
"""simple docstring"""
def  lowercase     (_lowerCAmelCase						):
						assert isinstance(_lowerCAmelCase ,    _lowerCAmelCase						), f"""The input value of [n={number}] is not an integer"""

						if number == 1:
												return 2
						elif number < 1:
												__lowerCAmelCase     =  f"""The input value of [n={number}] has to be > 0"""
												raise ValueError(_lowerCAmelCase						)
						else:
												__lowerCAmelCase     =  sylvester(number - 1						)
												__lowerCAmelCase     =  num - 1
												__lowerCAmelCase     =  num
												return lower * upper + 1


if __name__ == "__main__":
			print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")


"""simple docstring"""
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

SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				enum.Enum ):



							'''simple docstring'''





							_snake_case						      =		0
							_snake_case						      =		1
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''generated'''




							def __init__( self    ,				*snake_case_    ,				**snake_case_				)       ->  Optional[int]:
													super().__init__(*snake_case_    ,				**snake_case_				)

													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    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				**snake_case_    ,				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  {}
													if truncation is not None:
																			__lowerCAmelCase     =  truncation

													__lowerCAmelCase     =  generate_kwargs

													__lowerCAmelCase     =  {}
													if return_tensors is not None and return_type is None:
																			__lowerCAmelCase     =  ReturnType.TENSORS if return_tensors else ReturnType.TEXT
													if return_type is not None:
																			__lowerCAmelCase     =  return_type

													if clean_up_tokenization_spaces is not None:
																			__lowerCAmelCase     =  clean_up_tokenization_spaces

													if stop_sequence is not None:
																			__lowerCAmelCase     =  self.tokenizer.encode(snake_case_    ,				add_special_tokens=snake_case_				)
																			if len(snake_case_				) > 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."""				)
																			__lowerCAmelCase     =  stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Any:
													return True




							def    A__						( self    ,				*snake_case_    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  self.model.config.prefix if self.model.config.prefix is not None else """"""
													if isinstance(args[0]    ,				snake_case_				):
																			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"""				)
																			__lowerCAmelCase     =  ([prefix + arg for arg in args[0]],)
																			__lowerCAmelCase     =  True

													elif isinstance(args[0]    ,				snake_case_				):
																			__lowerCAmelCase     =  (prefix + args[0],)
																			__lowerCAmelCase     =  False
													else:
																			raise ValueError(
																			    f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""				)
													__lowerCAmelCase     =  self.tokenizer(*snake_case_    ,				padding=snake_case_    ,				truncation=snake_case_    ,				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    ,				*snake_case_    ,				**snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  super().__call__(*snake_case_    ,				**snake_case_				)
													if (
													    isinstance(args[0]    ,				snake_case_				)
													    and all(isinstance(snake_case_    ,				snake_case_				) for el in args[0]				)
													    and all(len(snake_case_				) == 1 for res in result				)
													):
																			return [res[0] for res in result]
													return result




							def    A__						( self    ,				snake_case_    ,				snake_case_=TruncationStrategy.DO_NOT_TRUNCATE    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self._parse_and_tokenize(snake_case_    ,				truncation=snake_case_    ,				**snake_case_				)
													return inputs




							def    A__						( self    ,				snake_case_    ,				**snake_case_				)       ->  Union[str, Any]:
													if self.framework == "pt":
																			__lowerCAmelCase   ,     __lowerCAmelCase     =  model_inputs["""input_ids"""].shape
													elif self.framework == "tf":
																			__lowerCAmelCase   ,     __lowerCAmelCase     =  tf.shape(model_inputs["""input_ids"""]				).numpy()

													__lowerCAmelCase     =  generate_kwargs.get("""min_length"""    ,				self.model.config.min_length				)
													__lowerCAmelCase     =  generate_kwargs.get("""max_length"""    ,				self.model.config.max_length				)
													self.check_inputs(snake_case_    ,				generate_kwargs["""min_length"""]    ,				generate_kwargs["""max_length"""]				)
													__lowerCAmelCase     =  self.model.generate(**snake_case_    ,				**snake_case_				)
													__lowerCAmelCase     =  output_ids.shape[0]
													if self.framework == "pt":
																			__lowerCAmelCase     =  output_ids.reshape(snake_case_    ,				out_b // in_b    ,				*output_ids.shape[1:]				)
													elif self.framework == "tf":
																			__lowerCAmelCase     =  tf.reshape(snake_case_    ,				(in_b, out_b // in_b, *output_ids.shape[1:])				)
													return {"output_ids": output_ids}




							def    A__						( self    ,				snake_case_    ,				snake_case_=ReturnType.TEXT    ,				snake_case_=False				)       ->  Dict:
													__lowerCAmelCase     =  []
													for output_ids in model_outputs["output_ids"][0]:
																			if return_type == ReturnType.TENSORS:
																									__lowerCAmelCase     =  {f"""{self.return_name}_token_ids""": output_ids}
																			elif return_type == ReturnType.TEXT:
																									__lowerCAmelCase     =  {
																									    f"""{self.return_name}_text""": self.tokenizer.decode(
																									        snake_case_    ,				skip_special_tokens=snake_case_    ,				clean_up_tokenization_spaces=snake_case_    ,				)
																									}
																			records.append(snake_case_				)
													return records
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''summary'''




							def __call__( self    ,				*snake_case_    ,				**snake_case_				)       ->  Tuple:
													return super().__call__(*snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  bool:
													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(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''translation'''




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Optional[int]:
													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    A__						( self    ,				*snake_case_    ,				snake_case_=TruncationStrategy.DO_NOT_TRUNCATE    ,				snake_case_=None    ,				snake_case_=None				)       ->  List[Any]:
													if getattr(self.tokenizer    ,				"""_build_translation_inputs"""    ,				snake_case_				):
																			return self.tokenizer._build_translation_inputs(
																			    *snake_case_    ,				return_tensors=self.framework    ,				truncation=snake_case_    ,				src_lang=snake_case_    ,				tgt_lang=snake_case_				)
													else:
																			return super()._parse_and_tokenize(*snake_case_    ,				truncation=snake_case_				)




							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				**snake_case_				)       ->  List[Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  super()._sanitize_parameters(**snake_case_				)
													if src_lang is not None:
																			__lowerCAmelCase     =  src_lang
													if tgt_lang is not None:
																			__lowerCAmelCase     =  tgt_lang
													if src_lang is None and tgt_lang is None:
																			# Backward compatibility, direct arguments use is preferred.
																			__lowerCAmelCase     =  kwargs.get("""task"""    ,				self.task				)
																			__lowerCAmelCase     =  task.split("""_"""				)
																			if task and len(snake_case_				) == 4:
																									# translation, XX, to YY
																									__lowerCAmelCase     =  items[1]
																									__lowerCAmelCase     =  items[3]
													return preprocess_params, forward_params, postprocess_params




							def __call__( self    ,				*snake_case_    ,				**snake_case_				)       ->  List[Any]:
													return super().__call__(*snake_case_    ,				**snake_case_				)
