




"""simple docstring"""




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A       :						Union[str, Any]												=			{512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
def     A_	(						snake_case_   :				int			):







    '''simple docstring'''
    UpperCamelCase :      str      				=							f'pretrained_models/{model_name}'
    if not os.path.isfile(lowerCamelCase_			):
        os.makedirs("""pretrained_models"""  ,exist_ok=lowerCamelCase_			)
        UpperCamelCase :      Optional[int]      				=							f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
        download_url(lowerCamelCase_  ,"""pretrained_models"""			)
    UpperCamelCase :      List[Any]      				=							torch.load(lowerCamelCase_  ,map_location=lambda snake_case_  ,snake_case_			: storage			)
    return model
def     A_	(						snake_case_   :				Any			):







    '''simple docstring'''
    UpperCamelCase :      Union[str, Any]      				=							download_model(pretrained_models[args.image_size]			)

    UpperCamelCase :      int      				=							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(2_8			):
        UpperCamelCase :      Dict      				=							state_dict[
            't_embedder.mlp.0.weight'
        ]
        UpperCamelCase :      int      				=							state_dict[
            't_embedder.mlp.0.bias'
        ]
        UpperCamelCase :      List[Any]      				=							state_dict[
            't_embedder.mlp.2.weight'
        ]
        UpperCamelCase :      List[Any]      				=							state_dict[
            't_embedder.mlp.2.bias'
        ]
        UpperCamelCase :      List[str]      				=							state_dict[
            'y_embedder.embedding_table.weight'
        ]

        UpperCamelCase :      Union[str, Any]      				=							state_dict[
            f'blocks.{depth}.adaLN_modulation.1.weight'
        ]
        UpperCamelCase :      Any      				=							state_dict[
            f'blocks.{depth}.adaLN_modulation.1.bias'
        ]

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

        UpperCamelCase :      Optional[Any]      				=							q
        UpperCamelCase :      Any      				=							q_bias
        UpperCamelCase :      Dict      				=							k
        UpperCamelCase :      List[str]      				=							k_bias
        UpperCamelCase :      str      				=							v
        UpperCamelCase :      Optional[Any]      				=							v_bias

        UpperCamelCase :      Union[str, Any]      				=							state_dict[
            f'blocks.{depth}.attn.proj.weight'
        ]
        UpperCamelCase :      Union[str, Any]      				=							state_dict[f'blocks.{depth}.attn.proj.bias']

        UpperCamelCase :      str      				=							state_dict[f'blocks.{depth}.mlp.fc1.weight']
        UpperCamelCase :      Optional[Any]      				=							state_dict[f'blocks.{depth}.mlp.fc1.bias']
        UpperCamelCase :      Any      				=							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 :      Any      				=							state_dict['final_layer.adaLN_modulation.1.weight']
    UpperCamelCase :      int      				=							state_dict['final_layer.adaLN_modulation.1.bias']
    UpperCamelCase :      Optional[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=2_8  ,attention_head_dim=7_2  ,in_channels=4  ,out_channels=8  ,patch_size=2  ,attention_bias=lowerCamelCase_  ,num_attention_heads=1_6  ,activation_fn="""gelu-approximate"""  ,num_embeds_ada_norm=1_0_0_0  ,norm_type="""ada_norm_zero"""  ,norm_elementwise_affine=lowerCamelCase_  ,)
    transformer.load_state_dict(lowerCamelCase_  ,strict=lowerCamelCase_			)

    UpperCamelCase :      Optional[int]      				=							DDIMScheduler(
        num_train_timesteps=1_0_0_0  ,beta_schedule="""linear"""  ,prediction_type="""epsilon"""  ,clip_sample=lowerCamelCase_  ,)

    UpperCamelCase :      Any      				=							AutoencoderKL.from_pretrained(args.vae_model			)

    UpperCamelCase :      Dict      				=							DiTPipeline(transformer=lowerCamelCase_  ,vae=lowerCamelCase_  ,scheduler=lowerCamelCase_			)

