




'''simple docstring'''


from __future__ import annotations

from decimal import Decimal

from numpy import array







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    list[list[float]]:
	UpperCAmelCase_		:      List[str]       	=					Decimal

	# Check if the provided matrix has 2 rows and 2 columns
	# since this implementation only works for 2x2 matrices
	if len(UpperCamelCase  ) == 2 and len(matrix[0]  ) == 2 and len(matrix[1]  ) == 2:
		# Calculate the determinant of the matrix
		UpperCAmelCase_		:      Optional[Any]       	=					float(
		    d(matrix[0][0]  ) * d(matrix[1][1]  ) - d(matrix[1][0]  ) * d(matrix[0][1]  )  )
		if determinant == 0:
			raise ValueError('This matrix has no inverse.'  )

		# Creates a copy of the matrix with swapped positions of the elements
		UpperCAmelCase_		:      Optional[int]       	=					[[0.0, 0.0], [0.0, 0.0]]
		UpperCAmelCase_		:      Optional[int]       	=					matrix[1][1], matrix[0][0]
		UpperCAmelCase_		:      List[Any]       	=					-matrix[1][0], -matrix[0][1]

		# Calculate the inverse of the matrix
		return [
		    [(float(d(UpperCamelCase  )  ) / determinant) or 0.0 for n in row] for row in swapped_matrix
		]
	elif (
	    len(UpperCamelCase  ) == 3
	    and len(matrix[0]  ) == 3
	    and len(matrix[1]  ) == 3
	    and len(matrix[2]  ) == 3
	):
		# Calculate the determinant of the matrix using Sarrus rule
		UpperCAmelCase_		:      int       	=					float(
		    (
		        (d(matrix[0][0]  ) * d(matrix[1][1]  ) * d(matrix[2][2]  ))
		        + (d(matrix[0][1]  ) * d(matrix[1][2]  ) * d(matrix[2][0]  ))
		        + (d(matrix[0][2]  ) * d(matrix[1][0]  ) * d(matrix[2][1]  ))
		    )
		    - (
		        (d(matrix[0][2]  ) * d(matrix[1][1]  ) * d(matrix[2][0]  ))
		        + (d(matrix[0][1]  ) * d(matrix[1][0]  ) * d(matrix[2][2]  ))
		        + (d(matrix[0][0]  ) * d(matrix[1][2]  ) * d(matrix[2][1]  ))
		    )  )
		if determinant == 0:
			raise ValueError('This matrix has no inverse.'  )

		# Creating cofactor matrix
		UpperCAmelCase_		:      int       	=					[
		    [d(0.0  ), d(0.0  ), d(0.0  )],
		    [d(0.0  ), d(0.0  ), d(0.0  )],
		    [d(0.0  ), d(0.0  ), d(0.0  )],
		]
		UpperCAmelCase_		:      int       	=					(d(matrix[1][1]  ) * d(matrix[2][2]  )) - (
		    d(matrix[1][2]  ) * d(matrix[2][1]  )
		)
		UpperCAmelCase_		:      Optional[int]       	=					-(
		    (d(matrix[1][0]  ) * d(matrix[2][2]  )) - (d(matrix[1][2]  ) * d(matrix[2][0]  ))
		)
		UpperCAmelCase_		:      Optional[int]       	=					(d(matrix[1][0]  ) * d(matrix[2][1]  )) - (
		    d(matrix[1][1]  ) * d(matrix[2][0]  )
		)
		UpperCAmelCase_		:      Dict       	=					-(
		    (d(matrix[0][1]  ) * d(matrix[2][2]  )) - (d(matrix[0][2]  ) * d(matrix[2][1]  ))
		)
		UpperCAmelCase_		:      Dict       	=					(d(matrix[0][0]  ) * d(matrix[2][2]  )) - (
		    d(matrix[0][2]  ) * d(matrix[2][0]  )
		)
		UpperCAmelCase_		:      List[Any]       	=					-(
		    (d(matrix[0][0]  ) * d(matrix[2][1]  )) - (d(matrix[0][1]  ) * d(matrix[2][0]  ))
		)
		UpperCAmelCase_		:      Dict       	=					(d(matrix[0][1]  ) * d(matrix[1][2]  )) - (
		    d(matrix[0][2]  ) * d(matrix[1][1]  )
		)
		UpperCAmelCase_		:      Optional[Any]       	=					-(
		    (d(matrix[0][0]  ) * d(matrix[1][2]  )) - (d(matrix[0][2]  ) * d(matrix[1][0]  ))
		)
		UpperCAmelCase_		:      Tuple       	=					(d(matrix[0][0]  ) * d(matrix[1][1]  )) - (
		    d(matrix[0][1]  ) * d(matrix[1][0]  )
		)

		# Transpose the cofactor matrix (Adjoint matrix)
		UpperCAmelCase_		:      str       	=					array(UpperCamelCase  )
		for i in range(3  ):
			for j in range(3  ):
				UpperCAmelCase_		:      int       	=					cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
		UpperCAmelCase_		:      str       	=					array(UpperCamelCase  )
		for i in range(3  ):
			for j in range(3  ):
				inverse_matrix[i][j] /= d(UpperCamelCase  )

        # Calculate the inverse of the matrix
		return [[float(d(UpperCamelCase  )  ) or 0.0 for n in row] for row in inverse_matrix]
	raise ValueError('Please provide a matrix of size 2x2 or 3x3.'  )









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

						lowerCAmelCase__										= 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)






