


'''simple docstring'''
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__magic_name__   :   str  							={5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				__magic_name__	  = F'pretrained_models/{model_name}'
				if not os.path.isfile(lowerCamelCase_						):
								os.makedirs("pretrained_models"	,  exist_ok=lowerCamelCase_						)
								__magic_name__	  = F'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
								download_url(lowerCamelCase_	,  "pretrained_models"						)
				__magic_name__	  = torch.load(lowerCamelCase_	,  map_location=lambda lowerCamelCase_	,  lowerCamelCase_						: storage						)
				return model




def      __snake_case       (       lowerCamelCase_ :				Optional[int]						):
				'''simple docstring'''




				__magic_name__	  = download_model(pretrained_models[args.image_size]						)

				__magic_name__	  = state_dict["x_embedder.proj.weight"]
				__magic_name__	  = 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						):
								__magic_name__	  = state_dict[
								    "t_embedder.mlp.0.weight"
								]
								__magic_name__	  = state_dict[
								    "t_embedder.mlp.0.bias"
								]
								__magic_name__	  = state_dict[
								    "t_embedder.mlp.2.weight"
								]
								__magic_name__	  = state_dict[
								    "t_embedder.mlp.2.bias"
								]
								__magic_name__	  = state_dict[
								    "y_embedder.embedding_table.weight"
								]

								__magic_name__	  = state_dict[
								    F'blocks.{depth}.adaLN_modulation.1.weight'
								]
								__magic_name__	  = state_dict[
								    F'blocks.{depth}.adaLN_modulation.1.bias'
								]

								__magic_name__  ,      __magic_name__  ,      __magic_name__	  = torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.weight']	,  3	,  dim=0						)
								__magic_name__  ,      __magic_name__  ,      __magic_name__	  = torch.chunk(state_dict[F'blocks.{depth}.attn.qkv.bias']	,  3	,  dim=0						)

								__magic_name__	  = q
								__magic_name__	  = q_bias
								__magic_name__	  = k
								__magic_name__	  = k_bias
								__magic_name__	  = v
								__magic_name__	  = v_bias

								__magic_name__	  = state_dict[
								    F'blocks.{depth}.attn.proj.weight'
								]
								__magic_name__	  = state_dict[F'blocks.{depth}.attn.proj.bias']

								__magic_name__	  = state_dict[F'blocks.{depth}.mlp.fc1.weight']
								__magic_name__	  = state_dict[F'blocks.{depth}.mlp.fc1.bias']
								__magic_name__	  = state_dict[F'blocks.{depth}.mlp.fc2.weight']
								__magic_name__	  = 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"						)

				__magic_name__	  = state_dict["final_layer.adaLN_modulation.1.weight"]
				__magic_name__	  = state_dict["final_layer.adaLN_modulation.1.bias"]
				__magic_name__	  = state_dict["final_layer.linear.weight"]
				__magic_name__	  = 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
				__magic_name__	  = 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=lowerCamelCase_	,  num_attention_heads=16	,  activation_fn="gelu-approximate"	,  num_embeds_ada_norm=1000	,  norm_type="ada_norm_zero"	,  norm_elementwise_affine=lowerCamelCase_	,  )
				transformer.load_state_dict(lowerCamelCase_	,  strict=lowerCamelCase_						)

				__magic_name__	  = DDIMScheduler(
				    num_train_timesteps=1000	,  beta_schedule="linear"	,  prediction_type="epsilon"	,  clip_sample=lowerCamelCase_	,  )

				__magic_name__	  = AutoencoderKL.from_pretrained(args.vae_model						)

				__magic_name__	  = DiTPipeline(transformer=lowerCamelCase_	,  vae=lowerCamelCase_	,  scheduler=lowerCamelCase_						)

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


if __name__ == "__main__":
						__magic_name__   :   Optional[int]  							=argparse.ArgumentParser()

						parser.add_argument(
						    '--image_size',
						    default=2_56,
						    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.'
						)

						__magic_name__   :   List[Any]  							=parser.parse_args()
						main(args)







'''simple docstring'''
import numpy as np

__magic_name__   :   str  							=[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Union[str, Any]  )							->					None:
										__magic_name__	  = np.array(_lowerCamelCase  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					str  )							->					np.ndarray:
										__magic_name__  ,      __magic_name__	  = np.where(letter == self.SQUARE  )
										__magic_name__	  = np.concatenate([indexa + 1, indexa + 1]  )
										return indexes
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					int  )							->					str:
										return self.SQUARE[indexa - 1, indexa - 1]
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.lower()
										__magic_name__	  = message.replace("j"	,				"i"  )

										__magic_name__	  = ""
										for letter_index in range(len(_lowerCamelCase  )  ):
														if message[letter_index] != " ":
																		__magic_name__	  = self.letter_to_numbers(message[letter_index]  )
																		__magic_name__	  = encoded_message + str(numbers[0]  ) + str(numbers[1]  )
														elif message[letter_index] == " ":
																		__magic_name__	  = encoded_message + " "

										return encoded_message

						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.replace(" "	,				"  "  )
										__magic_name__	  = ""
										for numbers_index in range(int(len(_lowerCamelCase  ) / 2  )  ):
														if message[numbers_index * 2] != " ":
																		__magic_name__	  = message[numbers_index * 2]
																		__magic_name__	  = message[numbers_index * 2 + 1]

																		__magic_name__	  = self.numbers_to_letter(int(_lowerCamelCase  )	,				int(_lowerCamelCase  )  )
																		__magic_name__	  = decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		__magic_name__	  = decoded_message + " "

										return decoded_message



