




import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase    :		List[str]			   =       logging.get_logger(__name__)

lowerCamelCase    :		Tuple			   =       {"vocab_file": "vocab.json"}

lowerCamelCase    :		Optional[Any]			   =       {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCamelCase    :		int			   =       {"mgp-str": 27}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						VOCAB_FILES_NAMES
 UpperCamelCase						       =						PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

 def __init__(    self :  Optional[int]						,							A_ :  Dict						,							A_ :  str="[GO]"						,							A_ :  str="[GO]"						,							A_ :  List[Any]="[s]"						,							A_ :  Union[str, Any]="[GO]"						,							**A_ :  Tuple  )       ->     Union[str, Any]:

       """simple docstring"""






       super().__init__(
           unk_token=A_						,							bos_token=A_						,							eos_token=A_						,							pad_token=A_						,							**A_						,							)

       with open(A_						,							encoding='utf-8'  ) as vocab_handle:
             lowerCamelCase_							=   json.load(A_  )
       lowerCamelCase_							=   {v: k for k, v in self.vocab.items()}

 @property
 def     a__			(    self :  str  )       ->     Dict:

       """simple docstring"""






       return len(self.vocab  )

 def     a__			(    self :  Optional[Any]  )       ->     Optional[Any]:

       """simple docstring"""






       return dict(self.vocab						,							**self.added_tokens_encoder  )

 def     a__			(    self :  Any						,							A_ :  Optional[int]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   []
       for s in text:
             char_tokens.extend(A_  )
       return char_tokens

 def     a__			(    self :  List[Any]						,							A_ :  Tuple  )       ->     Tuple:

       """simple docstring"""






       return self.vocab.get(A_						,							self.vocab.get(self.unk_token  )  )

 def     a__			(    self :  Any						,							A_ :  str  )       ->     Optional[int]:

       """simple docstring"""






       return self.decoder.get(A_  )





 def     a__			(    self :  str						,							A_ :  str						,							A_ :  Optional[str] = None  )       ->     Tuple[str]:

       """simple docstring"""






       if not os.path.isdir(A_  ):
             logger.error('Vocabulary path ({}) should be a directory'.format(A_  )  )
             return
       lowerCamelCase_							=   os.path.join(
           A_						,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

       with open(A_						,							'w'						,							encoding='utf-8'  ) as f:
             f.write(json.dumps(self.vocab						,							indent=2						,							sort_keys=A_						,							ensure_ascii=A_  ) + '\n'  )

       return (vocab_file,)









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



