

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A     						=      {5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}



def  snake_case_(_UpperCamelCase	)				->      int:



  """simple docstring"""






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



def  snake_case_(_UpperCamelCase	)				->      Dict:



  """simple docstring"""






  _snake_case		=				download_model(pretrained_models[args.image_size]	)

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

    _snake_case		=				state_dict[
        F"""blocks.{depth}.adaLN_modulation.1.weight"""
    ]
    _snake_case		=				state_dict[
        F"""blocks.{depth}.adaLN_modulation.1.bias"""
    ]

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

    _snake_case		=				q
    _snake_case		=				q_bias
    _snake_case		=				k
    _snake_case		=				k_bias
    _snake_case		=				v
    _snake_case		=				v_bias

    _snake_case		=				state_dict[
        F"""blocks.{depth}.attn.proj.weight"""
    ]
    _snake_case		=				state_dict[F"""blocks.{depth}.attn.proj.bias"""]

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

  _snake_case		=				state_dict['''final_layer.adaLN_modulation.1.weight''']
  _snake_case		=				state_dict['''final_layer.adaLN_modulation.1.bias''']
  _snake_case		=				state_dict['''final_layer.linear.weight''']
  _snake_case		=				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
  _snake_case		=				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=1_000					,			norm_type='''ada_norm_zero'''					,			norm_elementwise_affine=_UpperCamelCase					,			)
  transformer.load_state_dict(_UpperCamelCase					,			strict=_UpperCamelCase	)

  _snake_case		=				DDIMScheduler(
      num_train_timesteps=1_000					,			beta_schedule='''linear'''					,			prediction_type='''epsilon'''					,			clip_sample=_UpperCamelCase					,			)

  _snake_case		=				AutoencoderKL.from_pretrained(args.vae_model	)

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

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


if __name__ == "__main__":
 __A     						=      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     						=      parser.parse_args()
 main(args)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A     						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

