					:		Dict 					=					list(lowercase__       )







						def __len__(self       ):
											return len(self.__components       )







						def __str__(self       ):
											return "(" + ",".join(map(lowercase__   ,		self.__components       )       ) + ")"







						def __add__(self   ,		lowercase__       ):
											snake_case_					:		Any 					=					len(self       )
											if size == len(lowercase__       ):
																snake_case_					:		Union[str, Any] 					=					[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   ,		lowercase__       ):
											snake_case_					:		Optional[Any] 					=					len(self       )
											if size == len(lowercase__       ):
																snake_case_					:		str 					=					[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   ,		lowercase__       ):
											...







						@overload
						def __mul__(self   ,		lowercase__       ):
											...







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







						def 						__UpperCamelCase							(self       ):
											return Vector(self.__components       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ) and -len(self.__components       ) <= i < len(self.__components       ):
																return self.__components[i]
											else:
																raise Exception("""index out of range"""       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											assert -len(self.__components       ) <= pos < len(self.__components       )
											snake_case_					:		Any 					=					value







						def 						__UpperCamelCase							(self       ):
											if len(self.__components       ) == 0:
																raise Exception("""Vector is empty"""       )
											snake_case_					:		int 					=					[c**2 for c in self.__components]
											return math.sqrt(sum(lowercase__       )       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = False       ):
											snake_case_					:		str 					=					self * other
											snake_case_					:		Dict 					=					self.euclidean_length() * other.euclidean_length()
											if deg:
																return math.degrees(math.acos(num / den       )       )
											else:
																return math.acos(num / den       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





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







def 							SCREAMING_SNAKE_CASE__      (		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__			))
					snake_case_					:		int 					=					[0] * dimension
					snake_case_					:		Tuple 					=					1
					return Vector(SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					random.seed(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Tuple 					=					[random.randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			) for _ in range(SCREAMING_SNAKE_CASE__			)]
					return Vector(SCREAMING_SNAKE_CASE__			)







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		str 					=					matrix
											snake_case_					:		List[Any] 					=					w
											snake_case_					:		List[Any] 					=					h







						def __str__(self       ):
											snake_case_					:		Union[str, Any] 					=					""""""
											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   ,		lowercase__       ):
											if self.__width == other.width() and self.__height == other.height():
																snake_case_					:		List[str] 					=					[]
																for i in range(self.__height       ):
																					snake_case_					:		List[Any] 					=					[
																					    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   ,		lowercase__       ):
											if self.__width == other.width() and self.__height == other.height():
																snake_case_					:		List[str] 					=					[]
																for i in range(self.__height       ):
																					snake_case_					:		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   ,		lowercase__       ):
											...







						@overload
						def __mul__(self   ,		lowercase__       ):
											...







						def __mul__(self   ,		lowercase__       ):
											if isinstance(lowercase__   ,		lowercase__       ):  # matrix-vector
																if len(lowercase__       ) == self.__width:
																					snake_case_					:		Optional[int] 					=					zero_vector(self.__height       )
																					for i in range(self.__height       ):
																										snake_case_					:		List[str] 					=					[
																										    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
																snake_case_					:		Optional[int] 					=					[
																    [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 						__UpperCamelCase							(self       ):
											return self.__height







						def 						__UpperCamelCase							(self       ):
											return self.__width







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											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 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																snake_case_					:		List[Any] 					=					value
											else:
																raise Exception("""change_component: indices out of bounds"""       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if self.__height != self.__width:
																raise Exception("""Matrix is not square"""       )
											snake_case_					:		int 					=					self.__matrix[:x] + self.__matrix[x + 1 :]
											for i in range(len(lowercase__       )       ):
																snake_case_					:		Tuple 					=					minor[i][:y] + minor[i][y + 1 :]
											return Matrix(lowercase__   ,		self.__width - 1   ,		self.__height - 1       ).determinant()







