





import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
				import tensorflow as tf

				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

UpperCAmelCase						:   Optional[int]			  =     logging.get_logger(__name__)





class 						_A(					enum.Enum   ):


				"""simple docstring"""


				UpperCamelCase    :  List[Any]					    =	0
				UpperCamelCase    :  List[Any]					    =	1





@add_end_docstrings(snake_case__   )
class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[Any]					    =	'''generated'''



				def __init__(			self     ,					*_A     ,					**_A     ):
									super().__init__(*_A     ,					**_A     )

									self.check_model_type(
									    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
									    if self.framework == 'tf'
									    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING     )



				def 					UpperCAmelCase_		(			self     ,					_A=None     ,					_A=None     ,					_A=None     ,					_A=None     ,					_A=None     ,					_A=None     ,					**_A     ,					):
									__A	:						Union[str, Any]       =				{}
									if truncation is not None:
														__A	:						Any       =				truncation

									__A	:						str       =				generate_kwargs

									__A	:						List[Any]       =				{}
									if return_tensors is not None and return_type is None:
														__A	:						Optional[int]       =				ReturnType.TENSORS if return_tensors else ReturnType.TEXT
									if return_type is not None:
														__A	:						List[str]       =				return_type

									if clean_up_tokenization_spaces is not None:
														__A	:						str       =				clean_up_tokenization_spaces

									if stop_sequence is not None:
														__A	:						Optional[Any]       =				self.tokenizer.encode(_A     ,					add_special_tokens=_A     )
														if len(_A     ) > 1:
																			warnings.warn(
																			    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
																			    ' the stop sequence will be used as the stop sequence string in the interim.'     )
														__A	:						Optional[int]       =				stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									return True



				def 					UpperCAmelCase_		(			self     ,					*_A     ,					_A     ):
									__A	:						Any       =				self.model.config.prefix if self.model.config.prefix is not None else ''
									if isinstance(args[0]     ,					_A     ):
														if self.tokenizer.pad_token_id is None:
																			raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'     )
														__A	:						Any       =				([prefix + arg for arg in args[0]],)
														__A	:						List[str]       =				True

									elif isinstance(args[0]     ,					_A     ):
														__A	:						Optional[int]       =				(prefix + args[0],)
														__A	:						Tuple       =				False
									else:
														raise ValueError(
														    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""     )
									__A	:						Tuple       =				self.tokenizer(*_A     ,					padding=_A     ,					truncation=_A     ,					return_tensors=self.framework     )
									# This is produced by tokenizers but is an invalid generate kwargs
									if "token_type_ids" in inputs:
														del inputs["token_type_ids"]
									return inputs



				def __call__(			self     ,					*_A     ,					**_A     ):
									__A	:						Optional[int]       =				super().__call__(*_A     ,					**_A     )
									if (
									    isinstance(args[0]     ,					_A     )
									    and all(isinstance(_A     ,					_A     ) for el in args[0]     )
									    and all(len(_A     ) == 1 for res in result     )
									):
														return [res[0] for res in result]
									return result



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=TruncationStrategy.DO_NOT_TRUNCATE     ,					**_A     ):
									__A	:						List[Any]       =				self._parse_and_tokenize(_A     ,					truncation=_A     ,					**_A     )
									return inputs



				def 					UpperCAmelCase_		(			self     ,					_A     ,					**_A     ):
									if self.framework == "pt":
														__A    ,				__A	:						Optional[Any]       =				model_inputs['input_ids'].shape
									elif self.framework == "tf":
														__A    ,				__A	:						Tuple       =				tf.shape(model_inputs['input_ids']     ).numpy()

