




'''simple docstring'''



from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class 		lowercase_   (			A					,  A					,  A       ):


   """simple docstring"""

   lowerCamelCase_      =						[r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']



   @register_to_config
   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Optional[int] = None  ,				__lowerCamelCase :     int = 5_0_2_5_7  ,				__lowerCamelCase :     int = 1_0_2_4  ,				__lowerCamelCase :     int = 7_6_8  ,				__lowerCamelCase :     int = 1_2  ,				__lowerCamelCase :     int = 1_2  ,				__lowerCamelCase :     Optional[int] = None  ,				__lowerCamelCase :     str = "gelu_new"  ,				__lowerCamelCase :     float = 0.1  ,				__lowerCamelCase :     float = 0.1  ,				__lowerCamelCase :     float = 0.1  ,				__lowerCamelCase :     float = 1e-5  ,				__lowerCamelCase :     float = 0.0_2  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     bool = False  ,				__lowerCamelCase :     bool = False  ,				):

      """simple docstring"""
      super().__init__()

      _SCREAMING_SNAKE_CASE	=				prefix_length

      if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
         raise ValueError(
             F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
             F""" `n_embd`: {n_embd} are not equal."""   )

      _SCREAMING_SNAKE_CASE	=				prefix_inner_dim
      _SCREAMING_SNAKE_CASE	=				prefix_hidden_dim

      _SCREAMING_SNAKE_CASE	=				(
          nn.Linear(self.prefix_inner_dim  ,				self.prefix_hidden_dim   )
          if self.prefix_hidden_dim is not None
          else nn.Identity()
      )
      _SCREAMING_SNAKE_CASE	=				(
          nn.Linear(self.prefix_hidden_dim  ,				__lowerCamelCase   ) if self.prefix_hidden_dim is not None else nn.Identity()
      )

      _SCREAMING_SNAKE_CASE	=				GPTaConfig(
          vocab_size=__lowerCamelCase  ,				n_positions=__lowerCamelCase  ,				n_embd=__lowerCamelCase  ,				n_layer=__lowerCamelCase  ,				n_head=__lowerCamelCase  ,				n_inner=__lowerCamelCase  ,				activation_function=__lowerCamelCase  ,				resid_pdrop=__lowerCamelCase  ,				embd_pdrop=__lowerCamelCase  ,				attn_pdrop=__lowerCamelCase  ,				layer_norm_epsilon=__lowerCamelCase  ,				initializer_range=__lowerCamelCase  ,				scale_attn_weights=__lowerCamelCase  ,				use_cache=__lowerCamelCase  ,				scale_attn_by_inverse_layer_idx=__lowerCamelCase  ,				reorder_and_upcast_attn=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				GPTaLMHeadModel(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     torch.Tensor  ,				__lowerCamelCase :     torch.Tensor  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				__lowerCamelCase :     Optional[torch.Tensor] = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.transformer.transformer.wte(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.encode_prefix(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.decode_prefix(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.cat((prefix_embeds, embedding_text)  ,				dim=1   )

      if labels is not None:
         _SCREAMING_SNAKE_CASE	=				self.get_dummy_token(input_ids.shape[0]  ,				input_ids.device   )
         _SCREAMING_SNAKE_CASE	=				torch.cat((dummy_token, input_ids)  ,				dim=1   )
      _SCREAMING_SNAKE_CASE	=				self.transformer(inputs_embeds=__lowerCamelCase  ,				labels=__lowerCamelCase  ,				attention_mask=__lowerCamelCase   )
      if self.prefix_hidden_dim is not None:
         return out, hidden
      else:
         return out



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     torch.device   ):

      """simple docstring"""
      return torch.zeros(__lowerCamelCase  ,				self.prefix_length  ,				dtype=torch.intaa  ,				device=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     int   ):

      """simple docstring"""
      return self.encode_prefix(__lowerCamelCase   )



   @torch.no_grad()
   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.split(__lowerCamelCase  ,				1  ,				dim=0   )
      _SCREAMING_SNAKE_CASE	=				[]
      _SCREAMING_SNAKE_CASE	=				[]
      for feature in features:
         _SCREAMING_SNAKE_CASE	=				self.decode_prefix(feature.to(__lowerCamelCase   )   )  # back to the clip feature
         # Only support beam search for now
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.generate_beam(
             input_embeds=__lowerCamelCase  ,				device=__lowerCamelCase  ,				eos_token_id=__lowerCamelCase   )
         generated_tokens.append(output_tokens[0]   )
         generated_seq_lengths.append(seq_lengths[0]   )
      _SCREAMING_SNAKE_CASE	=				torch.stack(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.stack(__lowerCamelCase   )
      return generated_tokens, generated_seq_lengths