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											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 						__UpperCamelCase							(self       ):
											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:
																snake_case_					:		str 					=					[
																    self.__matrix[0][y] * self.cofactor(0   ,		lowercase__       ) for y in range(self.__width       )
																]
																return sum(lowercase__       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					random.seed(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		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,
    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, is_vision_available, logging


if is_vision_available():
			import PIL


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							int							       =		["""pixel_values"""]







						def __init__(self   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 0.9   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 1 / 2_55   ,		lowercase__ = True   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = None   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )
											snake_case_					:		Tuple 					=					size if size is not None else {"""shortest_edge""": 2_24}
											snake_case_					:		Union[str, Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		str 					=					crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
											snake_case_					:		Dict 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		Union[str, Any] 					=					do_resize
											snake_case_					:		List[str] 					=					size
											snake_case_					:		str 					=					crop_pct
											snake_case_					:		str 					=					resample
											snake_case_					:		Optional[Any] 					=					do_center_crop
											snake_case_					:		Dict 					=					crop_size
											snake_case_					:		int 					=					do_rescale
											snake_case_					:		Optional[int] 					=					rescale_factor
											snake_case_					:		str 					=					do_normalize
											snake_case_					:		str 					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											snake_case_					:		List[str] 					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		Tuple 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
											if crop_pct is not None:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					int(size["""shortest_edge"""] / crop_pct       )
																elif "height" in size and "width" in size:
																					if size["height"] == size["width"]:
																										snake_case_					:		Dict 					=					int(size["""height"""] / crop_pct       )
																					else:
																										snake_case_					:		List[str] 					=					(int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

																snake_case_					:		List[Any] 					=					get_resize_output_image_size(lowercase__   ,		size=lowercase__   ,		default_to_square=lowercase__       )
											else:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					get_resize_output_image_size(lowercase__   ,		size=size["""shortest_edge"""]   ,		default_to_square=lowercase__       )
																elif "height" in size and "width" in size:
																					snake_case_					:		int 					=					(size["""height"""], size["""width"""])
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

											return resize(lowercase__   ,		size=lowercase__   ,		resample=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		int 					=					get_size_dict(lowercase__       )
											if "height" not in size or "width" not in size:
																raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
											return center_crop(lowercase__   ,		size=(size["""height"""], size["""width"""])   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return rescale(lowercase__   ,		scale=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return normalize(lowercase__   ,		mean=lowercase__   ,		std=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = ChannelDimension.FIRST   ,		**lowercase__   ,		):
											snake_case_					:		str 					=					do_resize if do_resize is not None else self.do_resize
											snake_case_					:		Any 					=					crop_pct if crop_pct is not None else self.crop_pct
											snake_case_					:		List[Any] 					=					resample if resample is not None else self.resample
											snake_case_					:		str 					=					do_center_crop if do_center_crop is not None else self.do_center_crop
											snake_case_					:		str 					=					do_rescale if do_rescale is not None else self.do_rescale
											snake_case_					:		Any 					=					rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_					:		str 					=					do_normalize if do_normalize is not None else self.do_normalize
											snake_case_					:		List[Any] 					=					image_mean if image_mean is not None else self.image_mean
											snake_case_					:		int 					=					image_std if image_std is not None else self.image_std

											snake_case_					:		List[Any] 					=					size if size is not None else self.size
											snake_case_					:		Optional[Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		List[Any] 					=					crop_size if crop_size is not None else self.crop_size
											snake_case_					:		int 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		List[str] 					=					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_pct is None:
																raise ValueError("""Crop_pct 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_					:		int 					=					[to_numpy_array(lowercase__       ) for image in images]

											if do_resize:
																snake_case_					:		str 					=					[self.resize(image=lowercase__   ,		size=lowercase__   ,		crop_pct=lowercase__   ,		resample=lowercase__       ) for image in images]

											if do_center_crop:
																snake_case_					:		Optional[int] 					=					[self.center_crop(image=lowercase__   ,		size=lowercase__       ) for image in images]

											if do_rescale:
																snake_case_					:		List[Any] 					=					[self.rescale(image=lowercase__   ,		scale=lowercase__       ) for image in images]

											if do_normalize:
																snake_case_					:		Optional[Any] 					=					[self.normalize(image=lowercase__   ,		mean=lowercase__   ,		std=lowercase__       ) for image in images]

											snake_case_					:		List[Any] 					=					[to_channel_dimension_format(lowercase__   ,		lowercase__       ) for image in images]

											snake_case_					:		Dict 					=					{"""pixel_values""": images}
											return BatchFeature(data=lowercase__   ,		tensor_type=lowercase__       )




