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  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   ['''image_processor''', '''tokenizer''']
   lowerCAmelCase      :    Any  			=   '''Pix2StructImageProcessor'''
   lowerCAmelCase      :    Optional[Any]  			=   ('''T5Tokenizer''', '''T5TokenizerFast''')



   def __init__(	self					:  Tuple		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= False
    super().__init__(_lowercase		,  _lowercase							)



   def __call__(	self					:  Union[str, Any]		,  _lowercase					:  List[Any]=None		,  _lowercase					:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,  _lowercase					:  bool = True		,  _lowercase					:  Union[bool, str, PaddingStrategy] = False		,  _lowercase					:  Union[bool, str, TruncationStrategy] = None		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  Optional[int] = 2_048		,  _lowercase					:  int = 0		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  Optional[bool] = None		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = True		,  _lowercase					:  Optional[Union[str, TensorType]] = None		,  **_lowercase					:  Optional[int]		,  ):



    """simple docstring"""



    if images is None and text is None:
     raise ValueError('''You have to specify either images or text.'''							)

    # Get only text
    if images is None and not self.image_processor.is_vqa:
     _UpperCamelCase:     Optional[Any]							= self.tokenizer
     _UpperCamelCase:     str							= self.tokenizer(
         text=_lowercase		,  add_special_tokens=_lowercase		,  padding=_lowercase		,  truncation=_lowercase		,  max_length=_lowercase		,  stride=_lowercase		,  pad_to_multiple_of=_lowercase		,  return_attention_mask=_lowercase		,  return_overflowing_tokens=_lowercase		,  return_special_tokens_mask=_lowercase		,  return_offsets_mapping=_lowercase		,  return_token_type_ids=_lowercase		,  return_length=_lowercase		,  verbose=_lowercase		,  return_tensors=_lowercase		,  **_lowercase		,  )
     return text_encoding

    if not self.image_processor.is_vqa:
     # add pixel_values
     _UpperCamelCase:     int							= self.image_processor(
         _lowercase		,  return_tensors=_lowercase		,  max_patches=_lowercase		,  **_lowercase							)
    else:
     # add pixel_values and bbox
     _UpperCamelCase:     Dict							= self.image_processor(
         _lowercase		,  return_tensors=_lowercase		,  max_patches=_lowercase		,  header_text=_lowercase		,  **_lowercase							)

    if text is not None and not self.image_processor.is_vqa:
     _UpperCamelCase:     List[Any]							= self.tokenizer(
         text=_lowercase		,  add_special_tokens=_lowercase		,  padding=_lowercase		,  truncation=_lowercase		,  max_length=_lowercase		,  stride=_lowercase		,  pad_to_multiple_of=_lowercase		,  return_attention_mask=_lowercase		,  return_overflowing_tokens=_lowercase		,  return_special_tokens_mask=_lowercase		,  return_offsets_mapping=_lowercase		,  return_token_type_ids=_lowercase		,  return_length=_lowercase		,  verbose=_lowercase		,  return_tensors=_lowercase		,  **_lowercase		,  )

     if "attention_mask" in text_encoding:
      _UpperCamelCase:     Dict							= text_encoding.pop('''attention_mask'''							)
     if "input_ids" in text_encoding:
      _UpperCamelCase:     Dict							= text_encoding.pop('''input_ids'''							)
    else:
     _UpperCamelCase:     Any							= None

    if text_encoding is not None:
     encoding_image_processor.update(_lowercase							)

    return encoding_image_processor



   def   lowerCAmelCase						(	self					:  List[Any]		,  *_lowercase					:  Optional[int]		,  **_lowercase					:  Dict							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  List[Any]		,  *_lowercase					:  Optional[Any]		,  **_lowercase					:  List[str]							):



    """simple docstring"""



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



   @property
   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= self.tokenizer.model_input_names
    _UpperCamelCase:     Optional[Any]							= self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
from __future__ import annotations

UpperCAmelCase_			    =							1.6_0_2_1E-1_9  # units = C




def       lowerCAmelCase_     (		lowercase:							float  ,					lowercase:							float  ,					lowercase:							float  ,					)    ->				tuple[str, float]:







 '''simple docstring'''
 if (conductivity, electron_conc, mobility).count(0		) != 1:
  raise ValueError('''You cannot supply more or less than 2 values'''		)
 elif conductivity < 0:
  raise ValueError('''Conductivity cannot be negative'''		)
 elif electron_conc < 0:
  raise ValueError('''Electron concentration cannot be negative'''		)
 elif mobility < 0:
  raise ValueError('''mobility cannot be negative'''		)
 elif conductivity == 0:
  return (
      "conductivity",
      mobility * electron_conc * ELECTRON_CHARGE,
  )
 elif electron_conc == 0:
  return (
      "electron_conc",
      conductivity / (mobility * ELECTRON_CHARGE),
  )
 else:
  return (
      "mobility",
      conductivity / (electron_conc * ELECTRON_CHARGE),
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()