



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 							A     (						__UpperCAmelCase      )			->			List[str]:



						'''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 							A     (						__UpperCAmelCase      )			->			Optional[Any]:



						'''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__":
							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

import sys







class 				a_    :

						def __init__(       self      :Dict  ,		_lowercase      :str  ,		_lowercase      :int)						->   List[Any]:
												UpperCAmelCase_					=	letter
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	{}




						def __repr__(       self      :str)						->   str:
												return f"{self.letter}:{self.freq}"







class 				a_    :

						def __init__(       self      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :Letter | TreeNode  ,		_lowercase      :Letter | TreeNode)						->   Dict:
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	left
												UpperCAmelCase_					=	right







def 							A     (						__UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						UpperCAmelCase_					=	{}
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		UpperCAmelCase_					=	chars[c] + 1 if c in chars else 1
						return sorted((Letter(__UpperCAmelCase				,   __UpperCAmelCase      ) for c, f in chars.items())				,   key=lambda __UpperCAmelCase      : x.freq      )







def 							A     (						__UpperCAmelCase      )			->			Letter | TreeNode:



						'''simple docstring'''






						UpperCAmelCase_					=	letters  # type: ignore
						while len(__UpperCAmelCase      ) > 1:
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	left.freq + right.freq
												UpperCAmelCase_					=	TreeNode(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
												response.append(__UpperCAmelCase      )
												response.sort(key=lambda __UpperCAmelCase      : x.freq      )
						return response[0]







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						if isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ):
												UpperCAmelCase_					=	bitstring
												return [root]
						UpperCAmelCase_					=	root  # type: ignore
						UpperCAmelCase_					=	[]
						letters += traverse_tree(treenode.left				,   bitstring + '''0'''      )
						letters += traverse_tree(treenode.right				,   bitstring + '''1'''      )
						return letters







def 							A     (						__UpperCAmelCase      )			->			None:



						'''simple docstring'''






						UpperCAmelCase_					=	parse_file(__UpperCAmelCase      )
						UpperCAmelCase_					=	build_tree(__UpperCAmelCase      )
						UpperCAmelCase_					=	{
						    k: v for letter in traverse_tree(__UpperCAmelCase				,   ''''''      ) for k, v in letter.bitstring.items()
						}
						print(f"Huffman Coding  of {file_path}: "      )
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		print(letters[c]				,   end=''' '''      )
						print()


if __name__ == "__main__":
							# pass the file path to the huffman function
							huffman(sys.argv[1])





