



"""simple docstring"""


from __future__ import annotations






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""

  # preprocessing the first row
  for i in range(1   ,len(matrix[0]		)		):
    matrix[0][i] += matrix[0][i - 1]

  # preprocessing the first column
  for i in range(1   ,len(lowercase		)		):
    matrix[i][0] += matrix[i - 1][0]

  # updating the path cost for current position
  for i in range(1   ,len(lowercase		)		):
    for j in range(1   ,len(matrix[0]		)		):
      matrix[i][j] += min(matrix[i - 1][j]   ,matrix[i][j - 1]		)

  return matrix[-1][-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

"""simple docstring"""


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    UpperCAmelCase__  		=						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.""",
    )

    UpperCAmelCase__  		=						parser.parse_args()

    UpperCAmelCase__  		=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    UpperCAmelCase__  		=						CLIPImageProcessor()
    UpperCAmelCase__  		=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

    UpperCAmelCase__  		=						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)
