






'''simple docstring'''

import mpmath  # for roots of unity
import numpy as np






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      list(poly_a or [0]   )[:]
        UpperCamelCase       						=      list(poly_b or [0]   )[:]

        # Remove leading zero coefficients
        while self.polyA[-1] == 0:
              self.polyA.pop()
        UpperCamelCase       						=      len(self.polyA   )

        while self.polyB[-1] == 0:
              self.polyB.pop()
        UpperCamelCase       						=      len(self.polyB   )

        # Add 0 to make lengths equal a power of 2
        UpperCamelCase       						=      int(
            2 ** np.ceil(np.loga(len(self.polyA   ) + len(self.polyB   ) - 1   )   )   )

        while len(self.polyA   ) < self.c_max_length:
              self.polyA.append(0   )
        while len(self.polyB   ) < self.c_max_length:
              self.polyB.append(0   )
        # A complex root used for the fourier transform
        UpperCamelCase       						=      complex(mpmath.root(x=1    ,	n=self.c_max_length    ,	k=1   )   )

        # The product
        UpperCamelCase       						=      self.__multiply()

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      [[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
        # Corner case
        if len(_SCREAMING_SNAKE_CASE   ) <= 1:
              return dft[0]
        #
        UpperCamelCase       						=      self.c_max_length // 2
        while next_ncol > 0:
              UpperCamelCase       						=      [[] for i in range(_SCREAMING_SNAKE_CASE   )]
              UpperCamelCase       						=      self.root**next_ncol

              # First half of next step
              UpperCamelCase       						=      1
              for j in range(self.c_max_length // (next_ncol * 2)   ):
                    for i in range(_SCREAMING_SNAKE_CASE   ):
                          new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]   )
                    current_root *= root
              # Second half of next step
              UpperCamelCase       						=      1
              for j in range(self.c_max_length // (next_ncol * 2)   ):
                    for i in range(_SCREAMING_SNAKE_CASE   ):
                          new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]   )
                    current_root *= root
              # Update
              UpperCamelCase       						=      new_dft
              UpperCamelCase       						=      next_ncol // 2
        return dft[0]

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.__dft("""A"""   )
        UpperCamelCase       						=      self.__dft("""B"""   )
        UpperCamelCase       						=      [[dft_a[i] * dft_b[i] for i in range(self.c_max_length   )]]
        del dft_a
        del dft_b

        # Corner Case
        if len(inverce_c[0]   ) <= 1:
              return inverce_c[0]
        # Inverse DFT
        UpperCamelCase       						=      2
        while next_ncol <= self.c_max_length:
              UpperCamelCase       						=      [[] for i in range(_SCREAMING_SNAKE_CASE   )]
              UpperCamelCase       						=      self.root ** (next_ncol // 2)
              UpperCamelCase       						=      1
              # First half of next step
              for j in range(self.c_max_length // next_ncol   ):
                    for i in range(next_ncol // 2   ):
                          # Even positions
                          new_inverse_c[i].append(
                              (
                                  inverce_c[i][j]
                                  + inverce_c[i][j + self.c_max_length // next_ncol]
                              )
                              / 2   )
                          # Odd positions
                          new_inverse_c[i + next_ncol // 2].append(
                              (
                                  inverce_c[i][j]
                                  - inverce_c[i][j + self.c_max_length // next_ncol]
                              )
                              / (2 * current_root)   )
                    current_root *= root
              # Update
              UpperCamelCase       						=      new_inverse_c
              next_ncol *= 2
        # Unpack
        UpperCamelCase       						=      [round(x[0].real    ,	8   ) + round(x[0].imag    ,	8   ) * 1j for x in inverce_c]

        # Remove leading 0's
        while inverce_c[-1] == 0:
              inverce_c.pop()
        return inverce_c


  def __str__(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      """A = """ + """ + """.join(
            F"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A]   )   )
        UpperCamelCase       						=      """B = """ + """ + """.join(
            F"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B]   )   )
        UpperCamelCase       						=      """A*B = """ + """ + """.join(
            F"{coef}*x^{i}" for coef, i in enumerate(self.product   )   )

        return F"{a}\n{b}\n{c}"


# Unit tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     SCREAMING_SNAKE_CASE__											=     CLIPImageProcessor()
     SCREAMING_SNAKE_CASE__											=     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     SCREAMING_SNAKE_CASE__											=     UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)
