






"""simple docstring"""
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


_lowerCAmelCase         =							logging.get_logger(__name__)

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

_lowerCAmelCase         =							{
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

_lowerCAmelCase         =							{"allegro/herbert-base-cased": 514}
_lowerCAmelCase         =							{}



class      SCREAMING_SNAKE_CASE  (				__A						):





 """simple docstring"""




 _A       :				Union[str, Any]    =				VOCAB_FILES_NAMES
 _A       :				List[Any]    =				PRETRAINED_VOCAB_FILES_MAP
 _A       :				Optional[int]    =				PRETRAINED_INIT_CONFIGURATION
 _A       :				Dict    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _A       :				int    =				HerbertTokenizer





 def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_="<s>"     ,     lowerCAmelCase_="<unk>"     ,     lowerCAmelCase_="<pad>"     ,     lowerCAmelCase_="<mask>"     ,     lowerCAmelCase_="</s>"     ,     **lowerCAmelCase_     ,     ):
  super().__init__(
      lowerCAmelCase_     ,     lowerCAmelCase_     ,     tokenizer_file=lowerCAmelCase_     ,     cls_token=lowerCAmelCase_     ,     unk_token=lowerCAmelCase_     ,     pad_token=lowerCAmelCase_     ,     mask_token=lowerCAmelCase_     ,     sep_token=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
  A_		:						List[Any]	    =   [self.cls_token_id]
  A_		:						Any	    =   [self.sep_token_id]
  if token_ids_a is None:
   return cls + token_ids_a + sep

  return cls + token_ids_a + sep + token_ids_a + sep





 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_							)) + [1]
  return [1] + ([0] * len(lowerCAmelCase_							)) + [1] + ([0] * len(lowerCAmelCase_							)) + [1]





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
  A_		:						Optional[Any]	    =   [self.sep_token_id]
  A_		:						Dict	    =   [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							) * [0] + len(token_ids_a + sep							) * [1]





 def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None							):
  A_		:						List[Any]	    =   self._tokenizer.model.save(lowerCAmelCase_     ,     name=lowerCAmelCase_							)
  return tuple(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)

