




import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A :		str      							=    logging.get_logger(__name__)
__A :		Union[str, Any]      							=    {"""vocab_file""": """spiece.model"""}

__A :		List[Any]      							=    {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

__A :		Any      							=    {
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

__A :		Optional[int]      							=    """▁"""
class 							lowerCamelCase		( _SCREAMING_SNAKE_CASE				):
   lowercase       :  int      =				VOCAB_FILES_NAMES
   lowercase       :  Tuple      =				PRETRAINED_VOCAB_FILES_MAP
   lowercase       :  List[str]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase       :  Any      =				['input_ids', 'token_type_ids']







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_="<unk>"    ,      SCREAMING_SNAKE_CASE_="[SEP]"    ,      SCREAMING_SNAKE_CASE_="<pad>"    ,      SCREAMING_SNAKE_CASE_="[CLS]"    ,      SCREAMING_SNAKE_CASE_="[MASK]"    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       UpperCamelCase				:   List[Any]          =							(
           AddedToken(__A    ,      lstrip=__A    ,      rstrip=__A    ,      normalized=__A )
           if isinstance(__A    ,      __A )
           else mask_token
       )

       UpperCamelCase				:   int          =							{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=__A    ,      remove_space=__A    ,      keep_accents=__A    ,      unk_token=__A    ,      sep_token=__A    ,      pad_token=__A    ,      cls_token=__A    ,      mask_token=__A    ,      sp_model_kwargs=self.sp_model_kwargs    ,      **__A    ,      )

       UpperCamelCase				:   Union[str, Any]          =							do_lower_case
       UpperCamelCase				:   Dict          =							remove_space
       UpperCamelCase				:   int          =							keep_accents
       UpperCamelCase				:   str          =							vocab_file

       UpperCamelCase				:   Dict          =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(__A )







   @property
   def       a_							(							self ):
       return len(self.sp_model )







   def       a_							(							self ):
       UpperCamelCase				:   Union[str, Any]          =							{self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab







   def __getstate__(							self ):
       UpperCamelCase				:   int          =							self.__dict__.copy()
       UpperCamelCase				:   Optional[int]          =							None
       return state







   def __setstate__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   List[str]          =							d

       # for backward compatibility
       if not hasattr(self    ,      """sp_model_kwargs""" ):
           UpperCamelCase				:   Tuple          =							{}

       UpperCamelCase				:   List[Any]          =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       if self.remove_space:
           UpperCamelCase				:   Tuple          =							""" """.join(inputs.strip().split() )
       else:
           UpperCamelCase				:   str          =							inputs
       UpperCamelCase				:   Dict          =							outputs.replace("""``"""    ,      """\"""" ).replace("""\'\'"""    ,      """\"""" )

       if not self.keep_accents:
           UpperCamelCase				:   Optional[int]          =							unicodedata.normalize("""NFKD"""    ,      __A )
           UpperCamelCase				:   List[Any]          =							"""""".join([c for c in outputs if not unicodedata.combining(__A )] )
       if self.do_lower_case:
           UpperCamelCase				:   Union[str, Any]          =							outputs.lower()

       return outputs







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Optional[int]          =							self.preprocess_text(__A )
       UpperCamelCase				:   int          =							self.sp_model.encode(__A    ,      out_type=__A )
       UpperCamelCase				:   str          =							[]
       for piece in pieces:
           if len(__A ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
               UpperCamelCase				:   Optional[Any]          =							self.sp_model.EncodeAsPieces(piece[:-1].replace(__A    ,      """""" ) )
               if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                   if len(cur_pieces[0] ) == 1:
                       UpperCamelCase				:   Union[str, Any]          =							cur_pieces[1:]
                   else:
                       UpperCamelCase				:   Dict          =							cur_pieces[0][1:]
               cur_pieces.append(piece[-1] )
               new_pieces.extend(__A )
           else:
               new_pieces.append(__A )

       return new_pieces







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       return self.sp_model.PieceToId(__A )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       return self.sp_model.IdToPiece(__A )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   List[str]          =							[]
       UpperCamelCase				:   List[str]          =							""""""
       UpperCamelCase				:   str          =							False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special:
                   out_string += " "
               out_string += self.sp_model.decode(__A ) + token
               UpperCamelCase				:   List[Any]          =							True
               UpperCamelCase				:   Optional[int]          =							[]
           else:
               current_sub_tokens.append(__A )
               UpperCamelCase				:   Any          =							False
       out_string += self.sp_model.decode(__A )
       return out_string.strip()