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


if __name__ == "__main__":
  __A       :						Union[str, Any]												=			argparse.ArgumentParser()

  parser.add_argument(
      '''--image_size''',
      default=256,
      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       :						List[str]												=			parser.parse_args()
  main(args)





'''simple docstring'''


import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


snake_case__     :			Optional[int]      			=  logging.getLogger(__name__)


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   List[Any]   =      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'   )
     parser.add_argument(
         '--dataset_name'	,     type=lowerCamelCase_	,     default='wikitext'	,     help='Name of the training. Explore datasets at: hf.co/datasets.'	,     )
     parser.add_argument(
         '--dataset_config'	,     type=lowerCamelCase_	,     default='wikitext-103-raw-v1'	,     help='Configuration name of the dataset.'   )
     parser.add_argument(
         '--batch_size'	,     type=lowerCamelCase_	,     default=1000	,     help='Batch size during training.'	,     )
     parser.add_argument(
         '--vocab_size'	,     type=lowerCamelCase_	,     default=10048	,     help='Size of the desired vocabulary.'	,     )
     parser.add_argument(
         '--limit'	,     default=lowerCamelCase_	,     type=lowerCamelCase_	,     help='Limit the number of shards (used for debugging).'	,     )
     parser.add_argument(
         '--export_to_hub'	,     action='store_true'	,     )

     UpperCAmelCase_							:   str   =      parser.parse_args()
     return args


def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict   ):



     """simple docstring"""





     UpperCAmelCase_							:   List[Any]   =      datasets.load_dataset(args.dataset_name	,     args.dataset_config	,     split='train'   )

     if args.limit is not None:
          UpperCAmelCase_							:   Union[str, Any]   =      min(len(lowerCamelCase_   )	,     args.limit   )
          UpperCAmelCase_							:   int   =      dataset.select(range(lowerCamelCase_   )   )
          logger.info(F'''Limiting the dataset to {args.limit} entries.'''   )

     def batch_iterator():
          for i in range(0	,     len(lowerCamelCase_   )	,     args.batch_size   ):
               yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
     UpperCAmelCase_							:   Optional[Any]   =      Tokenizer(Unigram()   )
     UpperCAmelCase_							:   Union[str, Any]   =      normalizers.Sequence([normalizers.Replace('``'	,     '"'   ), normalizers.Replace('\'\''	,     '"'   )]   )
     UpperCAmelCase_							:   Any   =      pre_tokenizers.Metaspace()

     # Prepare the trainer.
     UpperCAmelCase_							:   Tuple   =      UnigramTrainer(
         unk_token='<unk>'	,     special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']	,     vocab_size=args.vocab_size	,     )

     logger.info('Training the tokenizer.'   )
     tokenizer.train_from_iterator(batch_iterator()	,     trainer=lowerCamelCase_   )
     logger.info('Tokenizer training complete!'   )

     UpperCAmelCase_							:   List[str]   =      tokenizer.token_to_id('[CLS]'   )
     UpperCAmelCase_							:   Optional[int]   =      tokenizer.token_to_id('[SEP]'   )
     UpperCAmelCase_							:   Optional[Any]   =      processors.TemplateProcessing(
         single='[CLS]:0 $A:0 [SEP]:0'	,     pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'	,     special_tokens=[
             ('[CLS]', cls_token_id),
             ('[SEP]', sep_token_id),
         ]	,     )
     UpperCAmelCase_							:   Any   =      decoders.Metaspace()

     if args.export_to_hub:
          logger.info('Exporting the trained tokenzier to Hub.'   )
          UpperCAmelCase_							:   str   =      AlbertTokenizerFast(tokenizer_object=lowerCamelCase_   )
          new_tokenizer.push_to_hub('unigram-tokenizer-dataset'   )


if __name__ == "__main__":
 snake_case__     :			Optional[Any]      			=  parse_args()
 main(args)