									__A	:						str       =				generate_kwargs.get('min_length'     ,					self.model.config.min_length     )
									__A	:						List[str]       =				generate_kwargs.get('max_length'     ,					self.model.config.max_length     )
									self.check_inputs(_A     ,					generate_kwargs['min_length']     ,					generate_kwargs['max_length']     )
									__A	:						Tuple       =				self.model.generate(**_A     ,					**_A     )
									__A	:						str       =				output_ids.shape[0]
									if self.framework == "pt":
														__A	:						Any       =				output_ids.reshape(_A     ,					out_b // in_b     ,					*output_ids.shape[1:]     )
									elif self.framework == "tf":
														__A	:						Optional[Any]       =				tf.reshape(_A     ,					(in_b, out_b // in_b, *output_ids.shape[1:])     )
									return {"output_ids": output_ids}





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=ReturnType.TEXT     ,					_A=False     ):
									__A	:						Any       =				[]
									for output_ids in model_outputs["output_ids"][0]:
														if return_type == ReturnType.TENSORS:
																			__A	:						str       =				{F"""{self.return_name}_token_ids""": output_ids}
														elif return_type == ReturnType.TEXT:
																			__A	:						Optional[Any]       =				{
																			    F"""{self.return_name}_text""": self.tokenizer.decode(
																			        _A     ,					skip_special_tokens=_A     ,					clean_up_tokenization_spaces=_A     ,					)
																			}
														records.append(_A     )
									return records





@add_end_docstrings(snake_case__   )
class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  List[str]					    =	'''summary'''



				def __call__(			self     ,					*_A     ,					**_A     ):
									return super().__call__(*_A     ,					**_A     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if max_length < min_length:
														logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}."""     )

									if input_length < max_length:
														logger.warning(
														    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
														    'a summarization task, where outputs shorter than the input are typically wanted, you might '
														    F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""     )





@add_end_docstrings(snake_case__   )
class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Tuple					    =	'''translation'''



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if input_length > 0.9 * max_length:
														logger.warning(
														    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
														    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'     )
									return True



				def 					UpperCAmelCase_		(			self     ,					*_A     ,					_A=TruncationStrategy.DO_NOT_TRUNCATE     ,					_A=None     ,					_A=None     ):
									if getattr(self.tokenizer     ,					'_build_translation_inputs'     ,					_A     ):
														return self.tokenizer._build_translation_inputs(
														    *_A     ,					return_tensors=self.framework     ,					truncation=_A     ,					src_lang=_A     ,					tgt_lang=_A     )
									else:
														return super()._parse_and_tokenize(*_A     ,					truncation=_A     )



				def 					UpperCAmelCase_		(			self     ,					_A=None     ,					_A=None     ,					**_A     ):
									__A    ,				__A    ,				__A	:						int       =				super()._sanitize_parameters(**_A     )
									if src_lang is not None:
														__A	:						List[str]       =				src_lang
									if tgt_lang is not None:
														__A	:						Optional[int]       =				tgt_lang
									if src_lang is None and tgt_lang is None:
														# Backward compatibility, direct arguments use is preferred.
														__A	:						int       =				kwargs.get('task'     ,					self.task     )
														__A	:						List[str]       =				task.split('_'     )
														if task and len(_A     ) == 4:
																			# translation, XX, to YY
																			__A	:						str       =				items[1]
																			__A	:						Optional[int]       =				items[3]
									return preprocess_params, forward_params, postprocess_params





				def __call__(			self     ,					*_A     ,					**_A     ):
									return super().__call__(*_A     ,					**_A     )











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      bool:
					return str(a  ) == str(a  )[::-1]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					return int(a  ) + int(str(a  )[::-1]  )







def  _SCREAMING_SNAKE_CASE				(   a = 1_00_00  )      ->      int:
					__A	:						int       =				[]
					for num in range(1					,						a  ):
										__A	:						List[str]       =				0
										__A	:						List[Any]       =				num
										while iterations < 50:
															__A	:						str       =				sum_reverse(a  )
															iterations += 1
															if is_palindrome(a  ):
																				break
										else:
															lychrel_nums.append(a  )
					return len(a  )


if __name__ == "__main__":
				print(F"""{solution() = }""")




