

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_SCREAMING_SNAKE_CASE							 =			{5_1_2: '''DiT-XL-2-512x512.pt''', 2_5_6: '''DiT-XL-2-256x256.pt'''}





def  _snake_case		(snake_case_					:     List[Any]  )							->		Optional[Any]:
  __UpperCAmelCase	:  Dict		=     F"pretrained_models/{model_name}"
  if not os.path.isfile(__A  ):
    os.makedirs('pretrained_models'    ,       exist_ok=__A  )
    __UpperCAmelCase	:  Any		=     F"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
    download_url(__A    ,       'pretrained_models'  )
  __UpperCAmelCase	:  Optional[int]		=     torch.load(__A    ,       map_location=lambda snake_case_    ,       snake_case_  : storage  )
  return model





def  _snake_case		(snake_case_					:     Dict  )							->		Dict:
  __UpperCAmelCase	:  Union[str, Any]		=     download_model(pretrained_models[args.image_size]  )

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

    __UpperCAmelCase	:  int		=     state_dict[
        F"blocks.{depth}.adaLN_modulation.1.weight"
    ]
    __UpperCAmelCase	:  Tuple		=     state_dict[
        F"blocks.{depth}.adaLN_modulation.1.bias"
    ]

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

    __UpperCAmelCase	:  int		=     q
    __UpperCAmelCase	:  Any		=     q_bias
    __UpperCAmelCase	:  int		=     k
    __UpperCAmelCase	:  Tuple		=     k_bias
    __UpperCAmelCase	:  List[Any]		=     v
    __UpperCAmelCase	:  int		=     v_bias

    __UpperCAmelCase	:  int		=     state_dict[
        F"blocks.{depth}.attn.proj.weight"
    ]
    __UpperCAmelCase	:  List[str]		=     state_dict[F"blocks.{depth}.attn.proj.bias"]

    __UpperCAmelCase	:  List[Any]		=     state_dict[F"blocks.{depth}.mlp.fc1.weight"]
    __UpperCAmelCase	:  Tuple		=     state_dict[F"blocks.{depth}.mlp.fc1.bias"]
    __UpperCAmelCase	:  str		=     state_dict[F"blocks.{depth}.mlp.fc2.weight"]
    __UpperCAmelCase	:  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	:  int		=     state_dict['final_layer.adaLN_modulation.1.weight']
  __UpperCAmelCase	:  List[Any]		=     state_dict['final_layer.adaLN_modulation.1.bias']
  __UpperCAmelCase	:  Optional[int]		=     state_dict['final_layer.linear.weight']
  __UpperCAmelCase	:  Union[str, Any]		=     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	:  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=__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	:  List[Any]		=     DDIMScheduler(
      num_train_timesteps=1000    ,       beta_schedule='linear'    ,       prediction_type='epsilon'    ,       clip_sample=__A    ,       )

  __UpperCAmelCase	:  List[str]		=     AutoencoderKL.from_pretrained(args.vae_model  )

  __UpperCAmelCase	:  Optional[int]		=     DiTPipeline(transformer=__A    ,       vae=__A    ,       scheduler=__A  )

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


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE							 =			argparse.ArgumentParser()

      parser.add_argument(
          '''--image_size''',
          default=2_5_6,
          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.'''
      )

      _SCREAMING_SNAKE_CASE							 =			parser.parse_args()
      main(args)








from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


A__										=      [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

A__										=      get_logger(__name__)


A__										=      {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    task_template_dict.get("task"						)
    if task_name is None:
        logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(__A						)}"						)
        return None
    SCREAMING_SNAKE_CASE__         =    NAMEaTEMPLATE.get(__A						)
    return template.from_dict(__A						)


