
'''simple docstring'''
class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     arr.split(','     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     [int(self.array[0]     )] * len(self.array     )
											UpperCamelCase   			=     [int(self.array[0]     )] * len(self.array     )
											for i in range(1						,				len(self.array     )     ):
																		UpperCamelCase   			=     max(
																		    int(self.array[i]     ) + sum_value[i - 1]						,				int(self.array[i]     )     )
																		UpperCamelCase   			=     max(sum_value[i]						,				rear[i - 1]     )
											return rear[len(self.array     ) - 1]


if __name__ == "__main__":
						lowerCAmelCase					:    List[str]           = input('please input some numbers:')
						lowerCAmelCase					:    str           = SubArray(whole_array)
						lowerCAmelCase					:    str           = array.solve_sub_array()
						print(('the results is:', re))






'''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

lowerCAmelCase					:    Dict           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      (     enum.Enum):
				lowerCAmelCase_     			=							0
				lowerCAmelCase_     			=							1


@add_end_docstrings(snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""generated"""







				def __init__(       self						,				*A_						,				**A_     )->		Optional[int]:







											'''simple docstring'''






											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_						,				)->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     {}
											if truncation is not None:
																		UpperCamelCase   			=     truncation

											UpperCamelCase   			=     generate_kwargs

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

											if clean_up_tokenization_spaces is not None:
																		UpperCamelCase   			=     clean_up_tokenization_spaces

											if stop_sequence is not None:
																		UpperCamelCase   			=     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.'     )
																		UpperCamelCase   			=     stop_sequence_ids[0]

											return preprocess_params, forward_params, postprocess_params







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		Optional[int]:







											'''simple docstring'''






											return True







				def 						UpperCAmelCase_    (       self						,				*A_						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     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'     )
																		UpperCamelCase   			=     ([prefix + arg for arg in args[0]],)
																		UpperCamelCase   			=     True

											elif isinstance(args[0]						,				A_     ):
																		UpperCamelCase   			=     (prefix + args[0],)
																		UpperCamelCase   			=     False
											else:
																		raise ValueError(
																		    F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''     )
											UpperCamelCase   			=     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_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     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_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self._parse_and_tokenize(A_						,				truncation=A_						,				**A_     )
											return inputs







				def 						UpperCAmelCase_    (       self						,				A_						,				**A_     )->		int:







											'''simple docstring'''






											if self.framework == "pt":
																		UpperCamelCase			,	UpperCamelCase   			=     model_inputs['input_ids'].shape
											elif self.framework == "tf":
																		UpperCamelCase			,	UpperCamelCase   			=     tf.shape(model_inputs['input_ids']     ).numpy()

											UpperCamelCase   			=     generate_kwargs.get('min_length'						,				self.model.config.min_length     )
											UpperCamelCase   			=     generate_kwargs.get('max_length'						,				self.model.config.max_length     )
											self.check_inputs(A_						,				generate_kwargs['min_length']						,				generate_kwargs['max_length']     )
											UpperCamelCase   			=     self.model.generate(**A_						,				**A_     )
											UpperCamelCase   			=     output_ids.shape[0]
											if self.framework == "pt":
																		UpperCamelCase   			=     output_ids.reshape(A_						,				out_b // in_b						,				*output_ids.shape[1:]     )
											elif self.framework == "tf":
																		UpperCamelCase   			=     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     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     []
											for output_ids in model_outputs["output_ids"][0]:
																		if return_type == ReturnType.TENSORS:
																									UpperCamelCase   			=     {F'''{self.return_name}_token_ids''': output_ids}
																		elif return_type == ReturnType.TEXT:
																									UpperCamelCase   			=     {
																									    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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""summary"""







				def __call__(       self						,				*A_						,				**A_     )->		Optional[int]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		bool:







											'''simple docstring'''






											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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""translation"""







				def 						UpperCAmelCase_    (       self						,				A_						,				A_						,				A_     )->		List[Any]:







											'''simple docstring'''






											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     )->		Dict:







											'''simple docstring'''






											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_     )->		str:







											'''simple docstring'''






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







				def __call__(       self						,				*A_						,				**A_     )->		Any:







											'''simple docstring'''






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




