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

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCAmelCase :  Optional[int]      =      {5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}







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







def 						A_(			A		:						Any):
							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=A						,			num_attention_heads=16						,			activation_fn='gelu-approximate'						,			num_embeds_ada_norm=1000						,			norm_type='ada_norm_zero'						,			norm_elementwise_affine=A						,			)
							transformer.load_state_dict(A						,			strict=A)

							UpperCamelCase					       =			DDIMScheduler(
							    num_train_timesteps=1000						,			beta_schedule='linear'						,			prediction_type='epsilon'						,			clip_sample=A						,			)

							UpperCamelCase					       =			AutoencoderKL.from_pretrained(args.vae_model)

							UpperCamelCase					       =			DiTPipeline(transformer=A						,			vae=A						,			scheduler=A)

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


if __name__ == "__main__":
						lowerCAmelCase :  List[str]      =      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.'
						)

						lowerCAmelCase :  Optional[int]      =      parser.parse_args()
						main(args)






'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm







def 						A_(			A		:						int						,			A		:						bool = False):
							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(A)
							np.random.seed(A)
							torch.manual_seed(A)
							if is_xpu_available():
														torch.xpu.manual_seed_all(A)
							elif is_npu_available():
														torch.npu.manual_seed_all(A)
							else:
														torch.cuda.manual_seed_all(A)
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(A)







def 						A_(			A		:						Optional[RNGType] = None						,			A		:						Optional[torch.Generator] = None):
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														UpperCamelCase					       =			torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														UpperCamelCase					       =			torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														UpperCamelCase					       =			torch.tensor(xm.get_rng_state())
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														UpperCamelCase					       =			torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														UpperCamelCase					       =			torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														UpperCamelCase					       =			generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							UpperCamelCase					       =			AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														UpperCamelCase					       =			rng_state.to(xm.xla_device())
														xm.collective_broadcast([rng_state])
														xm.mark_step()
														UpperCamelCase					       =			rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														UpperCamelCase					       =			rng_state.to(state.device)
														torch.distributed.broadcast(A						,			0)
														UpperCamelCase					       =			rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(A						,			0)

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(A)
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(A)
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(A)
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(A)
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item())
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(A)







def 						A_(			A		:						List[Union[str, RNGType]]						,			A		:						Optional[torch.Generator] = None):
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(A)						,			generator=A)




