






"""simple docstring"""
import os
from typing import List, Optional, Union

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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{"vocab_file": "vocab.txt"}

_lowerCAmelCase         =							{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

_lowerCAmelCase         =							{
    "facebook/esm2_t6_8M_UR50D": 1_024,
    "facebook/esm2_t12_35M_UR50D": 1_024,
}






def 			__UpperCamelCase	(		snake_case__					):
 with open(snake_case__   ,   """r"""					) as f:
  A_		:						Optional[Any]	    =   f.read().splitlines()
  return [l.strip() for l in lines]




class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





 """simple docstring"""




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





 def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_="<unk>"     ,     lowerCAmelCase_="<cls>"     ,     lowerCAmelCase_="<pad>"     ,     lowerCAmelCase_="<mask>"     ,     lowerCAmelCase_="<eos>"     ,     **lowerCAmelCase_     ,     ):
  super().__init__(**lowerCAmelCase_							)
  A_		:						Tuple	    =   load_vocab_file(lowerCAmelCase_							)
  A_		:						Any	    =   dict(enumerate(self.all_tokens							)							)
  A_		:						int	    =   {tok: ind for ind, tok in enumerate(self.all_tokens							)}
  A_		:						str	    =   unk_token
  A_		:						List[str]	    =   cls_token
  A_		:						str	    =   pad_token
  A_		:						List[Any]	    =   mask_token
  A_		:						List[str]	    =   eos_token
  A_		:						Dict	    =   self.all_tokens
  self._create_trie(self.unique_no_split_tokens							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_							):
  return self._id_to_token.get(lowerCAmelCase_     ,     self.unk_token							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_							):
  return self._token_to_id.get(lowerCAmelCase_     ,     self._token_to_id.get(self.unk_token							)							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							):
  return text.split()





 def    lowerCamelCase(self     ,     lowerCAmelCase_=False							):
  return len(self._id_to_token							)





 def    lowerCamelCase(self							):
  return {token: i for i, token in enumerate(self.all_tokens							)}





 def    lowerCamelCase(self     ,     lowerCAmelCase_							):
  return self._token_to_id.get(lowerCAmelCase_     ,     self._token_to_id.get(self.unk_token							)							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_							):
  return self._id_to_token.get(lowerCAmelCase_     ,     self.unk_token							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
  A_		:						List[str]	    =   [self.cls_token_id]
  A_		:						Optional[Any]	    =   [self.eos_token_id]  # No sep token in ESM vocabulary
  if token_ids_a is None:
   if self.eos_token_id is None:
    return cls + token_ids_a
   else:
    return cls + token_ids_a + sep
  elif self.eos_token_id is None:
   raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""							)
  return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = False							):
  if already_has_special_tokens:
   if token_ids_a is not None:
    raise ValueError(
        """You should not supply a second sequence if the provided sequence of """
        """ids is already formatted with special tokens for the model."""							)

   return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
  A_		:						List[Any]	    =   [1] + ([0] * len(lowerCAmelCase_							)) + [1]
  if token_ids_a is not None:
   mask += [0] * len(lowerCAmelCase_							) + [1]
  return mask





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
  A_		:						str	    =   os.path.join(lowerCAmelCase_     ,     (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""							)
  with open(lowerCAmelCase_     ,     """w"""							) as f:
   f.write("""\n""".join(self.all_tokens							)							)
  return (vocab_file,)





 @property
 def    lowerCamelCase(self							):
  return self.get_vocab_size(with_added_tokens=lowerCAmelCase_							)





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = False							):
  return super()._add_tokens(lowerCAmelCase_     ,     special_tokens=lowerCAmelCase_							)









"""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)