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = True    ,      **SCREAMING_SNAKE_CASE_    ,      ):
       UpperCamelCase				:   List[Any]          =							kwargs.pop("""use_source_tokenizer"""    ,      __A )

       UpperCamelCase				:   str          =							self.convert_ids_to_tokens(__A    ,      skip_special_tokens=__A )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       UpperCamelCase				:   Tuple          =							[]
       UpperCamelCase				:   str          =							[]
       for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
               continue
           if token in self.added_tokens_encoder:
               if current_sub_text:
                   sub_texts.append(self.convert_tokens_to_string(__A ) )
                   UpperCamelCase				:   List[Any]          =							[]
               sub_texts.append(__A )
           else:
               current_sub_text.append(__A )
       if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(__A ) )

       # Mimic the behavior of the Rust tokenizer:
       # No space after <unk>
       if spaces_between_special_tokens:
           UpperCamelCase				:   List[str]          =							re.sub(r"""(<unk>) """    ,      r"""\1"""    ,      """ """.join(__A ) )
       else:
           UpperCamelCase				:   Union[str, Any]          =							"""""".join(__A )

       UpperCamelCase				:   List[Any]          =							(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
           UpperCamelCase				:   str          =							self.clean_up_tokenization(__A )
           return clean_text
       else:
           return text







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
       UpperCamelCase				:   Optional[int]          =							[self.sep_token_id]
       UpperCamelCase				:   List[str]          =							[self.cls_token_id]
       if token_ids_a is None:
           return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = False ):

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

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







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
       UpperCamelCase				:   Tuple          =							[self.sep_token_id]
       UpperCamelCase				:   str          =							[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    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
       if not os.path.isdir(__A ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
           return
       UpperCamelCase				:   List[str]          =							os.path.join(
           __A    ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
           copyfile(self.vocab_file    ,      __A )
       elif not os.path.isfile(self.vocab_file ):
           with open(__A    ,      """wb""" ) as fi:
               UpperCamelCase				:   str          =							self.sp_model.serialized_model_proto()
               fi.write(__A )

       return (out_vocab_file,)










"""simple docstring"""




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__A :		int      							=    {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
def 					A_			(		snake_case_     :       Dict    ):







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







    '''simple docstring'''
    UpperCamelCase				:   int          =							download_model(pretrained_models[args.image_size]    )

    UpperCamelCase				:   str          =							state_dict["""x_embedder.proj.weight"""]
    UpperCamelCase				:   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(2_8    ):
        UpperCamelCase				:   Optional[int]          =							state_dict[
            """t_embedder.mlp.0.weight"""
        ]
        UpperCamelCase				:   Optional[Any]          =							state_dict[
            """t_embedder.mlp.0.bias"""
        ]
        UpperCamelCase				:   str          =							state_dict[
            """t_embedder.mlp.2.weight"""
        ]
        UpperCamelCase				:   Optional[int]          =							state_dict[
            """t_embedder.mlp.2.bias"""
        ]
        UpperCamelCase				:   int          =							state_dict[
            """y_embedder.embedding_table.weight"""
        ]

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

        UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							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				:   int          =							q
        UpperCamelCase				:   Tuple          =							q_bias
        UpperCamelCase				:   List[Any]          =							k
        UpperCamelCase				:   int          =							k_bias
        UpperCamelCase				:   List[str]          =							v
        UpperCamelCase				:   str          =							v_bias

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

        UpperCamelCase				:   str          =							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				:   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"""    )

    UpperCamelCase				:   List[str]          =							state_dict["""final_layer.adaLN_modulation.1.weight"""]
    UpperCamelCase				:   str          =							state_dict["""final_layer.adaLN_modulation.1.bias"""]
    UpperCamelCase				:   Tuple          =							state_dict["""final_layer.linear.weight"""]
    UpperCamelCase				:   List[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=2_8						,attention_head_dim=7_2						,in_channels=4						,out_channels=8						,patch_size=2						,attention_bias=snake_case_						,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=snake_case_						,)
    transformer.load_state_dict(snake_case_						,strict=snake_case_    )

    UpperCamelCase				:   str          =							DDIMScheduler(
        num_train_timesteps=1_0_0_0						,beta_schedule="""linear"""						,prediction_type="""epsilon"""						,clip_sample=snake_case_						,)

    UpperCamelCase				:   Optional[Any]          =							AutoencoderKL.from_pretrained(args.vae_model    )

    UpperCamelCase				:   List[Any]          =							DiTPipeline(transformer=snake_case_						,vae=snake_case_						,scheduler=snake_case_    )

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


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




