

'''simple docstring'''


from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowerCAmelCase__						:       List[Any]										=						logging.get_logger(__name__)




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								if isinstance(snake_case_			,			snake_case_  ):
										snake_case__						:       str       						=  [label.strip() for label in labels.split(''','''  ) if label.strip()]
								return labels







						def __call__(							self					:      List[str]			,			snake_case_					:      Any			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								if len(snake_case_  ) == 0 or len(snake_case_  ) == 0:
										raise ValueError('''You must include at least one label and at least one sequence.'''  )
								if hypothesis_template.format(labels[0]  ) == hypothesis_template:
										raise ValueError(
										    (
										        '''The provided hypothesis_template "{}" was not able to be formatted with the target labels. '''
										        '''Make sure the passed template includes formatting syntax such as {{}} where the label should go.'''
										    ).format(snake_case_  )  )

								if isinstance(snake_case_			,			snake_case_  ):
										snake_case__						:       Dict       						=  [sequences]

								snake_case__						:       Optional[int]       						=  []
								for sequence in sequences:
										sequence_pairs.extend([[sequence, hypothesis_template.format(snake_case_  )] for label in labels]  )

								return sequence_pairs, sequences





@add_end_docstrings(SCREAMING_SNAKE_CASE       )
class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      Optional[Any]			,			snake_case_					:      Tuple=ZeroShotClassificationArgumentHandler()			,			*snake_case_					:      Any			,			**snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  args_parser
								super().__init__(*snake_case_			,			**snake_case_  )
								if self.entailment_id == -1:
										logger.warning(
										    '''Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '''
										    '''-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.'''  )







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								for label, ind in self.model.config.labelaid.items():
										if label.lower().startswith('''entail'''  ):
												return ind
								return -1







						def       __magic_name__							(							self					:      int			,			snake_case_					:      Tuple			,			snake_case_					:      List[Any]=True			,			snake_case_					:      Optional[Any]=True			,			snake_case_					:      Tuple=TruncationStrategy.ONLY_FIRST			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.framework
								if self.tokenizer.pad_token is None:
										# Override for tokenizers not supporting padding
										logger.error(
										    '''Tokenizer was not supporting padding necessary for zero-shot, attempting to use '''
										    ''' `pad_token=eos_token`'''  )
										snake_case__						:       Optional[int]       						=  self.tokenizer.eos_token
								try:
										snake_case__						:       List[str]       						=  self.tokenizer(
										    snake_case_			,			add_special_tokens=snake_case_			,			return_tensors=snake_case_			,			padding=snake_case_			,			truncation=snake_case_			,			)
								except Exception as e:
										if "too short" in str(snake_case_  ):
												# tokenizers might yell that we want to truncate
												# to a value that is not even reached by the input.
												# In that case we don't want to truncate.
												# It seems there's not a really better way to catch that
												# exception.

												snake_case__						:       Tuple       						=  self.tokenizer(
												    snake_case_			,			add_special_tokens=snake_case_			,			return_tensors=snake_case_			,			padding=snake_case_			,			truncation=TruncationStrategy.DO_NOT_TRUNCATE			,			)
										else:
												raise e

								return inputs







						def       __magic_name__							(							self					:      Any			,			**snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								if kwargs.get('''multi_class'''			,			snake_case_  ) is not None:
										snake_case__						:       str       						=  kwargs['''multi_class''']
										logger.warning(
										    '''The `multi_class` argument has been deprecated and renamed to `multi_label`. '''
										    '''`multi_class` will be removed in a future version of Transformers.'''  )
								snake_case__						:       List[Any]       						=  {}
								if "candidate_labels" in kwargs:
										snake_case__						:       Union[str, Any]       						=  self._args_parser._parse_labels(kwargs['''candidate_labels''']  )
								if "hypothesis_template" in kwargs:
										snake_case__						:       Dict       						=  kwargs['''hypothesis_template''']

								snake_case__						:       Dict       						=  {}
								if "multi_label" in kwargs:
										snake_case__						:       Optional[int]       						=  kwargs['''multi_label''']
								return preprocess_params, {}, postprocess_params







						def __call__(							self					:      Optional[Any]			,			snake_case_					:      Union[str, List[str]]			,			*snake_case_					:      str			,			**snake_case_					:      int			,			):



