






"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							"▁"

_lowerCAmelCase         =							{"vocab_file": "sentencepiece.bpe.model"}

_lowerCAmelCase         =							{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

_lowerCAmelCase         =							{
    "facebook/xglm-564M": 2_048,
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





 """simple docstring"""




 _A       :				Optional[Any]    =				VOCAB_FILES_NAMES
 _A       :				Dict    =				PRETRAINED_VOCAB_FILES_MAP
 _A       :				Optional[Any]    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _A       :				str    =				["""input_ids""", """attention_mask"""]





 def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_="<s>"     ,     lowerCAmelCase_="</s>"     ,     lowerCAmelCase_="</s>"     ,     lowerCAmelCase_="<s>"     ,     lowerCAmelCase_="<unk>"     ,     lowerCAmelCase_="<pad>"     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
  A_		:						Any	    =   {} if sp_model_kwargs is None else sp_model_kwargs

  # Compatibility with the original tokenizer
  A_		:						List[str]	    =   7
  A_		:						int	    =   [f"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

  A_		:						Union[str, Any]	    =   kwargs.get("""additional_special_tokens"""     ,     []							)
  kwargs["additional_special_tokens"] += [
      word for word in madeup_words if word not in kwargs["additional_special_tokens"]
  ]

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

  A_		:						Optional[Any]	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
  self.sp_model.Load(str(lowerCAmelCase_							)							)
  A_		:						str	    =   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'

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

  # Mimic fairseq token-to-id alignment for the first 4 token
  A_		:						int	    =   {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

  A_		:						Optional[Any]	    =   len(self.sp_model							)
  A_		:						int	    =   {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
  self.fairseq_tokens_to_ids.update(lowerCAmelCase_							)

  A_		:						Tuple	    =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}





 def __getstate__(self							):
  A_		:						Optional[int]	    =   self.__dict__.copy()
  A_		:						int	    =   None
  A_		:						Optional[Any]	    =   self.sp_model.serialized_model_proto()
  return state





 def __setstate__(self     ,     lowerCAmelCase_							):
  A_		:						Dict	    =   d

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

  A_		:						List[Any]	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
  self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):

  if token_ids_a is None:
   return [self.sep_token_id] + token_ids_a
  A_		:						Union[str, Any]	    =   [self.sep_token_id]
  return sep + token_ids_a + sep + sep + token_ids_a





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = False							):

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

  if token_ids_a is None:
   return [1] + ([0] * len(lowerCAmelCase_							))
  return [1] + ([0] * len(lowerCAmelCase_							)) + [1, 1] + ([0] * len(lowerCAmelCase_							))





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
  A_		:						List[str]	    =   [self.sep_token_id]

  if token_ids_a is None:
   return len(sep + token_ids_a							) * [0]
  return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]





 @property
 def    lowerCamelCase(self							):
  return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words





 def    lowerCamelCase(self							):
  A_		:						Optional[int]	    =   {self.convert_ids_to_tokens(lowerCAmelCase_							): i for i in range(self.vocab_size							)}
  vocab.update(self.added_tokens_encoder							)
  return vocab





 def    lowerCamelCase(self     ,     lowerCAmelCase_							):
  return self.sp_model.encode(lowerCAmelCase_     ,     out_type=lowerCAmelCase_							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_							):
  if token in self.fairseq_tokens_to_ids:
   return self.fairseq_tokens_to_ids[token]
  A_		:						Union[str, Any]	    =   self.sp_model.PieceToId(lowerCAmelCase_							)

  # 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     ,     lowerCAmelCase_							):
  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     ,     lowerCAmelCase_							):
  A_		:						str	    =   """""".join(lowerCAmelCase_							).replace(lowerCAmelCase_     ,     """ """							).strip()
  return out_string





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
  if not os.path.isdir(lowerCAmelCase_							):
   logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
   return
  A_		:						Dict	    =   os.path.join(
      lowerCAmelCase_     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

  if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCAmelCase_							) and os.path.isfile(self.vocab_file							):
   copyfile(self.vocab_file     ,     lowerCAmelCase_							)
  elif not os.path.isfile(self.vocab_file							):
   with open(lowerCAmelCase_     ,     """wb"""							) as fi:
    A_		:						Optional[int]	    =   self.sp_model.serialized_model_proto()
    fi.write(lowerCAmelCase_							)

  return (out_vocab_file,)









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

    # extract state_dict for UNetLDM
	A_		:						Tuple	    =   {}
	A_		:						Dict	    =   """model.diffusion_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						Optional[Any]	    =   state_dict[key]

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						List[str]	    =   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=snake_case__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


if __name__ == "__main__":
							_lowerCAmelCase         =							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         =							parser.parse_args()

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

