






'''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


UpperCamelCase    :      Optional[Any]  			=		logging.get_logger(__name__)

UpperCamelCase    :      str  			=		{'vocab_file': 'vocab.txt'}

UpperCamelCase    :      int  			=		{
    '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',
    },
}

UpperCamelCase    :      Dict  			=		{
    'facebook/esm2_t6_8M_UR50D': 10_24,
    'facebook/esm2_t12_35M_UR50D': 10_24,
}
def 						A__			(				__lowerCAmelCase		:			List[str]						):
						with open(__lowerCAmelCase   ,    """r"""						) as f:
												lowerCamelCase__         =  f.read().splitlines()
												return [l.strip() for l in lines]





class 		UpperCamelCase__    (a			):




		'''simple docstring'''





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






		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase="<unk>"   ,_lowerCAmelCase="<cls>"   ,_lowerCAmelCase="<pad>"   ,_lowerCAmelCase="<mask>"   ,_lowerCAmelCase="<eos>"   ,**_lowerCAmelCase   ,):
								super().__init__(**_lowerCAmelCase       )
								lowerCamelCase__         =  load_vocab_file(_lowerCAmelCase       )
								lowerCamelCase__         =  dict(enumerate(self.all_tokens       )       )
								lowerCamelCase__         =  {tok: ind for ind, tok in enumerate(self.all_tokens       )}
								lowerCamelCase__         =  unk_token
								lowerCamelCase__         =  cls_token
								lowerCamelCase__         =  pad_token
								lowerCamelCase__         =  mask_token
								lowerCamelCase__         =  eos_token
								lowerCamelCase__         =  self.all_tokens
								self._create_trie(self.unique_no_split_tokens       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self._id_to_token.get(_lowerCAmelCase   ,self.unk_token       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self._token_to_id.get(_lowerCAmelCase   ,self._token_to_id.get(self.unk_token       )       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,**_lowerCAmelCase       ):
								return text.split()






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase=False       ):
								return len(self._id_to_token       )






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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self._token_to_id.get(_lowerCAmelCase   ,self._token_to_id.get(self.unk_token       )       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self._id_to_token.get(_lowerCAmelCase   ,self.unk_token       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								lowerCamelCase__         =  [self.cls_token_id]
								lowerCamelCase__         =  [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 			UpperCamelCase_					(      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]
								lowerCamelCase__         =  [1] + ([0] * len(_lowerCAmelCase       )) + [1]
								if token_ids_a is not None:
														mask += [0] * len(_lowerCAmelCase       ) + [1]
								return mask






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  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 			UpperCamelCase_					(      self       ):
								return self.get_vocab_size(with_added_tokens=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      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 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  OmegaConf.load(__lowerCAmelCase						)
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)["""model"""]
						lowerCamelCase__         =  list(state_dict.keys()						)

						# extract state_dict for VQVAE
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """first_stage_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

    # extract state_dict for UNetLDM
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """model.diffusion_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

						lowerCamelCase__         =  config.model.params.first_stage_config.params
						lowerCamelCase__         =  config.model.params.unet_config.params

						lowerCamelCase__         =  VQModel(**__lowerCAmelCase						).eval()
						vqvae.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  UNetLDMModel(**__lowerCAmelCase						).eval()
						unet.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  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=__lowerCAmelCase   ,    )

						lowerCamelCase__         =  LDMPipeline(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						pipeline.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
							UpperCamelCase    :      Optional[int]  			=		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)
							UpperCamelCase    :      List[Any]  			=		parser.parse_args()

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

