
import warnings

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







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					["image_processor", "tokenizer"]
	_lowerCAmelCase											=					"AutoImageProcessor"
	_lowerCAmelCase											=					"AutoTokenizer"





	def __init__(      self					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							**UpperCamelCase__		):
					A__	:						Optional[int]								=				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__					,							)
									A__	:						Optional[Any]								=				kwargs.pop('''feature_extractor'''		)

					A__	:						List[Any]								=				image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
									raise ValueError('''You have to specify an image_processor.'''		)
					if tokenizer is None:
									raise ValueError('''You have to specify a tokenizer.'''		)

					super().__init__(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						Dict								=				self.image_processor





	def __call__(      self					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							**UpperCamelCase__		):

					if text is None and images is None:
									raise ValueError('''You have to specify either text or images. Both cannot be none.'''		)

					if text is not None:
									A__	:						int								=				self.tokenizer(UpperCamelCase__					,							return_tensors=UpperCamelCase__					,							**UpperCamelCase__		)

					if images is not None:
									A__	:						Tuple								=				self.image_processor(UpperCamelCase__					,							return_tensors=UpperCamelCase__					,							**UpperCamelCase__		)

					if text is not None and images is not None:
									A__	:						Union[str, Any]								=				image_features.pixel_values
									return encoding
					elif text is not None:
									return encoding
					else:
									return BatchEncoding(data=dict(**UpperCamelCase__		)					,							tensor_type=UpperCamelCase__		)





	def 	__snake_case					(      self					,							*UpperCamelCase__					,							**UpperCamelCase__		):
					return self.tokenizer.batch_decode(*UpperCamelCase__					,							**UpperCamelCase__		)





	def 	__snake_case					(      self					,							*UpperCamelCase__					,							**UpperCamelCase__		):
					return self.tokenizer.decode(*UpperCamelCase__					,							**UpperCamelCase__		)





	@property
	def 	__snake_case					(      self		):
					A__	:						Union[str, Any]								=				self.tokenizer.model_input_names
					A__	:						List[str]								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





	@property
	def 	__snake_case					(      self		):
					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 	__snake_case					(      self		):
					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,							UpperCamelCase__					,							)
					return self.image_processor

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652E26,
}

_SCREAMING_SNAKE_CASE     :   dict[str, float]							 =      {
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.660540199E-27,
}

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    str				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    float							)			->							float:

				"""simple docstring"""







				if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
								A__	:						int								=				(
								    F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
								    F"Supported values are: {', '.join(__UpperCamelCase							)}"
								)
								raise ValueError(__UpperCamelCase							)
				return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()