import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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


def SCREAMING_SNAKE_CASE					(  lowercase_:						Dict					):
       _UpperCamelCase  :							Optional[int]	    =	F"pretrained_models/{model_name}"
       if not os.path.isfile(lowercase__					):
              os.makedirs('pretrained_models'		,    exist_ok=lowercase__					)
              _UpperCamelCase  :							Dict	    =	F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
              download_url(lowercase__		,    'pretrained_models'					)
       _UpperCamelCase  :							Optional[Any]	    =	torch.load(lowercase__		,    map_location=lambda lowercase_		,    lowercase_					: storage					)
       return model


def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):
       _UpperCamelCase  :							Union[str, Any]	    =	download_model(pretrained_models[args.image_size]					)

       _UpperCamelCase  :							Any	    =	state_dict['x_embedder.proj.weight']
       _UpperCamelCase  :							str	    =	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  :							List[Any]	    =	state_dict[
                  't_embedder.mlp.0.weight'
              ]
              _UpperCamelCase  :							Tuple	    =	state_dict[
                  't_embedder.mlp.0.bias'
              ]
              _UpperCamelCase  :							List[str]	    =	state_dict[
                  't_embedder.mlp.2.weight'
              ]
              _UpperCamelCase  :							str	    =	state_dict[
                  't_embedder.mlp.2.bias'
              ]
              _UpperCamelCase  :							Union[str, Any]	    =	state_dict[
                  'y_embedder.embedding_table.weight'
              ]

              _UpperCamelCase  :							Union[str, Any]	    =	state_dict[
                  F"blocks.{depth}.adaLN_modulation.1.weight"
              ]
              _UpperCamelCase  :							Optional[Any]	    =	state_dict[
                  F"blocks.{depth}.adaLN_modulation.1.bias"
              ]

              _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							List[str]	    =	torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.weight"]		,    3		,    dim=0					)
              _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.bias"]		,    3		,    dim=0					)

              _UpperCamelCase  :							List[Any]	    =	q
              _UpperCamelCase  :							Any	    =	q_bias
              _UpperCamelCase  :							str	    =	k
              _UpperCamelCase  :							Tuple	    =	k_bias
              _UpperCamelCase  :							Union[str, Any]	    =	v
              _UpperCamelCase  :							Union[str, Any]	    =	v_bias

              _UpperCamelCase  :							Union[str, Any]	    =	state_dict[
                  F"blocks.{depth}.attn.proj.weight"
              ]
              _UpperCamelCase  :							str	    =	state_dict[F"blocks.{depth}.attn.proj.bias"]

              _UpperCamelCase  :							str	    =	state_dict[F"blocks.{depth}.mlp.fc1.weight"]
              _UpperCamelCase  :							str	    =	state_dict[F"blocks.{depth}.mlp.fc1.bias"]
              _UpperCamelCase  :							int	    =	state_dict[F"blocks.{depth}.mlp.fc2.weight"]
              _UpperCamelCase  :							List[Any]	    =	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  :							Any	    =	state_dict['final_layer.adaLN_modulation.1.weight']
       _UpperCamelCase  :							Optional[Any]	    =	state_dict['final_layer.adaLN_modulation.1.bias']
       _UpperCamelCase  :							Union[str, Any]	    =	state_dict['final_layer.linear.weight']
       _UpperCamelCase  :							List[str]	    =	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  :							Optional[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__					)

       _UpperCamelCase  :							Optional[Any]	    =	DDIMScheduler(
           num_train_timesteps=1000		,    beta_schedule='linear'		,    prediction_type='epsilon'		,    clip_sample=lowercase__		,    )

       _UpperCamelCase  :							Union[str, Any]	    =	AutoencoderKL.from_pretrained(args.vae_model					)

       _UpperCamelCase  :							str	    =	DiTPipeline(transformer=lowercase__		,    vae=lowercase__		,    scheduler=lowercase__					)

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


if __name__ == "__main__":
 UpperCAmelCase      		=					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.'''
 )

 UpperCAmelCase      		=					parser.parse_args()
 main(args)











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				:				Union[str, Any]					=		{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""GPT2TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[int]					=		[
     """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPT2DoubleHeadsModel""",
     """GPT2ForQuestionAnswering""",
     """GPT2ForSequenceClassification""",
     """GPT2ForTokenClassification""",
     """GPT2LMHeadModel""",
     """GPT2Model""",
     """GPT2PreTrainedModel""",
     """load_tf_weights_in_gpt2""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[Any]					=		[
     """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFGPT2DoubleHeadsModel""",
     """TFGPT2ForSequenceClassification""",
     """TFGPT2LMHeadModel""",
     """TFGPT2MainLayer""",
     """TFGPT2Model""",
     """TFGPT2PreTrainedModel""",
 ]

try:
 if not is_keras_nlp_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Any					=		["""TFGPT2Tokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
 from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
 from .tokenization_gpta import GPTaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_fast import GPTaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpta import (
      GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTaDoubleHeadsModel,
      GPTaForQuestionAnswering,
      GPTaForSequenceClassification,
      GPTaForTokenClassification,
      GPTaLMHeadModel,
      GPTaModel,
      GPTaPreTrainedModel,
      load_tf_weights_in_gpta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gpta import (
      TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGPTaDoubleHeadsModel,
      TFGPTaForSequenceClassification,
      TFGPTaLMHeadModel,
      TFGPTaMainLayer,
      TFGPTaModel,
      TFGPTaPreTrainedModel,
  )

 try:
  if not is_keras_nlp_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_tf import TFGPTaTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
 import sys

 __A				:				Tuple					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

