import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ['image_processor', 'tokenizer']
      _snake_case    		=       'OwlViTImageProcessor'
      _snake_case    		=       ('CLIPTokenizer', 'CLIPTokenizerFast')



      def __init__(					self     :				str    ,  _UpperCamelCase     :				Dict=None    ,  _UpperCamelCase     :				Any=None    ,  **_UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""    ,  _UpperCamelCase    ,  )
              _lowerCamelCase     :		Optional[int] 						=						kwargs.pop("""feature_extractor""")

          _lowerCamelCase     :		List[Any] 						=						image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError("""You need to specify an `image_processor`.""")
          if tokenizer is None:
              raise ValueError("""You need to specify a `tokenizer`.""")

          super().__init__(_UpperCamelCase    ,  _UpperCamelCase)



      def __call__(					self     :				int    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				Optional[int]=None    ,  _UpperCamelCase     :				Dict=None    ,  _UpperCamelCase     :				Dict="max_length"    ,  _UpperCamelCase     :				List[str]="np"    ,  **_UpperCamelCase     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          if text is None and query_images is None and images is None:
              raise ValueError(
                  """You have to specify at least one text or query image or image. All three cannot be none.""")

          if text is not None:
              if isinstance(_UpperCamelCase    ,  _UpperCamelCase) or (isinstance(_UpperCamelCase    ,  _UpperCamelCase) and not isinstance(text[0]    ,  _UpperCamelCase)):
                  _lowerCamelCase     :		int 						=						[self.tokenizer(_UpperCamelCase    ,  padding=_UpperCamelCase    ,  return_tensors=_UpperCamelCase    ,  **_UpperCamelCase)]

              elif isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(text[0]    ,  _UpperCamelCase):
                  _lowerCamelCase     :		Union[str, Any] 						=						[]

                  # Maximum number of queries across batch
                  _lowerCamelCase     :		List[str] 						=						max([len(_UpperCamelCase) for t in text])

                  # Pad all batch samples to max number of text queries
                  for t in text:
                      if len(_UpperCamelCase) != max_num_queries:
                          _lowerCamelCase     :		Any 						=						t + [""" """] * (max_num_queries - len(_UpperCamelCase))

                      _lowerCamelCase     :		int 						=						self.tokenizer(_UpperCamelCase    ,  padding=_UpperCamelCase    ,  return_tensors=_UpperCamelCase    ,  **_UpperCamelCase)
                      encodings.append(_UpperCamelCase)
              else:
                  raise TypeError("""Input text should be a string, a list of strings or a nested list of strings""")

              if return_tensors == "np":
                  _lowerCamelCase     :		List[str] 						=						np.concatenate([encoding["""input_ids"""] for encoding in encodings]    ,  axis=0)
                  _lowerCamelCase     :		Union[str, Any] 						=						np.concatenate([encoding["""attention_mask"""] for encoding in encodings]    ,  axis=0)

              elif return_tensors == "jax" and is_flax_available():
                  import jax.numpy as jnp

                  _lowerCamelCase     :		str 						=						jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]    ,  axis=0)
                  _lowerCamelCase     :		Optional[Any] 						=						jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]    ,  axis=0)

              elif return_tensors == "pt" and is_torch_available():
                  import torch

                  _lowerCamelCase     :		str 						=						torch.cat([encoding["""input_ids"""] for encoding in encodings]    ,  dim=0)
                  _lowerCamelCase     :		List[Any] 						=						torch.cat([encoding["""attention_mask"""] for encoding in encodings]    ,  dim=0)

              elif return_tensors == "tf" and is_tf_available():
                  import tensorflow as tf

                  _lowerCamelCase     :		List[str] 						=						tf.stack([encoding["""input_ids"""] for encoding in encodings]    ,  axis=0)
                  _lowerCamelCase     :		List[str] 						=						tf.stack([encoding["""attention_mask"""] for encoding in encodings]    ,  axis=0)

              else:
                  raise ValueError("""Target return tensor type could not be returned""")

              _lowerCamelCase     :		Any 						=						BatchEncoding()
              _lowerCamelCase     :		str 						=						input_ids
              _lowerCamelCase     :		Union[str, Any] 						=						attention_mask

          if query_images is not None:
              _lowerCamelCase     :		Optional[Any] 						=						BatchEncoding()
              _lowerCamelCase     :		int 						=						self.image_processor(
                  _UpperCamelCase    ,  return_tensors=_UpperCamelCase    ,  **_UpperCamelCase).pixel_values
              _lowerCamelCase     :		Tuple 						=						query_pixel_values

          if images is not None:
              _lowerCamelCase     :		Any 						=						self.image_processor(_UpperCamelCase    ,  return_tensors=_UpperCamelCase    ,  **_UpperCamelCase)

          if text is not None and images is not None:
              _lowerCamelCase     :		List[str] 						=						image_features.pixel_values
              return encoding
          elif query_images is not None and images is not None:
              _lowerCamelCase     :		List[str] 						=						image_features.pixel_values
              return encoding
          elif text is not None or query_images is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**_UpperCamelCase)    ,  tensor_type=_UpperCamelCase)



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


          """simple docstring"""


          return self.image_processor.post_process(*_UpperCamelCase    ,  **_UpperCamelCase)



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


          """simple docstring"""


          return self.image_processor.post_process_object_detection(*_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  *_UpperCamelCase     :				Dict    ,  **_UpperCamelCase     :				str)				->int:


          """simple docstring"""


          return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase    ,  **_UpperCamelCase)



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


          """simple docstring"""


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



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


          """simple docstring"""


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



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,  _UpperCamelCase    ,  )
          return self.image_processor_class




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->List[Any]:


          """simple docstring"""


          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,  _UpperCamelCase    ,  )
          return self.image_processor





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()




