





'''simple docstring'''



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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



def  lowercase__(		_UpperCamelCase  : Any				)->   int:






					"""simple docstring"""

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



def  lowercase__(		_UpperCamelCase  : Dict				)->   str:






					"""simple docstring"""

					_UpperCamelCase											=   download_model(pretrained_models[args.image_size]				)

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

										_UpperCamelCase											=   state_dict[
										    f"blocks.{depth}.adaLN_modulation.1.weight"
										]
										_UpperCamelCase											=   state_dict[
										    f"blocks.{depth}.adaLN_modulation.1.bias"
										]

										_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.weight"] ,   3 ,   dim=0				)
										_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.bias"] ,   3 ,   dim=0				)

										_UpperCamelCase											=   q
										_UpperCamelCase											=   q_bias
										_UpperCamelCase											=   k
										_UpperCamelCase											=   k_bias
										_UpperCamelCase											=   v
										_UpperCamelCase											=   v_bias

										_UpperCamelCase											=   state_dict[
										    f"blocks.{depth}.attn.proj.weight"
										]
										_UpperCamelCase											=   state_dict[f"blocks.{depth}.attn.proj.bias"]

										_UpperCamelCase											=   state_dict[f"blocks.{depth}.mlp.fc1.weight"]
										_UpperCamelCase											=   state_dict[f"blocks.{depth}.mlp.fc1.bias"]
										_UpperCamelCase											=   state_dict[f"blocks.{depth}.mlp.fc2.weight"]
										_UpperCamelCase											=   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											=   state_dict["final_layer.adaLN_modulation.1.weight"]
					_UpperCamelCase											=   state_dict["final_layer.adaLN_modulation.1.bias"]
					_UpperCamelCase											=   state_dict["final_layer.linear.weight"]
					_UpperCamelCase											=   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											=   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=_UpperCamelCase ,   num_attention_heads=16 ,   activation_fn="gelu-approximate" ,   num_embeds_ada_norm=1000 ,   norm_type="ada_norm_zero" ,   norm_elementwise_affine=_UpperCamelCase ,   )
					transformer.load_state_dict(_UpperCamelCase ,   strict=_UpperCamelCase				)

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

					_UpperCamelCase											=   AutoencoderKL.from_pretrained(args.vae_model				)

					_UpperCamelCase											=   DiTPipeline(transformer=_UpperCamelCase ,   vae=_UpperCamelCase ,   scheduler=_UpperCamelCase				)

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


if __name__ == "__main__":
			snake_case_   :  str								=      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.'''
			)

			snake_case_   :  Any								=      parser.parse_args()
			main(args)










'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[list[int]] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int | float				)->   None:






					"""simple docstring"""

					_UpperCamelCase				,	_UpperCamelCase											=   m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f"Key {key} found at row- {i + 1} column- {j + 1}"				)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f"Key {key} not found"				)



def  lowercase__(		)->   None:






					"""simple docstring"""

					_UpperCamelCase											=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					_UpperCamelCase											=   int(input("Enter the element to be searched:"				)				)
					print(_UpperCamelCase				)
					search_in_a_sorted_matrix(_UpperCamelCase ,   len(_UpperCamelCase				) ,   len(mat[0]				) ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



