




"""simple docstring"""







import json
import os
from typing import Optional, Tuple

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


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{"""vocab_file""": """vocab.json"""}

__lowercase    =				{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

__lowercase    =				{"""mgp-str""": 27}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							str     =	VOCAB_FILES_NAMES
    UpperCAmelCase	:							List[str]     =	PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase	:							Any     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






    def __init__( self    :   Dict    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Any="[GO]"    ,			__UpperCAmelCase    :   int="[GO]"    ,			__UpperCAmelCase    :   Optional[Any]="[s]"    ,			__UpperCAmelCase    :   int="[GO]"    ,			**__UpperCAmelCase    :   List[str]):
      super().__init__(
          unk_token=lowerCAmelCase__    ,			bos_token=lowerCAmelCase__    ,			eos_token=lowerCAmelCase__    ,			pad_token=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)

      with open(lowerCAmelCase__    ,			encoding="utf-8") as vocab_handle:
        a						:		Tuple											=      json.load(lowerCAmelCase__)
      a						:		Optional[Any]											=      {v: k for k, v in self.vocab.items()}






    @property
    def      __snake_case							( self    :   List[str]):
      return len(self.vocab)






    def      __snake_case							( self    :   Dict):
      return dict(self.vocab    ,			**self.added_tokens_encoder)






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		List[Any]											=      []
      for s in text:
        char_tokens.extend(lowerCAmelCase__)
      return char_tokens






    def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   int):
      return self.vocab.get(lowerCAmelCase__    ,			self.vocab.get(self.unk_token))






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[int]):
      return self.decoder.get(lowerCAmelCase__)






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Optional[str] = None):




      if not os.path.isdir(lowerCAmelCase__):
        logger.error("Vocabulary path ({}) should be a directory".format(lowerCAmelCase__))
        return
      a						:		Union[str, Any]											=      os.path.join(
          lowerCAmelCase__    ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

      return (vocab_file,)










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

 __lowercase    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






