



"""simple docstring"""






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class        __snake_case       (	_lowercase):
  snake_case__				:   str  			=						["image_processor", "tokenizer"]
  snake_case__				:   int  			=						"LayoutLMv2ImageProcessor"
  snake_case__				:   Dict  			=						("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




  def __init__( self     :						int						,					__lowerCAmelCase     :						List[str]=None						,					__lowerCAmelCase     :						List[Any]=None						,					**__lowerCAmelCase     :						str		):






         """simple docstring"""






         if "feature_extractor" in kwargs:
                warnings.warn(
                    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                    ''' instead.'''						,					__lowerCAmelCase						,					)
                _lowerCamelCase						:				int    			= kwargs.pop('''feature_extractor'''		)

         _lowerCamelCase						:				List[str]    			= 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__(__lowerCAmelCase						,					__lowerCAmelCase		)




  def __call__( self     :						List[str]						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,					__lowerCAmelCase     :						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,					__lowerCAmelCase     :						Union[List[List[int]], List[List[List[int]]]] = None						,					__lowerCAmelCase     :						Optional[Union[List[int], List[List[int]]]] = None						,					__lowerCAmelCase     :						bool = True						,					__lowerCAmelCase     :						Union[bool, str, PaddingStrategy] = False						,					__lowerCAmelCase     :						Union[bool, str, TruncationStrategy] = None						,					__lowerCAmelCase     :						Optional[int] = None						,					__lowerCAmelCase     :						int = 0						,					__lowerCAmelCase     :						Optional[int] = None						,					__lowerCAmelCase     :						Optional[bool] = None						,					__lowerCAmelCase     :						Optional[bool] = None						,					__lowerCAmelCase     :						bool = False						,					__lowerCAmelCase     :						bool = False						,					__lowerCAmelCase     :						bool = False						,					__lowerCAmelCase     :						bool = False						,					__lowerCAmelCase     :						bool = True						,					__lowerCAmelCase     :						Optional[Union[str, TensorType]] = None						,					**__lowerCAmelCase     :						List[str]						,					):






         """simple docstring"""






         if self.image_processor.apply_ocr and (boxes is not None):
                raise ValueError(
                    '''You cannot provide bounding boxes '''
                    '''if you initialized the image processor with apply_ocr set to True.'''		)

         if self.image_processor.apply_ocr and (word_labels is not None):
                raise ValueError(
                    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''		)

         if return_overflowing_tokens is True and return_offsets_mapping is False:
                raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''		)

         # first, apply the image processor
         _lowerCamelCase						:				Dict    			= self.image_processor(images=__lowerCAmelCase						,					return_tensors=__lowerCAmelCase		)

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
                if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                       _lowerCamelCase						:				List[str]    			= [text]  # add batch dimension (as the image processor always adds a batch dimension)
                _lowerCamelCase						:				Tuple    			= features['''words''']

         _lowerCamelCase						:				List[Any]    			= self.tokenizer(
             text=text if text is not None else features['''words''']						,					text_pair=text_pair if text_pair is not None else None						,					boxes=boxes if boxes is not None else features['''boxes''']						,					word_labels=__lowerCAmelCase						,					add_special_tokens=__lowerCAmelCase						,					padding=__lowerCAmelCase						,					truncation=__lowerCAmelCase						,					max_length=__lowerCAmelCase						,					stride=__lowerCAmelCase						,					pad_to_multiple_of=__lowerCAmelCase						,					return_token_type_ids=__lowerCAmelCase						,					return_attention_mask=__lowerCAmelCase						,					return_overflowing_tokens=__lowerCAmelCase						,					return_special_tokens_mask=__lowerCAmelCase						,					return_offsets_mapping=__lowerCAmelCase						,					return_length=__lowerCAmelCase						,					verbose=__lowerCAmelCase						,					return_tensors=__lowerCAmelCase						,					**__lowerCAmelCase						,					)

         # add pixel values
         _lowerCamelCase						:				Dict    			= features.pop('''pixel_values'''		)
         if return_overflowing_tokens is True:
                _lowerCamelCase						:				int    			= self.get_overflowing_images(__lowerCAmelCase						,					encoded_inputs['''overflow_to_sample_mapping''']		)
         _lowerCamelCase						:				str    			= images

         return encoded_inputs




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= []
         for sample_idx in overflow_to_sample_mapping:
                images_with_overflow.append(images[sample_idx]		)

         if len(__lowerCAmelCase		) != len(__lowerCAmelCase		):
                raise ValueError(
                    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                    f''' {len(__lowerCAmelCase		)} and {len(__lowerCAmelCase		)}'''		)

         return images_with_overflow




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					*__lowerCAmelCase     :						List[str]						,					**__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					*__lowerCAmelCase     :						Union[str, Any]						,					**__lowerCAmelCase     :						Optional[int]		):






         """simple docstring"""






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




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         return ["input_ids", "bbox", "attention_mask", "image"]




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






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




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






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



'''simple docstring'''





from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE_:     Optional[int]  =3_00  # TEMPERATURE (unit = K)





def 	lowerCAmelCase_				(					snake_case_       : float					,    snake_case_       : float					,    snake_case_       : float					,    )    ->      float:


     '''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()




