



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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


lowercase							       =			logging.get_logger(__name__)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=b.T
     a_       		=np.sum(np.square(lowercase__       )       ,	axis=1       )
     a_       		=np.sum(np.square(lowercase__       )       ,	axis=0       )
     a_       		=np.matmul(lowercase__       ,	lowercase__       )
     a_       		=aa[:, None] - 2 * ab + ba[None, :]
     return d






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=x.reshape(-1       ,	3       )
     a_       		=squared_euclidean_distance(lowercase__       ,	lowercase__       )
     return np.argmin(lowercase__       ,	axis=1       )







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: List[str]  =      ["pixel_values"]




 def __init__(   self							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = PILImageResampling.BILINEAR							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = True							,  **lowerCAmelCase_							,  ) ->      None:



      """simple docstring"""







      super().__init__(**lowerCAmelCase_)
      a_       		=size if size is not None else {"height": 2_5_6, "width": 2_5_6}
      a_       		=get_size_dict(lowerCAmelCase_)
      a_       		=np.array(lowerCAmelCase_) if clusters is not None else None
      a_       		=do_resize
      a_       		=size
      a_       		=resample
      a_       		=do_normalize
      a_       		=do_color_quantize




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = PILImageResampling.BILINEAR							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      np.ndarray:



      """simple docstring"""







      a_       		=get_size_dict(lowerCAmelCase_)
      if "height" not in size or "width" not in size:
           raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}""")
      return resize(
          lowerCAmelCase_							,  size=(size["height"], size["width"])							,  resample=lowerCAmelCase_							,  data_format=lowerCAmelCase_							,  **lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  ) ->      np.ndarray:



      """simple docstring"""







      a_       		=rescale(image=lowerCAmelCase_							,  scale=1 / 1_2_7.5							,  data_format=lowerCAmelCase_)
      a_       		=image - 1
      return image




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = ChannelDimension.FIRST							,  **lowerCAmelCase_							,  ) ->      PIL.Image.Image:



      """simple docstring"""







      a_       		=do_resize if do_resize is not None else self.do_resize
      a_       		=size if size is not None else self.size
      a_       		=get_size_dict(lowerCAmelCase_)
      a_       		=resample if resample is not None else self.resample
      a_       		=do_normalize if do_normalize is not None else self.do_normalize
      a_       		=do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      a_       		=clusters if clusters is not None else self.clusters
      a_       		=np.array(lowerCAmelCase_)

      a_       		=make_list_of_images(lowerCAmelCase_)

      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.")

      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_color_quantize and clusters is None:
           raise ValueError("Clusters must be specified if do_color_quantize is True.")

      # All transformations expect numpy arrays.
      a_       		=[to_numpy_array(lowerCAmelCase_) for image in images]

      if do_resize:
           a_       		=[self.resize(image=lowerCAmelCase_							,  size=lowerCAmelCase_							,  resample=lowerCAmelCase_) for image in images]

      if do_normalize:
           a_       		=[self.normalize(image=lowerCAmelCase_) for image in images]

      if do_color_quantize:
           a_       		=[to_channel_dimension_format(lowerCAmelCase_							,  ChannelDimension.LAST) for image in images]
           # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
           a_       		=np.array(lowerCAmelCase_)
           a_       		=color_quantize(lowerCAmelCase_							,  lowerCAmelCase_).reshape(images.shape[:-1])

           # flatten to (batch_size, height*width)
           a_       		=images.shape[0]
           a_       		=images.reshape(lowerCAmelCase_							,  -1)

           # We need to convert back to a list of images to keep consistent behaviour across processors.
           a_       		=list(lowerCAmelCase_)
      else:
           a_       		=[to_channel_dimension_format(lowerCAmelCase_							,  lowerCAmelCase_) for image in images]

      a_       		={"input_ids": images}
      return BatchFeature(data=lowerCAmelCase_							,  tensor_type=lowerCAmelCase_)







'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if b == 0:
          return (1, 0)
     ((a_)		,						(a_))       		=extended_euclid(lowercase__       ,	a % b       )
     a_       		=a // b
     return (y, x - k * y)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     ((a_)		,						(a_))       		=extended_euclid(lowercase__       ,	lowercase__       )
     a_       		=na * na
     a_       		=ra * x * na + ra * y * na
     return (n % m + m) % m






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     ((a_)		,						(a_))       		=extended_euclid(lowercase__       ,	lowercase__       )
     if b < 0:
          a_       		=(b % n + n) % n
     return b






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_		,						a_       		=invert_modulo(lowercase__       ,	lowercase__       ), invert_modulo(lowercase__       ,	lowercase__       )
     a_       		=na * na
     a_       		=ra * x * na + ra * y * na
     return (n % m + m) % m


if __name__ == "__main__":
  from doctest import testmod

  testmod(name='''chinese_remainder_theorem''', verbose=True)
  testmod(name='''chinese_remainder_theorem2''', verbose=True)
  testmod(name='''invert_modulo''', verbose=True)
  testmod(name='''extended_euclid''', verbose=True)


