



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput






@dataclass
class SCREAMING_SNAKE_CASE__     (		snake_case_    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      None
    __lowerCamelCase			: Dict					=      None
    __lowerCamelCase			: Union[str, Any]					=      None
    __lowerCamelCase			: Tuple					=      None






class SCREAMING_SNAKE_CASE__     (		snake_case_    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__=512,		lowerCamelCase__="cls",		lowerCamelCase__=False,		lowerCamelCase__=True,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)
        A :       Dict          =   project_dim
        A :       Union[str, Any]          =   pooler_fn
        A :       int          =   learn_encoder
        A :       int          =   use_attention_mask






class SCREAMING_SNAKE_CASE__     (		snake_case_    ):




    '''simple docstring'''






    __lowerCamelCase			: str					=      [r"pooler", r"logit_scale"]
    __lowerCamelCase			: List[str]					=      [r"position_ids", r"predictions.decoder.bias"]
    __lowerCamelCase			: Optional[int]					=      "roberta"
    __lowerCamelCase			: Optional[Any]					=      RobertaSeriesConfig






    def __init__(       self,		lowerCamelCase__		):
        super().__init__(lowerCamelCase__		)
        A :       Tuple          =   XLMRobertaModel(lowerCamelCase__		)
        A :       List[str]          =   nn.Linear(config.hidden_size,		config.project_dim		)
        A :       Union[str, Any]          =   getattr(lowerCamelCase__,		"""has_pre_transformation""",		lowerCamelCase__		)
        if self.has_pre_transformation:
            A :       Any          =   nn.Linear(config.hidden_size,		config.project_dim		)
            A :       Optional[Any]          =   nn.LayerNorm(config.hidden_size,		eps=config.layer_norm_eps		)
        self.post_init()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		):
        A :       str          =   return_dict if return_dict is not None else self.config.use_return_dict

        A :       Tuple          =   self.base_model(
            input_ids=lowerCamelCase__,		attention_mask=lowerCamelCase__,		token_type_ids=lowerCamelCase__,		position_ids=lowerCamelCase__,		head_mask=lowerCamelCase__,		inputs_embeds=lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		encoder_attention_mask=lowerCamelCase__,		output_attentions=lowerCamelCase__,		output_hidden_states=True if self.has_pre_transformation else output_hidden_states,		return_dict=lowerCamelCase__,		)

        if self.has_pre_transformation:
            A :       Dict          =   outputs["""hidden_states"""][-2]
            A :       Union[str, Any]          =   self.pre_LN(lowerCamelCase__		)
            A :       List[Any]          =   self.transformation_pre(lowerCamelCase__		)

            return TransformationModelOutput(
                projection_state=lowerCamelCase__,		last_hidden_state=outputs.last_hidden_state,		hidden_states=outputs.hidden_states,		attentions=outputs.attentions,		)
        else:
            A :       str          =   self.transformation(outputs.last_hidden_state		)
            return TransformationModelOutput(
                projection_state=lowerCamelCase__,		last_hidden_state=outputs.last_hidden_state,		hidden_states=outputs.hidden_states,		attentions=outputs.attentions,		)









'''simple docstring'''

from collections.abc import Sequence





def lowerCamelCase						(     __lowerCamelCase :    Sequence[float]					,       __lowerCamelCase :    bool = False    )						->float:
    if not arr:
        return 0

    _SCREAMING_SNAKE_CASE									=			0 if allow_empty_subarrays else float("""-inf"""    )
    _SCREAMING_SNAKE_CASE									=			0.0
    for num in arr:
        _SCREAMING_SNAKE_CASE									=			max(0 if allow_empty_subarrays else num					,       curr_sum + num    )
        _SCREAMING_SNAKE_CASE									=			max(__lowerCamelCase					,       __lowerCamelCase    )

    return max_sum


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      lowercase_					= [-2, 1, -3, 4, -1, 2, 1, -5, 4]
      print(f"""{max_subarray_sum(nums) = }""")



