




'''simple docstring'''

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 		a							(  _lowerCamelCase	):
				snake_case_           =						["image_processor", "tokenizer"]
				snake_case_           =						"Pix2StructImageProcessor"
				snake_case_           =						("T5Tokenizer", "T5TokenizerFast")


				def __init__(						self       :   Dict			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple   ):
									snake_case_		     =							False
									super().__init__(lowercase_			,							lowercase_   )


				def __call__(						self       :   Optional[Any]			,							lowercase_       :   str=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] = 2048			,							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_       :   List[Any]			,							):
									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:
														snake_case_		     =							self.tokenizer
														snake_case_		     =							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
														snake_case_		     =							self.image_processor(
														    lowercase_			,							return_tensors=lowercase_			,							max_patches=lowercase_			,							**lowercase_   )
									else:
														# add pixel_values and bbox
														snake_case_		     =							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:
														snake_case_		     =							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:
																			snake_case_		     =							text_encoding.pop('''attention_mask'''   )
														if "input_ids" in text_encoding:
																			snake_case_		     =							text_encoding.pop('''input_ids'''   )
									else:
														snake_case_		     =							None

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

									return encoding_image_processor


				def     A_					(						self       :   Union[str, Any]			,							*lowercase_       :   List[str]			,							**lowercase_       :   Dict   ):
									return self.tokenizer.batch_decode(*lowercase_			,							**lowercase_   )


				def     A_					(						self       :   Optional[int]			,							*lowercase_       :   Tuple			,							**lowercase_       :   Dict   ):
									return self.tokenizer.decode(*lowercase_			,							**lowercase_   )


				@property
				def     A_					(						self       :   List[Any]   ):
									snake_case_		     =							self.tokenizer.model_input_names
									snake_case_		     =							self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return int((input_a, input_a).count(0      ) == 0      )


def 						__magic_name__       (      )						->   None:

					'''simple docstring'''

					assert and_gate(0, 0      ) == 0
					assert and_gate(0, 1      ) == 0
					assert and_gate(1, 0      ) == 0
					assert and_gate(1, 1      ) == 1


if __name__ == "__main__":
							test_and_gate()
							print(and_gate(1, 0))
							print(and_gate(0, 0))
							print(and_gate(0, 1))
							print(and_gate(1, 1))

