






"""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


lowercase_				     =     logging.get_logger(__name__)



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  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 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

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


   def __init__(	self :     Optional[int],       _lowerCamelCase :     bool = True,       _lowerCamelCase :     Dict[str, int] = None,       _lowerCamelCase :     PILImageResampling = PILImageResampling.BILINEAR,       _lowerCamelCase :     bool = True,       _lowerCamelCase :     Dict[str, int] = None,       _lowerCamelCase :     bool = True,       _lowerCamelCase :     Union[int, float] = 1 / 2_55,       _lowerCamelCase :     bool = True,       _lowerCamelCase :     Optional[Union[float, List[float]]] = None,       _lowerCamelCase :     Optional[Union[float, List[float]]] = None,       **_lowerCamelCase :     List[str],       ):

     '''simple docstring'''
     super().__init__(**_lowerCamelCase  )
     __A									= size if size is not None else {'''shortest_edge''': 2_24}
     __A									= get_size_dict(_lowerCamelCase,       default_to_square=_lowerCamelCase  )
     __A									= crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
     __A									= get_size_dict(_lowerCamelCase,       param_name='''crop_size'''  )

     __A									= do_resize
     __A									= size
     __A									= do_center_crop
     __A									= crop_size
     __A									= resample
     __A									= do_rescale
     __A									= rescale_factor
     __A									= do_normalize
     __A									= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __A									= image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     np.ndarray,       _lowerCamelCase :     Dict[str, int],       _lowerCamelCase :     PILImageResampling = PILImageResampling.BILINEAR,       _lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None,       **_lowerCamelCase :     int,       ):

     '''simple docstring'''
     __A									= get_size_dict(_lowerCamelCase,       default_to_square=_lowerCamelCase  )
     if "shortest_edge" in size:
       __A									= get_resize_output_image_size(_lowerCamelCase,       size['''shortest_edge'''],       default_to_square=_lowerCamelCase  )
     elif "height" in size and "width" in size:
       __A									= (size['''height'''], size['''width'''])
     else:
       raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'  )
     return resize(_lowerCamelCase,       size=_lowerCamelCase,       resample=_lowerCamelCase,       data_format=_lowerCamelCase,       **_lowerCamelCase  )


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

     '''simple docstring'''
     __A									= get_size_dict(_lowerCamelCase  )
     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(_lowerCamelCase,       size=(size['''height'''], size['''width''']),       data_format=_lowerCamelCase,       **_lowerCamelCase  )


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

     '''simple docstring'''
     return rescale(_lowerCamelCase,       scale=_lowerCamelCase,       data_format=_lowerCamelCase,       **_lowerCamelCase  )


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

     '''simple docstring'''
     return normalize(_lowerCamelCase,       mean=_lowerCamelCase,       std=_lowerCamelCase,       data_format=_lowerCamelCase,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     ImageInput,       _lowerCamelCase :     bool = None,       _lowerCamelCase :     Dict[str, int] = None,       _lowerCamelCase :     PILImageResampling = None,       _lowerCamelCase :     bool = None,       _lowerCamelCase :     Dict[str, int] = 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[ChannelDimension] = 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									= to_numpy_array(_lowerCamelCase  )

     if do_resize:
       __A									= self.resize(image=_lowerCamelCase,       size=_lowerCamelCase,       resample=_lowerCamelCase  )

     if do_center_crop:
       __A									= self.center_crop(_lowerCamelCase,       size=_lowerCamelCase  )

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

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

     __A									= to_channel_dimension_format(_lowerCamelCase,       _lowerCamelCase  )
     return image






   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     ImageInput,       _lowerCamelCase :     bool = None,       _lowerCamelCase :     Dict[str, int] = None,       _lowerCamelCase :     PILImageResampling = None,       _lowerCamelCase :     bool = None,       _lowerCamelCase :     Dict[str, int] = 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'''
     __A									= do_resize if do_resize is not None else self.do_resize
     __A									= resample if resample is not None else self.resample
     __A									= do_center_crop if do_center_crop is not None else self.do_center_crop
     __A									= do_rescale if do_rescale is not None else self.do_rescale
     __A									= rescale_factor if rescale_factor is not None else self.rescale_factor
     __A									= do_normalize if do_normalize is not None else self.do_normalize
     __A									= image_mean if image_mean is not None else self.image_mean
     __A									= image_std if image_std is not None else self.image_std