   @torch.no_grad()
   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Any=None  ,				__lowerCamelCase :     Optional[Any]=None  ,				__lowerCamelCase :     Tuple=None  ,				__lowerCamelCase :     int = 5  ,				__lowerCamelCase :     int = 6_7  ,				__lowerCamelCase :     float = 1.0  ,				__lowerCamelCase :     Optional[int] = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				eos_token_id
      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				torch.ones(__lowerCamelCase  ,				device=__lowerCamelCase  ,				dtype=torch.int   )
      _SCREAMING_SNAKE_CASE	=				torch.zeros(__lowerCamelCase  ,				device=__lowerCamelCase  ,				dtype=torch.bool   )

      if input_embeds is not None:
         _SCREAMING_SNAKE_CASE	=				input_embeds
      else:
         _SCREAMING_SNAKE_CASE	=				self.transformer.transformer.wte(__lowerCamelCase   )

      for i in range(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				self.transformer(inputs_embeds=__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				outputs.logits
         _SCREAMING_SNAKE_CASE	=				logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
         _SCREAMING_SNAKE_CASE	=				logits.softmax(-1   ).log()

         if scores is None:
            _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				logits.topk(__lowerCamelCase  ,				-1   )
            _SCREAMING_SNAKE_CASE	=				generated.expand(__lowerCamelCase  ,				*generated.shape[1:]   )
            _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				next_tokens.permute(1  ,				0   ), scores.squeeze(0   )
            if tokens is None:
               _SCREAMING_SNAKE_CASE	=				next_tokens
            else:
               _SCREAMING_SNAKE_CASE	=				tokens.expand(__lowerCamelCase  ,				*tokens.shape[1:]   )
               _SCREAMING_SNAKE_CASE	=				torch.cat((tokens, next_tokens)  ,				dim=1   )
         else:
            _SCREAMING_SNAKE_CASE	=				-float(np.inf   )
            _SCREAMING_SNAKE_CASE	=				0
            _SCREAMING_SNAKE_CASE	=				scores[:, None] + logits
            seq_lengths[~is_stopped] += 1
            _SCREAMING_SNAKE_CASE	=				scores_sum / seq_lengths[:, None]
            _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				scores_sum_average.view(-1   ).topk(__lowerCamelCase  ,				-1   )
            _SCREAMING_SNAKE_CASE	=				next_tokens // scores_sum.shape[1]
            _SCREAMING_SNAKE_CASE	=				seq_lengths[next_tokens_source]
            _SCREAMING_SNAKE_CASE	=				next_tokens % scores_sum.shape[1]
            _SCREAMING_SNAKE_CASE	=				next_tokens.unsqueeze(1   )
            _SCREAMING_SNAKE_CASE	=				tokens[next_tokens_source]
            _SCREAMING_SNAKE_CASE	=				torch.cat((tokens, next_tokens)  ,				dim=1   )
            _SCREAMING_SNAKE_CASE	=				generated[next_tokens_source]
            _SCREAMING_SNAKE_CASE	=				scores_sum_average * seq_lengths
            _SCREAMING_SNAKE_CASE	=				is_stopped[next_tokens_source]

         _SCREAMING_SNAKE_CASE	=				self.transformer.transformer.wte(next_tokens.squeeze()   ).view(generated.shape[0]  ,				1  ,				-1   )
         _SCREAMING_SNAKE_CASE	=				torch.cat((generated, next_token_embed)  ,				dim=1   )
         _SCREAMING_SNAKE_CASE	=				is_stopped + next_tokens.eq(__lowerCamelCase   ).squeeze()
         if is_stopped.all():
            break

      _SCREAMING_SNAKE_CASE	=				scores / seq_lengths
      _SCREAMING_SNAKE_CASE	=				scores.argsort(descending=__lowerCamelCase   )
      # tokens tensors are already padded to max_seq_length
      _SCREAMING_SNAKE_CASE	=				[tokens[i] for i in order]
      _SCREAMING_SNAKE_CASE	=				torch.stack(__lowerCamelCase  ,				dim=0   )
      _SCREAMING_SNAKE_CASE	=				torch.tensor([seq_lengths[i] for i in order]  ,				dtype=seq_lengths.dtype   )
      return output_texts, seq_lengths







'''simple docstring'''



from collections.abc import Sequence
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Sequence[int] | None = None  )			->			int:
   if nums is None or not nums:
      raise ValueError("Input sequence should not be empty"  )

   _SCREAMING_SNAKE_CASE	=				nums[0]
   for i in range(1	,					len(__A  )  ):
      _SCREAMING_SNAKE_CASE	=				nums[i]
      _SCREAMING_SNAKE_CASE	=				max(__A	,					ans + num	,					__A  )

   return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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

