





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_ 							=       {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase_ 							=       {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		VOCAB_FILES_NAMES
					lowerCAmelCase_							=		PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=		['''input_ids''', '''attention_mask''']
					lowerCAmelCase_							=		RobertaTokenizer




					def __init__(						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_="replace"			,  lowerCAmelCase_="<s>"			,  lowerCAmelCase_="</s>"			,  lowerCAmelCase_="</s>"			,  lowerCAmelCase_="<s>"			,  lowerCAmelCase_="<unk>"			,  lowerCAmelCase_="<pad>"			,  lowerCAmelCase_="<mask>"			,  lowerCAmelCase_=False			,  lowerCAmelCase_=True			,  **lowerCAmelCase_			,  )						->							int:
										super().__init__(
										    lowerCAmelCase_			,  lowerCAmelCase_			,  tokenizer_file=lowerCAmelCase_			,  errors=lowerCAmelCase_			,  bos_token=lowerCAmelCase_			,  eos_token=lowerCAmelCase_			,  sep_token=lowerCAmelCase_			,  cls_token=lowerCAmelCase_			,  unk_token=lowerCAmelCase_			,  pad_token=lowerCAmelCase_			,  mask_token=lowerCAmelCase_			,  add_prefix_space=lowerCAmelCase_			,  trim_offsets=lowerCAmelCase_			,  **lowerCAmelCase_			,  )

										_snake_case													=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
										if pre_tok_state.get('add_prefix_space'			,  lowerCAmelCase_					) != add_prefix_space:
															_snake_case													=			getattr(lowerCAmelCase_			,  pre_tok_state.pop('type'					)					)
															_snake_case													=			add_prefix_space
															_snake_case													=			pre_tok_class(**lowerCAmelCase_					)

										_snake_case													=			add_prefix_space

										_snake_case													=			'post_processor'
										_snake_case													=			getattr(self.backend_tokenizer			,  lowerCAmelCase_			,  lowerCAmelCase_					)
										if tokenizer_component_instance:
															_snake_case													=			json.loads(tokenizer_component_instance.__getstate__()					)

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																				_snake_case													=			tuple(state['sep']					)
															if "cls" in state:
																				_snake_case													=			tuple(state['cls']					)

															_snake_case													=			False

															if state.get('add_prefix_space'			,  lowerCAmelCase_					) != add_prefix_space:
																				_snake_case													=			add_prefix_space
																				_snake_case													=			True

															if state.get('trim_offsets'			,  lowerCAmelCase_					) != trim_offsets:
																				_snake_case													=			trim_offsets
																				_snake_case													=			True

															if changes_to_apply:
																				_snake_case													=			getattr(lowerCAmelCase_			,  state.pop('type'					)					)
																				_snake_case													=			component_class(**lowerCAmelCase_					)
																				setattr(self.backend_tokenizer			,  lowerCAmelCase_			,  lowerCAmelCase_					)




					@property
					def 		lowerCAmelCase     (						self					)						->							str:
										if self._mask_token is None:
															if self.verbose:
																				logger.error('Using mask_token, but it is not set yet.'					)
															return None
										return str(self._mask_token					)




					@mask_token.setter
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Dict:
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else value
										_snake_case													=			value




					def 		lowerCAmelCase     (						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							BatchEncoding:
										_snake_case													=			kwargs.get('is_split_into_words'			,  lowerCAmelCase_					)
										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase_			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							BatchEncoding:
										_snake_case													=			kwargs.get('is_split_into_words'			,  lowerCAmelCase_					)

										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase_			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							Tuple[str]:
										_snake_case													=			self._tokenizer.model.save(lowerCAmelCase_			,  name=lowerCAmelCase_					)
										return tuple(lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Optional[Any]:
										_snake_case													=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
															return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							List[int]:
										_snake_case													=			[self.sep_token_id]
										_snake_case													=			[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]










import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Dict  )     ->	List[Any]:



					'''simple docstring'''




					_snake_case													=			OmegaConf.load(UpperCamelCase__  )
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )['model']
					_snake_case													=			list(state_dict.keys()  )

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

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

					_snake_case													=			config.model.params.first_stage_config.params
					_snake_case													=			config.model.params.unet_config.params

					_snake_case													=			VQModel(**UpperCamelCase__  ).eval()
					vqvae.load_state_dict(UpperCamelCase__  )

					_snake_case													=			UNetLDMModel(**UpperCamelCase__  ).eval()
					unet.load_state_dict(UpperCamelCase__  )

					_snake_case													=			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__    ,      )

					_snake_case													=			LDMPipeline(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )
					pipeline.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       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_ 							=       parser.parse_args()

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



