





"""simple docstring"""





import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       F'''pretrained_models/{model_name}'''
 if not os.path.isfile(lowercase_		):
  os.makedirs('''pretrained_models''' ,      exist_ok=lowercase_		)
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
  download_url(lowercase_ ,      '''pretrained_models'''		)
 __SCREAMING_SNAKE_CASE :			int												=       torch.load(lowercase_ ,      map_location=lambda lowercase_ ,      lowercase_		: storage		)
 return model




def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[str]												=       download_model(pretrained_models[args.image_size]		)

 __SCREAMING_SNAKE_CASE :			int												=       state_dict['''x_embedder.proj.weight''']
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       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		):
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       state_dict[
      '''t_embedder.mlp.0.weight'''
  ]
  __SCREAMING_SNAKE_CASE :			List[Any]												=       state_dict[
      '''t_embedder.mlp.0.bias'''
  ]
  __SCREAMING_SNAKE_CASE :			Any												=       state_dict[
      '''t_embedder.mlp.2.weight'''
  ]
  __SCREAMING_SNAKE_CASE :			Dict												=       state_dict[
      '''t_embedder.mlp.2.bias'''
  ]
  __SCREAMING_SNAKE_CASE :			List[str]												=       state_dict[
      '''y_embedder.embedding_table.weight'''
  ]

  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       state_dict[
      F'''blocks.{depth}.adaLN_modulation.1.weight'''
  ]
  __SCREAMING_SNAKE_CASE :			Tuple												=       state_dict[
      F'''blocks.{depth}.adaLN_modulation.1.bias'''
  ]

  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight'''] ,      3 ,      dim=0		)
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias'''] ,      3 ,      dim=0		)

  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       q
  __SCREAMING_SNAKE_CASE :			List[Any]												=       q_bias
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       k
  __SCREAMING_SNAKE_CASE :			Any												=       k_bias
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       v
  __SCREAMING_SNAKE_CASE :			Any												=       v_bias

  __SCREAMING_SNAKE_CASE :			Dict												=       state_dict[
      F'''blocks.{depth}.attn.proj.weight'''
  ]
  __SCREAMING_SNAKE_CASE :			int												=       state_dict[F'''blocks.{depth}.attn.proj.bias''']

  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
  __SCREAMING_SNAKE_CASE :			str												=       state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
  __SCREAMING_SNAKE_CASE :			Tuple												=       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'''		)

 __SCREAMING_SNAKE_CASE :			List[Any]												=       state_dict['''final_layer.adaLN_modulation.1.weight''']
 __SCREAMING_SNAKE_CASE :			str												=       state_dict['''final_layer.adaLN_modulation.1.bias''']
 __SCREAMING_SNAKE_CASE :			List[Any]												=       state_dict['''final_layer.linear.weight''']
 __SCREAMING_SNAKE_CASE :			Dict												=       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
 __SCREAMING_SNAKE_CASE :			int												=       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_		)

 __SCREAMING_SNAKE_CASE :			List[Any]												=       DDIMScheduler(
     num_train_timesteps=1000 ,      beta_schedule='''linear''' ,      prediction_type='''epsilon''' ,      clip_sample=lowercase_ ,      )

 __SCREAMING_SNAKE_CASE :			Dict												=       AutoencoderKL.from_pretrained(args.vae_model		)

 __SCREAMING_SNAKE_CASE :			str												=       DiTPipeline(transformer=lowercase_ ,      vae=lowercase_ ,      scheduler=lowercase_		)

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


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








"""simple docstring"""





from typing import TYPE_CHECKING

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


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MobileNetV1ForImageClassification''',
        '''MobileNetV1Model''',
        '''MobileNetV1PreTrainedModel''',
        '''load_tf_weights_in_mobilenet_v1''',
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileNetVaConfig,
        MobileNetVaOnnxConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

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

