






'''simple docstring'''





import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A				:				List[str]					=		{5_12: """DiT-XL-2-512x512.pt""", 2_56: """DiT-XL-2-256x256.pt"""}






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






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  download_model(pretrained_models[args.image_size])

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

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

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

  lowerCamelCase__	=  q
  lowerCamelCase__	=  q_bias
  lowerCamelCase__	=  k
  lowerCamelCase__	=  k_bias
  lowerCamelCase__	=  v
  lowerCamelCase__	=  v_bias

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

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

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

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

 lowerCamelCase__	=  AutoencoderKL.from_pretrained(args.vae_model)

 lowerCamelCase__	=  DiTPipeline(transformer=lowercase__					,  vae=lowercase__					,  scheduler=lowercase__)

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


if __name__ == "__main__":
 __A				:				Any					=		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."""
 )

 __A				:				int					=		parser.parse_args()
 main(args)









'''simple docstring'''





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch






def  lowerCamelCase_  (     ):
 lowerCamelCase__	=  Dataset.from_dict({"i": range(17)})
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  3
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(lowercase__) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






@pytest.mark.parametrize("shards_per_node"					,  [1, 2, 3])
def  lowerCamelCase_  (     lowercase__):
 def gen(lowercase__):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  shards_per_node * world_size
 lowerCamelCase__	=  {"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(lowercase__)]}
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__					,  gen_kwargs=lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 assert full_ds.n_shards == world_size * shards_per_node
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(lowercase__)) for ds in datasets_per_rank) == full_size
 assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size






def  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  2
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))

 lowerCamelCase__	=  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle(seed=42)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle()

 lowerCamelCase__	=  split_dataset_by_node(full_ds.shuffle(seed=42)					,  rank=0					,  world_size=lowercase__)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(full_ds.shuffle()					,  rank=0					,  world_size=lowercase__)






@pytest.mark.parametrize("streaming"					,  [False, True])
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  2
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())






@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,  [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,  )
@require_torch
@pytest.mark.skipif(os.name == "nt"					,  reason="execute_subprocess_async doesn't support windows")
@pytest.mark.integration
def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  True
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())

