



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        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     List[str]								=     ['image_processor', 'tokenizer']
				A_   :     List[str]								=     'ViltImageProcessor'
				A_   :     str								=     ('BertTokenizer', 'BertTokenizerFast')


				def __init__(							self      :  List[str]						, __UpperCamelCase      :  Union[str, Any]=None						, __UpperCamelCase      :  Dict=None						, **__UpperCamelCase      :  Optional[Any]       )		->     int:
											A      						=    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      						=    kwargs.pop('feature_extractor'       )

											A      						=    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       )
											A      						=    self.image_processor


				def __call__(							self      :  List[Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Union[bool, str, PaddingStrategy] = False						, __UpperCamelCase      :  Union[bool, str, TruncationStrategy] = None						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  int = 0						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Optional[Union[str, TensorType]] = None						, **__UpperCamelCase      :  List[str]						, )		->     BatchEncoding:
											A      						=    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
											A      						=    self.image_processor(__UpperCamelCase						, return_tensors=__UpperCamelCase       )
											encoding.update(__UpperCamelCase       )

											return encoding


				def __UpperCamelCase						(							self      :  Any						, *__UpperCamelCase      :  Dict						, **__UpperCamelCase      :  Optional[int]       )		->     Optional[int]:
											return self.tokenizer.batch_decode(*__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]						, *__UpperCamelCase      :  str						, **__UpperCamelCase      :  Optional[Any]       )		->     Optional[int]:
											return self.tokenizer.decode(*__UpperCamelCase						, **__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     int:
											A      						=    self.tokenizer.model_input_names
											A      						=    self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )


				@property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											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 __UpperCamelCase						(							self      :  List[Any]       )		->     List[Any]:
											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						, __UpperCamelCase						, )
											return self.image_processor




import re






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							if len(re.findall('[ATCG]'			,	lowerCAmelCase__				)				) != len(lowerCAmelCase__				):
														raise ValueError('Invalid Strand'				)

							return dna.translate(dna.maketrans('ATCG'			,	'TAGC'				)				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()