


'''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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


UpperCAmelCase_ :     Optional[int]								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any]			):
 if isinstance(UpperCamelCase__,				(list, tuple)			) and isinstance(videos[0],				(list, tuple)			) and is_valid_image(videos[0][0]			):
  return videos

 elif isinstance(UpperCamelCase__,				(list, tuple)			) and is_valid_image(videos[0]			):
  return [videos]

 elif is_valid_image(UpperCamelCase__			):
  return [[videos]]

 raise ValueError(f"""Could not make batched video from {videos}"""			)




class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		List[Any]    							=						['''pixel_values''']


 def __init__(      self      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = PILImageResampling.BILINEAR      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = True      ,      __lowercase = 1 / 255      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__lowercase						)
  __A  :		Union[str, Any]       =			size if size is not None else {'shortest_edge': 224}
  __A  :		Tuple       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  __A  :		str       =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
  __A  :		List[Any]       =			get_size_dict(__lowercase      ,      param_name='crop_size'						)

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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = PILImageResampling.BILINEAR      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		str       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  if "shortest_edge" in size:
   __A  :		Union[str, Any]       =			get_resize_output_image_size(__lowercase      ,      size['shortest_edge']      ,      default_to_square=__lowercase						)
  elif "height" in size and "width" in size:
   __A  :		List[str]       =			(size['height'], size['width'])
  else:
   raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""						)
  return resize(__lowercase      ,      size=__lowercase      ,      resample=__lowercase      ,      data_format=__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Tuple       =			get_size_dict(__lowercase						)
  if "height" not in size or "width" not in size:
   raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""						)
  return center_crop(__lowercase      ,      size=(size['height'], size['width'])      ,      data_format=__lowercase      ,      **__lowercase						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = ChannelDimension.FIRST      ,      ):

  """simple docstring"""







  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.
  __A  :		Tuple       =			to_numpy_array(__lowercase						)

  if do_resize:
   __A  :		List[Any]       =			self.resize(image=__lowercase      ,      size=__lowercase      ,      resample=__lowercase						)

  if do_center_crop:
   __A  :		List[Any]       =			self.center_crop(__lowercase      ,      size=__lowercase						)

  if do_rescale:
   __A  :		Any       =			self.rescale(image=__lowercase      ,      scale=__lowercase						)

  if do_normalize:
   __A  :		str       =			self.normalize(image=__lowercase      ,      mean=__lowercase      ,      std=__lowercase						)

  __A  :		Dict       =			to_channel_dimension_format(__lowercase      ,      __lowercase						)
  return image







 def  snake_case__   (      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 = ChannelDimension.FIRST      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Optional[int]       =			do_resize if do_resize is not None else self.do_resize
  __A  :		str       =			resample if resample is not None else self.resample
  __A  :		Optional[int]       =			do_center_crop if do_center_crop is not None else self.do_center_crop
  __A  :		Tuple       =			do_rescale if do_rescale is not None else self.do_rescale
  __A  :		str       =			rescale_factor if rescale_factor is not None else self.rescale_factor
  __A  :		int       =			do_normalize if do_normalize is not None else self.do_normalize
  __A  :		List[Any]       =			image_mean if image_mean is not None else self.image_mean
  __A  :		List[Any]       =			image_std if image_std is not None else self.image_std

  __A  :		Optional[Any]       =			size if size is not None else self.size
  __A  :		int       =			get_size_dict(__lowercase      ,      default_to_square=__lowercase						)
  __A  :		str       =			crop_size if crop_size is not None else self.crop_size
  __A  :		Any       =			get_size_dict(__lowercase      ,      param_name='crop_size'						)

  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.'						)

  __A  :		List[Any]       =			make_batched(__lowercase						)

  __A  :		Dict       =			[
      [
          self._preprocess_image(
              image=__lowercase      ,      do_resize=__lowercase      ,      size=__lowercase      ,      resample=__lowercase      ,      do_center_crop=__lowercase      ,      crop_size=__lowercase      ,      do_rescale=__lowercase      ,      rescale_factor=__lowercase      ,      do_normalize=__lowercase      ,      image_mean=__lowercase      ,      image_std=__lowercase      ,      data_format=__lowercase      ,      )
          for img in video
      ]
      for video in videos
  ]

  __A  :		List[str]       =			{'pixel_values': videos}
  return BatchFeature(data=__lowercase      ,      tensor_type=__lowercase						)










'''simple docstring'''
from __future__ import annotations

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

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase = None						):

  """simple docstring"""







  if components is None:
   __A  :		Optional[int]       =			[]
  __A  :		Dict       =			list(__lowercase						)


 def __len__(      self						):

  """simple docstring"""







  return len(self.__components						)


 def __str__(      self						):

  """simple docstring"""







  return "(" + ",".join(map(__lowercase      ,      self.__components						)						) + ")"


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			len(self						)
  if size == len(__lowercase						):
   __A  :		Dict       =			[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						):

  """simple docstring"""







  __A  :		int       =			len(self						)
  if size == len(__lowercase						):
   __A  :		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						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  return Vector(self.__components						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  assert -len(self.__components						) <= pos < len(self.__components						)
  __A  :		str       =			value


 def  snake_case__   (      self						):

  """simple docstring"""







  if len(self.__components						) == 0:
   raise Exception('Vector is empty'						)
  __A  :		List[Any]       =			[c**2 for c in self.__components]
  return math.sqrt(sum(__lowercase						)						)







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = False						):

  """simple docstring"""







  __A  :		List[Any]       =			self * other
  __A  :		Any       =			self.euclidean_length() * other.euclidean_length()
  if deg:
   return math.degrees(math.acos(num / den						)						)
  else:
   return math.acos(num / den						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			)
 return Vector([0] * dimension			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			) and (isinstance(UpperCamelCase__,				UpperCamelCase__			))
 __A  :		List[str]       =			[0] * dimension
 __A  :		Tuple       =			1
 return Vector(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					Vector,				UpperCamelCase__       :					Vector			):
 assert (
     isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and (isinstance(UpperCamelCase__,				(int, float)			))
 )
 return x * scalar + y


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		Any       =			[random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)]
 return Vector(UpperCamelCase__			)

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			matrix
  __A  :		str       =			w
  __A  :		Dict       =			h


 def __str__(      self						):

  """simple docstring"""







  __A  :		Tuple       =			''
  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						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Union[str, Any]       =			[]
   for i in range(self.__height						):
    __A  :		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						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Dict       =			[]
   for i in range(self.__height						):
    __A  :		str       =			[
        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						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						):  # matrix-vector
   if len(__lowercase						) == self.__width:
    __A  :		Tuple       =			zero_vector(self.__height						)
    for i in range(self.__height						):
     __A  :		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
   __A  :		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  snake_case__   (      self						):

  """simple docstring"""







  return self.__height


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__width


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  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  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   __A  :		Dict       =			value
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







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


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  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  snake_case__   (      self						):

  """simple docstring"""







  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:
   __A  :		List[str]       =			[
       self.__matrix[0][y] * self.cofactor(0      ,      __lowercase						) for y in range(self.__width						)
   ]
   return sum(__lowercase						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		list[list[float]]       =			[[0] * n for _ in range(UpperCamelCase__			)]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		list[list[float]]       =			[
     [random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)] for _ in range(UpperCamelCase__			)
 ]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)






