





import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      __lowercase				    =      None

__lowercase				    =      logging.get_logger(__name__)

__lowercase				    =      {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

__lowercase				    =      {
    """vocab_file""": {
        """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"""
        ),
        """microsoft/deberta-v2-xxlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"""
        ),
    }
}

__lowercase				    =      {
    """microsoft/deberta-v2-xlarge""": 512,
    """microsoft/deberta-v2-xxlarge""": 512,
    """microsoft/deberta-v2-xlarge-mnli""": 512,
    """microsoft/deberta-v2-xxlarge-mnli""": 512,
}

__lowercase				    =      {
    """microsoft/deberta-v2-xlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xlarge-mnli""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge-mnli""": {"""do_lower_case""": False},
}

class 	__snake_case					(      _a ):






    '''simple docstring'''
    _lowerCAmelCase  =						VOCAB_FILES_NAMES
    _lowerCAmelCase  =						PRETRAINED_VOCAB_FILES_MAP
    _lowerCAmelCase  =						PRETRAINED_INIT_CONFIGURATION
    _lowerCAmelCase  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowerCAmelCase  =						DebertaVaTokenizer





    def __init__( self    :		Union[str, Any]      ,    snake_case    :		List[Any]=None      ,    snake_case    :		Optional[Any]=None      ,    snake_case    :		int=False      ,    snake_case    :		str=False      ,    snake_case    :		int="[CLS]"      ,    snake_case    :		List[str]="[SEP]"      ,    snake_case    :		int="[UNK]"      ,    snake_case    :		Union[str, Any]="[SEP]"      ,    snake_case    :		Dict="[PAD]"      ,    snake_case    :		List[str]="[CLS]"      ,    snake_case    :		Optional[Any]="[MASK]"      ,    **snake_case    :		List[Any]      ,    ):

      """simple docstring"""





      super().__init__(
          snake_case      ,    tokenizer_file=snake_case      ,    do_lower_case=snake_case      ,    bos_token=snake_case      ,    eos_token=snake_case      ,    unk_token=snake_case      ,    sep_token=snake_case      ,    pad_token=snake_case      ,    cls_token=snake_case      ,    mask_token=snake_case      ,    split_by_punct=snake_case      ,    **snake_case      ,    )

      __lowerCAmelCase								=					do_lower_case
      __lowerCAmelCase								=					split_by_punct
      __lowerCAmelCase								=					vocab_file
      __lowerCAmelCase								=					False if not self.vocab_file else True





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		List[Any]      ,    snake_case    :		Union[str, Any]=None				):

      """simple docstring"""





      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      __lowerCAmelCase								=					[self.cls_token_id]
      __lowerCAmelCase								=					[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep





    def 						A		( self    :		List[str]      ,    snake_case    :		Union[str, Any]      ,    snake_case    :		Tuple=None      ,    snake_case    :		List[Any]=False				):

      """simple docstring"""





      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=snake_case      ,    token_ids_a=snake_case      ,    already_has_special_tokens=snake_case				)

      if token_ids_a is not None:
        return [1] + ([0] * len(snake_case				)) + [1] + ([0] * len(snake_case				)) + [1]
      return [1] + ([0] * len(snake_case				)) + [1]





    def 						A		( self    :		List[str]      ,    snake_case    :		Any      ,    snake_case    :		Dict=None				):

      """simple docstring"""





      __lowerCAmelCase								=					[self.sep_token_id]
      __lowerCAmelCase								=					[self.cls_token_id]
      if token_ids_a is None:
        return len(cls + token_ids_a + sep				) * [0]
      return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		str      ,    snake_case    :		Optional[str] = None				):

      """simple docstring"""





      if not self.can_save_slow_tokenizer:
        raise ValueError(
            '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
            '''tokenizer.'''				)

      if not os.path.isdir(snake_case				):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
        return
      __lowerCAmelCase								=					os.path.join(
          snake_case      ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

      if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case				):
        copyfile(self.vocab_file      ,    snake_case				)

      return (out_vocab_file,)







import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__lowercase				    =      {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

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

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.bias'''
    ]

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

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.attn.proj.weight'''
    ]
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.attn.proj.bias''']

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

  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					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
  __lowerCAmelCase								=					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=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

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


if __name__ == "__main__":
      __lowercase				    =      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."""
      )

      __lowercase				    =      parser.parse_args()
      main(args)
