



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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_big_bird import BigBirdTokenizer
else:
  _lowerCAmelCase    :Tuple     =					None

_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
_lowerCAmelCase    :str     =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase    :List[Any]     =					{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

_lowerCAmelCase    :Optional[int]     =					{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}


_lowerCAmelCase    :Optional[int]     =					'▁'
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=BigBirdTokenizer
     a__									=['''input_ids''', '''attention_mask''']
     a__									=[]





     def __init__(					self       ,	A=None       ,	A=None       ,	A="<unk>"       ,	A="<s>"       ,	A="</s>"       ,	A="<pad>"       ,	A="[SEP]"       ,	A="[MASK]"       ,	A="[CLS]"       ,	**A       ,	)							->					int:
         _UpperCAmelCase							:  int											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else bos_token
         _UpperCAmelCase							:  int											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else eos_token
         _UpperCAmelCase							:  List[Any]											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else unk_token
         _UpperCAmelCase							:  str											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else pad_token
         _UpperCAmelCase							:  Tuple											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else cls_token
         _UpperCAmelCase							:  List[str]											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else sep_token

         # Mask token behave like a normal word, i.e. include the space before it
         _UpperCAmelCase							:  List[str]											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else mask_token

         super().__init__(
             A       ,	tokenizer_file=A       ,	bos_token=A       ,	eos_token=A       ,	unk_token=A       ,	sep_token=A       ,	pad_token=A       ,	cls_token=A       ,	mask_token=A       ,	**A       ,	)

         _UpperCAmelCase							:  Dict											=    vocab_file
         _UpperCAmelCase							:  Optional[int]											=    False if not self.vocab_file else True





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:
         _UpperCAmelCase							:  Union[str, Any]											=    [self.sep_token_id]
         _UpperCAmelCase							:  Any											=    [self.cls_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       ,	A       ,	A = None       ,	A = False		)							->					List[int]:

         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 x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:
         _UpperCAmelCase							:  str											=    [self.sep_token_id]
         _UpperCAmelCase							:  Optional[Any]											=    [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       ,	A       ,	A = None		)							->					Tuple[str]:
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.'''		)

         if not os.path.isdir(A		):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
             return
         _UpperCAmelCase							:  str											=    os.path.join(
             A       ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

         if os.path.abspath(self.vocab_file		) != os.path.abspath(A		):
             copyfile(self.vocab_file       ,	A		)

         return (out_vocab_file,)







"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

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

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

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    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__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					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    :List[Any]     =					parser.parse_args()

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


