



'''simple docstring'''






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["""image_processor""", """tokenizer"""]
    lowercase__			=   """BlipImageProcessor"""
    lowercase__			=   """AutoTokenizer"""







    def __init__(				self			:  Dict						,      __snake_case			:  Tuple						,      __snake_case			:  List[str]						,      __snake_case			:  List[Any]     ):
      super().__init__(__snake_case						,      __snake_case     )

      # add QFormer tokenizer
      a						: Optional[Any]					       =					qformer_tokenizer







    def __call__(				self			:  Any						,      __snake_case			:  ImageInput = None						,      __snake_case			:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,      __snake_case			:  bool = True						,      __snake_case			:  Union[bool, str, PaddingStrategy] = False						,      __snake_case			:  Union[bool, str, TruncationStrategy] = None						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  int = 0						,      __snake_case			:  Optional[int] = None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  bool = False						,      __snake_case			:  bool = False						,      __snake_case			:  bool = False						,      __snake_case			:  bool = False						,      __snake_case			:  bool = False						,      __snake_case			:  bool = True						,      __snake_case			:  Optional[Union[str, TensorType]] = None						,      **__snake_case			:  Optional[int]						,      ):
      if images is None and text is None:
        raise ValueError('You have to specify at least images or text.'     )

      a						: Dict					       =					BatchFeature()

      if text is not None:
        a						: List[str]					       =					self.tokenizer(
            text=__snake_case						,      add_special_tokens=__snake_case						,      padding=__snake_case						,      truncation=__snake_case						,      max_length=__snake_case						,      stride=__snake_case						,      pad_to_multiple_of=__snake_case						,      return_attention_mask=__snake_case						,      return_overflowing_tokens=__snake_case						,      return_special_tokens_mask=__snake_case						,      return_offsets_mapping=__snake_case						,      return_token_type_ids=__snake_case						,      return_length=__snake_case						,      verbose=__snake_case						,      return_tensors=__snake_case						,      **__snake_case						,      )
        encoding.update(__snake_case     )
        a						: int					       =					self.qformer_tokenizer(
            text=__snake_case						,      add_special_tokens=__snake_case						,      padding=__snake_case						,      truncation=__snake_case						,      max_length=__snake_case						,      stride=__snake_case						,      pad_to_multiple_of=__snake_case						,      return_attention_mask=__snake_case						,      return_overflowing_tokens=__snake_case						,      return_special_tokens_mask=__snake_case						,      return_offsets_mapping=__snake_case						,      return_token_type_ids=__snake_case						,      return_length=__snake_case						,      verbose=__snake_case						,      return_tensors=__snake_case						,      **__snake_case						,      )
        a						: Union[str, Any]					       =					qformer_text_encoding.pop('input_ids'     )
        a						: List[str]					       =					qformer_text_encoding.pop('attention_mask'     )

      if images is not None:
        a						: List[Any]					       =					self.image_processor(__snake_case						,      return_tensors=__snake_case     )
        encoding.update(__snake_case     )

      return encoding







    def       lowercase_	(				self			:  int						,      *__snake_case			:  Optional[Any]						,      **__snake_case			:  Union[str, Any]     ):
      return self.tokenizer.batch_decode(*__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  Dict						,      *__snake_case			:  Union[str, Any]						,      **__snake_case			:  int     ):
      return self.tokenizer.decode(*__snake_case						,      **__snake_case     )







    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def       lowercase_	(				self			:  List[str]     ):
      a						: List[Any]					       =					self.tokenizer.model_input_names
      a						: Dict					       =					self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Tuple						,      **__snake_case			:  int     ):
      if os.path.isfile(__snake_case     ):
        raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""     )
      os.makedirs(__snake_case						,      exist_ok=__snake_case     )
      a						: Tuple					       =					os.path.join(__snake_case						,      'qformer_tokenizer'     )
      self.qformer_tokenizer.save_pretrained(__snake_case     )
      return super().save_pretrained(__snake_case						,      **__snake_case     )







    @classmethod
    def       lowercase_	(				cls			:  List[Any]						,      __snake_case			:  Union[str, Any]						,      **__snake_case			:  int     ):
      a						: Tuple					       =					AutoTokenizer.from_pretrained(__snake_case						,      subfolder='qformer_tokenizer'     )
      a						: Any					       =					cls._get_arguments_from_pretrained(__snake_case						,      **__snake_case     )
      args.append(__snake_case     )
      return cls(*__snake_case     )




'''simple docstring'''






lowerCAmelCase: Union[str, Any]							=						{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
lowerCAmelCase: Optional[Any]							=						{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 1_2,
    'Pm': 1_5,
    'Em': 1_8,
    'Zm': 2_1,
    'Ym': 2_4,
}
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  a						: Optional[int]					       =					from_type.lower().strip('s'    )
  a						: Optional[Any]					       =					to_type.lower().strip('s'    )

  a						: Dict					       =					UNIT_SYMBOL.get(_A			,      _A    )
  a						: Optional[Any]					       =					UNIT_SYMBOL.get(_A			,      _A    )

  if from_sanitized not in METRIC_CONVERSION:
    a						: Optional[Any]					       =					(
        f"""Invalid 'from_type' value: {from_type!r}.\n"""
        f"""Conversion abbreviations are: {", ".join(_A    )}"""
    )
    raise ValueError(_A    )
  if to_sanitized not in METRIC_CONVERSION:
    a						: Union[str, Any]					       =					(
        f"""Invalid 'to_type' value: {to_type!r}.\n"""
        f"""Conversion abbreviations are: {", ".join(_A    )}"""
    )
    raise ValueError(_A    )
  a						: List[Any]					       =					METRIC_CONVERSION[from_sanitized]
  a						: int					       =					METRIC_CONVERSION[to_sanitized]
  a						: Tuple					       =					1

  if from_exponent > to_exponent:
    a						: Optional[int]					       =					from_exponent - to_exponent
  else:
    a						: Optional[Any]					       =					-(to_exponent - from_exponent)

  return value * pow(10			,      _A    )


if __name__ == "__main__":
     from doctest import testmod

     testmod()