






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__UpperCAmelCase										=		{512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}




def   lowercase__   (  __snake_case					:							Dict						):


			'''simple docstring'''


			UpperCAmelCase_					:   Tuple   =		F"pretrained_models/{model_name}"
			if not os.path.isfile(__snake_case						):
						os.makedirs('pretrained_models'  , exist_ok=__snake_case						)
						UpperCAmelCase_					:   int   =		F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
						download_url(__snake_case  , 'pretrained_models'						)
			UpperCAmelCase_					:   Union[str, Any]   =		torch.load(__snake_case  , map_location=lambda __snake_case  , __snake_case						: storage						)
			return model




def   lowercase__   (  __snake_case					:							Optional[int]						):


			'''simple docstring'''


			UpperCAmelCase_					:   Any   =		download_model(pretrained_models[args.image_size]						)

			UpperCAmelCase_					:   str   =		state_dict['x_embedder.proj.weight']
			UpperCAmelCase_					:   Tuple   =		state_dict['x_embedder.proj.bias']
			state_dict.pop('x_embedder.proj.weight'						)
			state_dict.pop('x_embedder.proj.bias'						)

			for depth in range(28						):
						UpperCAmelCase_					:   List[Any]   =		state_dict[
						    't_embedder.mlp.0.weight'
						]
						UpperCAmelCase_					:   str   =		state_dict[
						    't_embedder.mlp.0.bias'
						]
						UpperCAmelCase_					:   Optional[int]   =		state_dict[
						    't_embedder.mlp.2.weight'
						]
						UpperCAmelCase_					:   Union[str, Any]   =		state_dict[
						    't_embedder.mlp.2.bias'
						]
						UpperCAmelCase_					:   Optional[Any]   =		state_dict[
						    'y_embedder.embedding_table.weight'
						]

						UpperCAmelCase_					:   str   =		state_dict[
						    F"blocks.{depth}.adaLN_modulation.1.weight"
						]
						UpperCAmelCase_					:   List[Any]   =		state_dict[
						    F"blocks.{depth}.adaLN_modulation.1.bias"
						]

						UpperCAmelCase_					:   int   =		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.weight"]  , 3  , dim=0						)
						UpperCAmelCase_					:   Optional[int]   =		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.bias"]  , 3  , dim=0						)

						UpperCAmelCase_					:   Dict   =		q
						UpperCAmelCase_					:   List[str]   =		q_bias
						UpperCAmelCase_					:   Union[str, Any]   =		k
						UpperCAmelCase_					:   Union[str, Any]   =		k_bias
						UpperCAmelCase_					:   List[Any]   =		v
						UpperCAmelCase_					:   Optional[Any]   =		v_bias

						UpperCAmelCase_					:   Optional[int]   =		state_dict[
						    F"blocks.{depth}.attn.proj.weight"
						]
						UpperCAmelCase_					:   str   =		state_dict[F"blocks.{depth}.attn.proj.bias"]

						UpperCAmelCase_					:   Optional[Any]   =		state_dict[F"blocks.{depth}.mlp.fc1.weight"]
						UpperCAmelCase_					:   List[Any]   =		state_dict[F"blocks.{depth}.mlp.fc1.bias"]
						UpperCAmelCase_					:   Dict   =		state_dict[F"blocks.{depth}.mlp.fc2.weight"]
						UpperCAmelCase_					:   Any   =		state_dict[F"blocks.{depth}.mlp.fc2.bias"]

						state_dict.pop(F"blocks.{depth}.attn.qkv.weight"						)
						state_dict.pop(F"blocks.{depth}.attn.qkv.bias"						)
						state_dict.pop(F"blocks.{depth}.attn.proj.weight"						)
						state_dict.pop(F"blocks.{depth}.attn.proj.bias"						)
						state_dict.pop(F"blocks.{depth}.mlp.fc1.weight"						)
						state_dict.pop(F"blocks.{depth}.mlp.fc1.bias"						)
						state_dict.pop(F"blocks.{depth}.mlp.fc2.weight"						)
						state_dict.pop(F"blocks.{depth}.mlp.fc2.bias"						)
						state_dict.pop(F"blocks.{depth}.adaLN_modulation.1.weight"						)
						state_dict.pop(F"blocks.{depth}.adaLN_modulation.1.bias"						)