     __A									= size if size is not None else self.size
     __A									= get_size_dict(_lowerCamelCase,       default_to_square=_lowerCamelCase  )
     __A									= crop_size if crop_size is not None else self.crop_size
     __A									= get_size_dict(_lowerCamelCase,       param_name='''crop_size'''  )

     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.'''  )

     __A									= make_batched(_lowerCamelCase  )

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

     __A									= {'''pixel_values''': videos}
     return BatchFeature(data=_lowerCamelCase,       tensor_type=_lowerCamelCase  )












"""simple docstring"""


def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= current_set.copy()
  for row_index, row in enumerate(__UpperCamelCase ):
    __A									= row[0]
    for column_index, column in enumerate(__UpperCamelCase ):
      if magnitude == 0:
        __A									= column
        continue
      __A									= column / magnitude
    # Subtract to cancel term
  __A									= current_set[0]
  __A									= [first_row]
  __A									= current_set[1::]
  for row in current_set:
    __A									= []
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(__UpperCamelCase )
      continue
    for column_index in range(len(__UpperCamelCase ) ):
      temp_row.append(first_row[column_index] - row[column_index] )
    final_set.append(__UpperCamelCase )
  # Create next recursion iteration set
  if len(final_set[0] ) != 3:
    __A									= final_set[0]
    __A									= []
    __A									= []
    for row in final_set[1::]:
      current_first_column.append(row[0] )
      next_iteration.append(row[1::] )
    __A									= simplify(__UpperCamelCase )
    for i in range(len(__UpperCamelCase ) ):
      resultant[i].insert(0		,       current_first_column[i] )
    resultant.insert(0		,       __UpperCamelCase )
    __A									= resultant
  return final_set



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if len(__UpperCamelCase ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  __A									= len(__UpperCamelCase ) + 1
  if any(len(__UpperCamelCase ) != _length for item in equations ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  for row in equations:
    if any(not isinstance(__UpperCamelCase		,       (int, float) ) for column in row ):
      raise ValueError('''solve_simultaneous() requires lists of integers''' )
  if len(__UpperCamelCase ) == 1:
    return [equations[0][-1] / equations[0][0]]
  __A									= equations.copy()
  if any(0 in row for row in data_set ):
    __A									= data_set.copy()
    __A									= []
    for row_index, row in enumerate(__UpperCamelCase ):
      if 0 not in row:
        __A									= data_set.pop(__UpperCamelCase )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
    data_set.insert(0		,       __UpperCamelCase )
  __A									= data_set.copy()
  __A									= simplify(__UpperCamelCase )
  __A									= simplified[::-1]
  __A									= []
  for row in simplified:
    __A									= row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0 )
        continue
      solutions.append(current_solution / row[-2] )
      continue
    __A									= row.copy()[: len(__UpperCamelCase ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0 )
    if len(__UpperCamelCase ) == 0:
      solutions.append(0 )
      continue
    __A									= temp_row[1::]
    __A									= temp_row[::-1]
    for column_index, column in enumerate(__UpperCamelCase ):
      current_solution -= column * solutions[column_index]
    solutions.append(__UpperCamelCase )
  __A									= []
  for item in solutions:
    final.append(float(round(__UpperCamelCase		,       5 ) ) )
  return final[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       lowercase_				     =     [
           [2, 1, 1, 1, 1, 4],
           [1, 2, 1, 1, 1, 5],
           [1, 1, 2, 1, 1, 6],
           [1, 1, 1, 2, 1, 7],
           [1, 1, 1, 1, 2, 8],
       ]
       print(solve_simultaneous(eq))
       print(solve_simultaneous([[4, 2]]))




