import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 					A							(    UpperCAmelCase_					):
			__UpperCAmelCase			:			int              =      ['image_processor', 'tokenizer']
			__UpperCAmelCase			:			Tuple              =      'ViTImageProcessor'
			__UpperCAmelCase			:			Dict              =      ('CLIPTokenizer', 'CLIPTokenizerFast')


			def __init__(self					:						str	,  __UpperCAmelCase					:						Optional[Any]=None	,  __UpperCAmelCase					:						Any=None	,  **__UpperCAmelCase					:						Any		)						->    Optional[int]:
									"""simple docstring"""



									UpperCAmelCase__								=  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	,  )
															UpperCAmelCase__								=  kwargs.pop("feature_extractor"		)

									UpperCAmelCase__								=  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					:						List[str]	,  __UpperCAmelCase					:						List[Any]=None	,  __UpperCAmelCase					:						List[Any]=None	,  __UpperCAmelCase					:						Dict=None	,  __UpperCAmelCase					:						Optional[int]=None	,  **__UpperCAmelCase					:						Optional[Any]		)						->    Optional[Any]:
									"""simple docstring"""



									if text is None and visual_prompt is None and images is None:
															raise ValueError("You have to specify either text, visual prompt or images."		)

									if text is not None and visual_prompt is not None:
															raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."		)

									if text is not None:
															UpperCAmelCase__								=  self.tokenizer(__UpperCAmelCase	,  return_tensors=__UpperCAmelCase	,  **__UpperCAmelCase		)

									if visual_prompt is not None:
															UpperCAmelCase__								=  self.image_processor(__UpperCAmelCase	,  return_tensors=__UpperCAmelCase	,  **__UpperCAmelCase		)

									if images is not None:
															UpperCAmelCase__								=  self.image_processor(__UpperCAmelCase	,  return_tensors=__UpperCAmelCase	,  **__UpperCAmelCase		)

									if visual_prompt is not None and images is not None:
															UpperCAmelCase__								=  {
															    "pixel_values": image_features.pixel_values,
															    "conditional_pixel_values": prompt_features.pixel_values,
															}
															return encoding
									elif text is not None and images is not None:
															UpperCAmelCase__								=  image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									elif visual_prompt is not None:
															UpperCAmelCase__								=  {
															    "conditional_pixel_values": prompt_features.pixel_values,
															}
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCAmelCase		)	,  tensor_type=__UpperCAmelCase		)


			def       lowercase_				(self					:						List[str]	,  *__UpperCAmelCase					:						Tuple	,  **__UpperCAmelCase					:						str		)						->    Dict:
									"""simple docstring"""



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


			def       lowercase_				(self					:						List[Any]	,  *__UpperCAmelCase					:						Optional[Any]	,  **__UpperCAmelCase					:						Any		)						->    List[str]:
									"""simple docstring"""



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


			@property
			def       lowercase_				(self					:						Tuple		)						->    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       lowercase_				(self					:						Optional[Any]		)						->    Dict:
									"""simple docstring"""



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




from __future__ import annotations




def        lowerCAmelCase_			(					__A,  __A,  __A,  )					->    tuple:







      '''simple docstring'''


      if (electron_conc, hole_conc, intrinsic_conc).count(0							) != 1:
            raise ValueError("You cannot supply more or less than 2 values"							)
      elif electron_conc < 0:
            raise ValueError("Electron concentration cannot be negative in a semiconductor"							)
      elif hole_conc < 0:
            raise ValueError("Hole concentration cannot be negative in a semiconductor"							)
      elif intrinsic_conc < 0:
            raise ValueError(
                "Intrinsic concentration cannot be negative in a semiconductor"							)
      elif electron_conc == 0:
            return (
                "electron_conc",
                intrinsic_conc**2 / hole_conc,
            )
      elif hole_conc == 0:
            return (
                "hole_conc",
                intrinsic_conc**2 / electron_conc,
            )
      elif intrinsic_conc == 0:
            return (
                "intrinsic_conc",
                (electron_conc * hole_conc) ** 0.5,
            )
      else:
            return (-1, -1)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



