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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ['image_processor', 'tokenizer']
      _snake_case    		=       'BlipImageProcessor'
      _snake_case    		=       'AutoTokenizer'



      def __init__(					self     :				Optional[Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Tuple)				->str:


          """simple docstring"""


          super().__init__(_UpperCamelCase    ,  _UpperCamelCase)

          # add QFormer tokenizer
          _lowerCamelCase     :		Dict 						=						qformer_tokenizer



      def __call__(					self     :				Union[str, Any]    ,  _UpperCamelCase     :				ImageInput = None    ,  _UpperCamelCase     :				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Union[bool, str, PaddingStrategy] = False    ,  _UpperCamelCase     :				Union[bool, str, TruncationStrategy] = None    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				int = 0    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				Optional[bool] = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  **_UpperCamelCase     :				Dict    ,  )				->BatchFeature:


          """simple docstring"""


          if images is None and text is None:
              raise ValueError("""You have to specify at least images or text.""")

          _lowerCamelCase     :		Tuple 						=						BatchFeature()

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

          if images is not None:
              _lowerCamelCase     :		str 						=						self.image_processor(_UpperCamelCase    ,  return_tensors=_UpperCamelCase)
              encoding.update(_UpperCamelCase)

          return encoding



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  *_UpperCamelCase     :				List[Any]    ,  **_UpperCamelCase     :				Optional[Any])				->List[str]:


          """simple docstring"""


          return self.tokenizer.batch_decode(*_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  *_UpperCamelCase     :				List[str]    ,  **_UpperCamelCase     :				Any)				->List[Any]:


          """simple docstring"""


          return self.tokenizer.decode(*_UpperCamelCase    ,  **_UpperCamelCase)



      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.tokenizer.model_input_names
          _lowerCamelCase     :		Union[str, Any] 						=						self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  **_UpperCamelCase     :				Union[str, Any])				->str:


          """simple docstring"""


          if os.path.isfile(_UpperCamelCase):
              raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""")
          os.makedirs(_UpperCamelCase    ,  exist_ok=_UpperCamelCase)
          _lowerCamelCase     :		str 						=						os.path.join(_UpperCamelCase    ,  """qformer_tokenizer""")
          self.qformer_tokenizer.save_pretrained(_UpperCamelCase)
          return super().save_pretrained(_UpperCamelCase    ,  **_UpperCamelCase)




      @classmethod
      def 	_SCREAMING_SNAKE_CASE    (					cls     :				int    ,  _UpperCamelCase     :				str    ,  **_UpperCamelCase     :				int)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						AutoTokenizer.from_pretrained(_UpperCamelCase    ,  subfolder="""qformer_tokenizer""")
          _lowerCamelCase     :		str 						=						cls._get_arguments_from_pretrained(_UpperCamelCase    ,  **_UpperCamelCase)
          args.append(_UpperCamelCase)
          return cls(*_UpperCamelCase)





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    if donor_conc <= 0:
        raise ValueError("""Donor concentration should be positive"""	)
    elif acceptor_conc <= 0:
        raise ValueError("""Acceptor concentration should be positive"""	)
    elif intrinsic_conc <= 0:
        raise ValueError("""Intrinsic concentration should be positive"""	)
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            """Donor concentration should be greater than intrinsic concentration"""	)
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            """Acceptor concentration should be greater than intrinsic concentration"""	)
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