			state_dict.pop('t_embedder.mlp.0.weight'						)
			state_dict.pop('t_embedder.mlp.0.bias'						)
			state_dict.pop('t_embedder.mlp.2.weight'						)
			state_dict.pop('t_embedder.mlp.2.bias'						)
			state_dict.pop('y_embedder.embedding_table.weight'						)

			UpperCAmelCase_					:   Optional[int]   =		state_dict['final_layer.adaLN_modulation.1.weight']
			UpperCAmelCase_					:   int   =		state_dict['final_layer.adaLN_modulation.1.bias']
			UpperCAmelCase_					:   Optional[int]   =		state_dict['final_layer.linear.weight']
			UpperCAmelCase_					:   List[str]   =		state_dict['final_layer.linear.bias']

			state_dict.pop('final_layer.linear.weight'						)
			state_dict.pop('final_layer.linear.bias'						)
			state_dict.pop('final_layer.adaLN_modulation.1.weight'						)
			state_dict.pop('final_layer.adaLN_modulation.1.bias'						)

			# DiT XL/2
			UpperCAmelCase_					:   Dict   =		TransformeraDModel(
			    sample_size=args.image_size // 8  , num_layers=28  , attention_head_dim=72  , in_channels=4  , out_channels=8  , patch_size=2  , attention_bias=__snake_case  , num_attention_heads=16  , activation_fn='gelu-approximate'  , num_embeds_ada_norm=1_000  , norm_type='ada_norm_zero'  , norm_elementwise_affine=__snake_case  , )
			transformer.load_state_dict(__snake_case  , strict=__snake_case						)

			UpperCAmelCase_					:   str   =		DDIMScheduler(
			    num_train_timesteps=1_000  , beta_schedule='linear'  , prediction_type='epsilon'  , clip_sample=__snake_case  , )

			UpperCAmelCase_					:   Tuple   =		AutoencoderKL.from_pretrained(args.vae_model						)

			UpperCAmelCase_					:   List[str]   =		DiTPipeline(transformer=__snake_case  , vae=__snake_case  , scheduler=__snake_case						)

			if args.save:
						pipeline.save_pretrained(args.checkpoint_path						)


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

		parser.add_argument(
		    '--image_size',
		    default=256,
		    type=int,
		    required=False,
		    help='Image size of pretrained model, either 256 or 512.',
		)
		parser.add_argument(
		    '--vae_model',
		    default='stabilityai/sd-vae-ft-ema',
		    type=str,
		    required=False,
		    help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
		)
		parser.add_argument(
		    '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
		)
		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
		)

		__UpperCAmelCase										=		parser.parse_args()
		main(args)














from __future__ import annotations

from typing import Any




def   lowercase__   (  *__snake_case					:							list[list[int]]						):


					'''simple docstring'''


					if all(_check_not_integer(__snake_case						) for m in matrix_s						):
										for i in matrix_s[1:]:
															_verify_matrix_sizes(matrix_s[0]  , __snake_case						)
										return [[sum(__snake_case						) for t in zip(*__snake_case						)] for m in zip(*__snake_case						)]
					raise TypeError('Expected a matrix, got int/list instead'						)




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					if (
					    _check_not_integer(__snake_case						)
					    and _check_not_integer(__snake_case						)
					    and _verify_matrix_sizes(__snake_case  , __snake_case						)
					):
										return [[i - j for i, j in zip(*__snake_case						)] for m in zip(__snake_case  , __snake_case						)]
					raise TypeError('Expected a matrix, got int/list instead'						)




