






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





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    ["image_processor", "tokenizer"]
							A_       =    "EfficientNetImageProcessor"
							A_       =    ("BertTokenizer", "BertTokenizerFast")

							def __init__(     self:      int						,   __A:      Dict						,   __A:      Tuple      )			-> Tuple:
												super().__init__(__A						,   __A      )

							def __call__(     self:      str						,   __A:      Tuple=None						,   __A:      List[Any]=None						,   __A:      str="max_length"						,   __A:      Union[str, Any]=64						,   __A:      List[Any]=None						,   **__A:      Union[str, Any]      )			-> int:
												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									=       self.tokenizer(
																	    __A						,   padding=__A						,   max_length=__A						,   return_tensors=__A						,   **__A      )

												if images is not None:
																	_A									=       self.image_processor(__A						,   return_tensors=__A						,   **__A      )

												if text is not None and images is not None:
																	_A									=       image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**__A      )						,   tensor_type=__A      )

							def 					__A       (     self:      Optional[Any]						,   *__A:      str						,   **__A:      str      )			-> List[Any]:
												return self.tokenizer.batch_decode(*__A						,   **__A      )

							def 					__A       (     self:      int						,   *__A:      Dict						,   **__A:      Tuple      )			-> Optional[int]:
												return self.tokenizer.decode(*__A						,   **__A      )



							@property
							def 					__A       (     self:      List[Any]      )			-> Dict:
												_A									=       self.tokenizer.model_input_names
												_A									=       self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )











from __future__ import annotations

from collections.abc import Generator




def    __A    (   _lowercase       ):

					'''simple docstring'''







					if not isinstance(_lowercase    ,	_lowercase       ) or n < 1:
										raise Exception('''Sequence only defined for positive integers'''       )

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_A									=       3 * n + 1
										yield n




def    __A    (   ):

					'''simple docstring'''







					_A									=       43
					_A									=       tuple(collatz_sequence(_lowercase       )       )
					print(_lowercase       )
					print(f"""Collatz sequence from {n} took {len(_lowercase       )} steps."""       )


if __name__ == "__main__":
				main()



