






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














def   lowercase__   (  __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					return len_board * len_board_column * row + column




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


					'''simple docstring'''


					if board[row][column] != word[word_index]:
										return False

					if word_index == len(__snake_case						) - 1:
										return True

					UpperCAmelCase_					:   Dict   =		[(0, 1), (0, -1), (-1, 0), (1, 0)]
					UpperCAmelCase_					:   Union[str, Any]   =		len(__snake_case						)
					UpperCAmelCase_					:   List[Any]   =		len(board[0]						)
					for direction in traverts_directions:
										UpperCAmelCase_					:   Optional[int]   =		row + direction[0]
										UpperCAmelCase_					:   Optional[int]   =		column + direction[1]
										if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
															continue

										UpperCAmelCase_					:   Tuple   =		get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)
										if key in visited_points_set:
															continue

										visited_points_set.add(__snake_case						)
										if exits_word(__snake_case  , __snake_case  , __snake_case  , __snake_case  , word_index + 1  , __snake_case						):
															return True

										visited_points_set.remove(__snake_case						)

					return False




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


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		(
					    'The board should be a non empty matrix of single chars strings.'
					)

					UpperCAmelCase_					:   Optional[Any]   =		len(__snake_case						)
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(__snake_case						)

					for row in board:
										if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
															raise ValueError(__snake_case						)

										for item in row:
															if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) != 1:
																				raise ValueError(__snake_case						)

    # Validate word
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(
										    'The word parameter should be a string of length greater than 0.'						)

					UpperCAmelCase_					:   Any   =		len(board[0]						)
					for i in range(__snake_case						):
										for j in range(__snake_case						):
															if exits_word(
															    __snake_case  , __snake_case  , __snake_case  , __snake_case  , 0  , {get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)}						):
																				return True

					return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()