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							int | float						):


					'''simple docstring'''


					return [[x * n for x in row] for row in matrix]




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					if _check_not_integer(__snake_case						) and _check_not_integer(__snake_case						):
										UpperCAmelCase_ ,						UpperCAmelCase_					:   str   =		_verify_matrix_sizes(__snake_case  , __snake_case						)

					if cols[0] != rows[1]:
										UpperCAmelCase_					:   Dict   =		(
										    'Cannot multiply matrix of dimensions '
										    F"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
										)
										raise ValueError(__snake_case						)
					return [
					    [sum(m * n for m, n in zip(__snake_case  , __snake_case						)						) for j in zip(*__snake_case						)] for i in matrix_a
					]




def   lowercase__   (  __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[Any]   =		int(__snake_case						)
					return [[int(row == column						) for column in range(__snake_case						)] for row in range(__snake_case						)]




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							bool = True						):


					'''simple docstring'''


					if _check_not_integer(__snake_case						):
										if return_map:
															return map(__snake_case  , zip(*__snake_case						)						)
										else:
															return list(map(__snake_case  , zip(*__snake_case						)						)						)
					raise TypeError('Expected a matrix, got int/list instead'						)




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		matrix[:row] + matrix[row + 1 :]
					return [row[:column] + row[column + 1 :] for row in minor]




def   lowercase__   (  __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					if len(__snake_case						) == 1:
										return matrix[0][0]

					return sum(
					    x * determinant(minor(__snake_case  , 0  , __snake_case						)						) * (-1) ** i
					    for i, x in enumerate(matrix[0]						)						)




def   lowercase__   (  __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[int]   =		determinant(__snake_case						)
					if det == 0:
										return None

					UpperCAmelCase_					:   List[str]   =		[
					    [determinant(minor(__snake_case  , __snake_case  , __snake_case						)						) for j in range(len(__snake_case						)						)]
					    for i in range(len(__snake_case						)						)
					]

					UpperCAmelCase_					:   List[str]   =		[
					    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]						)]
					    for row in range(len(__snake_case						)						)
					]
					UpperCAmelCase_					:   Tuple   =		list(transpose(__snake_case						)						)
					return scalar_multiply(__snake_case  , 1 / det						)




def   lowercase__   (  __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					return not isinstance(__snake_case  , __snake_case						) and not isinstance(matrix[0]  , __snake_case						)




def   lowercase__   (  __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					return len(__snake_case						), len(matrix[0]						)




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[Any]   =		_shape(__snake_case						) + _shape(__snake_case						)
					if shape[0] != shape[3] or shape[1] != shape[2]:
										UpperCAmelCase_					:   Optional[int]   =		(
										    'operands could not be broadcast together with shape '
										    F"({shape[0], shape[1]}), ({shape[2], shape[3]})"
										)
										raise ValueError(__snake_case						)
					return (shape[0], shape[2]), (shape[1], shape[3])




def   lowercase__   (  ):


					'''simple docstring'''


					UpperCAmelCase_					:   Tuple   =		[[12, 10], [3, 9]]
					UpperCAmelCase_					:   Optional[int]   =		[[3, 4], [7, 4]]
					UpperCAmelCase_					:   List[str]   =		[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
					UpperCAmelCase_					:   str   =		[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
					print(F"Add Operation, {add(__snake_case  , __snake_case						) = } \n"						)
					print(
					    F"Multiply Operation, {multiply(__snake_case  , __snake_case						) = } \n"  , )
					print(F"Identity: {identity(5						)}\n"						)
					print(F"Minor of {matrix_c} = {minor(__snake_case  , 1  , 2						)} \n"						)
					print(F"Determinant of {matrix_b} = {determinant(__snake_case						)} \n"						)
					print(F"Inverse of {matrix_d} = {inverse(__snake_case						)}\n"						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






