



"""simple docstring"""
import os
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


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)

_lowerCAmelCase    :List[str]     =					'▁'

_lowerCAmelCase    :Tuple     =					{'vocab_file': 'sentencepiece.bpe.model'}

_lowerCAmelCase    :List[Any]     =					{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

_lowerCAmelCase    :Tuple     =					{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=['''input_ids''', '''attention_mask''']





     def __init__(					self       ,	A       ,	A="<s>"       ,	A="</s>"       ,	A="</s>"       ,	A="<s>"       ,	A="<unk>"       ,	A="<pad>"       ,	A="<mask>"       ,	A = None       ,	**A       ,	)							->					None:
         # Mask token behave like a normal word, i.e. include the space before it
         _UpperCAmelCase							:  Tuple											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else mask_token

         _UpperCAmelCase							:  List[Any]											=    {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=A       ,	eos_token=A       ,	unk_token=A       ,	sep_token=A       ,	cls_token=A       ,	pad_token=A       ,	mask_token=A       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**A       ,	)

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

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         _UpperCAmelCase							:  List[str]											=    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         _UpperCAmelCase							:  Any											=    1

         _UpperCAmelCase							:  Optional[Any]											=    len(self.sp_model		) + self.fairseq_offset
         _UpperCAmelCase							:  int											=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}





     def __getstate__(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Tuple											=    self.__dict__.copy()
         _UpperCAmelCase							:  List[str]											=    None
         _UpperCAmelCase							:  str											=    self.sp_model.serialized_model_proto()
         return state





     def __setstate__(					self       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[int]											=    d

         # for backward compatibility
         if not hasattr(self       ,	'''sp_model_kwargs'''		):
             _UpperCAmelCase							:  Optional[Any]											=    {}

         _UpperCAmelCase							:  Optional[int]											=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:

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





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = False		)							->					List[int]:

         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 None:
             return [1] + ([0] * len(A		)) + [1]
         return [1] + ([0] * len(A		)) + [1, 1] + ([0] * len(A		)) + [1]





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:
         _UpperCAmelCase							:  Dict											=    [self.sep_token_id]
         _UpperCAmelCase							:  List[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 + sep + token_ids_a + sep		) * [0]





     @property
     def  __lowerCAmelCase	(					self		)							->					Dict:
         return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Dict											=    {self.convert_ids_to_tokens(A		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab





     def  __lowerCAmelCase	(					self       ,	A		)							->					List[str]:
         return self.sp_model.encode(A       ,	out_type=A		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Any:
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         _UpperCAmelCase							:  Any											=    self.sp_model.PieceToId(A		)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





     def  __lowerCAmelCase	(					self       ,	A		)							->					int:
         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					int:
         _UpperCAmelCase							:  str											=    ''''''.join(A		).replace(A       ,	''' '''		).strip()
         return out_string





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					Tuple[str]:
         if not os.path.isdir(A		):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
             return
         _UpperCAmelCase							:  List[Any]											=    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 OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    DDIMScheduler(
        timesteps=config.model.params.timesteps     ,       beta_schedule='''scaled_linear'''     ,       beta_start=config.model.params.linear_start     ,       beta_end=config.model.params.linear_end     ,       clip_sample=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


