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      __lowercase					(    __snake_case    ):
						lowercase					 =     ['image_processor', 'tokenizer']
						lowercase					 =     'ViltImageProcessor'
						lowercase					 =     ('BertTokenizer', 'BertTokenizerFast')

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      str=None							,							__lowerCamelCase				:      int=None							,							**__lowerCamelCase				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		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							,							)
												lowercase							=		kwargs.pop('''feature_extractor'''   )

									lowercase							=		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   )
									lowercase							=		self.image_processor

						def __call__(	self				:      str							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Any = None							,							__lowerCamelCase				:      Dict = True							,							__lowerCamelCase				:      List[Any] = False							,							__lowerCamelCase				:      Any = None							,							__lowerCamelCase				:      Dict = None							,							__lowerCamelCase				:      Dict = 0							,							__lowerCamelCase				:      Dict = None							,							__lowerCamelCase				:      Optional[int] = None							,							__lowerCamelCase				:      Optional[int] = None							,							__lowerCamelCase				:      Union[str, Any] = False							,							__lowerCamelCase				:      List[str] = False							,							__lowerCamelCase				:      Optional[Any] = False							,							__lowerCamelCase				:      str = False							,							__lowerCamelCase				:      Tuple = True							,							__lowerCamelCase				:      Optional[int] = None							,							**__lowerCamelCase				:      Any							,							)							->		int:


									'''simple docstring'''

									lowercase							=		self.tokenizer(
									    text=__UpperCamelCase							,							add_special_tokens=__UpperCamelCase							,							padding=__UpperCamelCase							,							truncation=__UpperCamelCase							,							max_length=__UpperCamelCase							,							stride=__UpperCamelCase							,							pad_to_multiple_of=__UpperCamelCase							,							return_token_type_ids=__UpperCamelCase							,							return_attention_mask=__UpperCamelCase							,							return_overflowing_tokens=__UpperCamelCase							,							return_special_tokens_mask=__UpperCamelCase							,							return_offsets_mapping=__UpperCamelCase							,							return_length=__UpperCamelCase							,							verbose=__UpperCamelCase							,							return_tensors=__UpperCamelCase							,							**__UpperCamelCase							,							)
									# add pixel_values + pixel_mask
									lowercase							=		self.image_processor(__UpperCamelCase							,							return_tensors=__UpperCamelCase   )
									encoding.update(__UpperCamelCase   )

									return encoding

						def        __a		(	self				:      str							,							*__lowerCamelCase				:      Dict							,							**__lowerCamelCase				:      List[Any]   )							->		int:


									'''simple docstring'''

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

						def        __a		(	self				:      Any							,							*__lowerCamelCase				:      str							,							**__lowerCamelCase				:      Tuple   )							->		Dict:


									'''simple docstring'''

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

						@property
						def        __a		(	self				:      str   )							->		str:


									'''simple docstring'''

									lowercase							=		self.tokenizer.model_input_names
									lowercase							=		self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		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        __a		(	self				:      Union[str, 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






import cmath
import math






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       complex:



			"""simple docstring"""







			lowercase							=		math.radians(UpperCAmelCase       )
			lowercase							=		math.radians(UpperCAmelCase       )

			# Convert voltage and current to rectangular form
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
						import doctest

						doctest.testmod()