								'''simple docstring'''



								if len(snake_case_  ) == 0:
										pass
								elif len(snake_case_  ) == 1 and "candidate_labels" not in kwargs:
										snake_case__						:       Dict       						=  args[0]
								else:
										raise ValueError(F"""Unable to understand extra arguments {args}"""  )

								return super().__call__(snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Any			,			snake_case_					:      Any=None			,			snake_case_					:      str="This example is {}."  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       Any       						=  self._args_parser(snake_case_			,			snake_case_			,			snake_case_  )

								for i, (candidate_label, sequence_pair) in enumerate(zip(snake_case_			,			snake_case_  )  ):
										snake_case__						:       List[Any]       						=  self._parse_and_tokenize([sequence_pair]  )

										yield {
										    "candidate_label": candidate_label,
										    "sequence": sequences[0],
										    "is_last": i == len(snake_case_  ) - 1,
										    **model_input,
										}







						def       __magic_name__							(							self					:      str			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  inputs['''candidate_label''']
								snake_case__						:       Any       						=  inputs['''sequence''']
								snake_case__						:       List[str]       						=  {k: inputs[k] for k in self.tokenizer.model_input_names}
								snake_case__						:       Tuple       						=  self.model(**snake_case_  )

								snake_case__						:       Any       						=  {
								    '''candidate_label''': candidate_label,
								    '''sequence''': sequence,
								    '''is_last''': inputs['''is_last'''],
								    **outputs,
								}
								return model_outputs







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      str			,			snake_case_					:      List[Any]=False  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  [outputs['''candidate_label'''] for outputs in model_outputs]
								snake_case__						:       List[Any]       						=  [outputs['''sequence'''] for outputs in model_outputs]
								snake_case__						:       Union[str, Any]       						=  np.concatenate([output['''logits'''].numpy() for output in model_outputs]  )
								snake_case__						:       Optional[Any]       						=  logits.shape[0]
								snake_case__						:       int       						=  len(snake_case_  )
								snake_case__						:       str       						=  N // n
								snake_case__						:       Tuple       						=  logits.reshape((num_sequences, n, -1)  )

								if multi_label or len(snake_case_  ) == 1:
										# softmax over the entailment vs. contradiction dim for each label independently
										snake_case__						:       Optional[int]       						=  self.entailment_id
										snake_case__						:       str       						=  -1 if entailment_id == 0 else 0
										snake_case__						:       int       						=  reshaped_outputs[..., [contradiction_id, entailment_id]]
										snake_case__						:       Union[str, Any]       						=  np.exp(snake_case_  ) / np.exp(snake_case_  ).sum(-1			,			keepdims=snake_case_  )
										snake_case__						:       int       						=  scores[..., 1]
								else:
										# softmax the "entailment" logits over all candidate labels
										snake_case__						:       Tuple       						=  reshaped_outputs[..., self.entailment_id]
										snake_case__						:       str       						=  np.exp(snake_case_  ) / np.exp(snake_case_  ).sum(-1			,			keepdims=snake_case_  )

								snake_case__						:       Optional[Any]       						=  list(reversed(scores[0].argsort()  )  )
								return {
								    "sequence": sequences[0],
								    "labels": [candidate_labels[i] for i in top_inds],
								    "scores": scores[0, top_inds].tolist(),
								}






'''simple docstring'''


from collections.abc import Sequence




def 	_a       (				__lowerCAmelCase		:      Sequence[int] | None = None     ):

		"""simple docstring"""

		if nums is None or not nums:
				raise ValueError('''Input sequence should not be empty'''     )

		snake_case__						:       Optional[Any]       						=  nums[0]
		for i in range(1   ,							len(__lowerCAmelCase     )     ):
				snake_case__						:       Tuple       						=  nums[i]
				snake_case__						:       Optional[int]       						=  max(__lowerCAmelCase   ,							ans + num   ,							__lowerCAmelCase     )

		return ans


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Try on a sample input from the user
					lowerCAmelCase__						:       str										=						int(input("""Enter number of elements : """).strip())
					lowerCAmelCase__						:       Dict										=						list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
					print(max_subsequence_sum(array))



