

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

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







class  __lowerCamelCase				(	__snake_case  ,							__snake_case  ,							__snake_case	):







      @register_to_config
      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase = False							,  )						->       Union[str, Any]:
        super().__init__()

        snake_case_       						=      nn.Linear(lowerCamelCase							,  lowerCamelCase							,  bias=lowerCamelCase   )

        snake_case_       						=      nn.Embedding(lowerCamelCase							,  lowerCamelCase   )
        snake_case_       						=      False

        snake_case_       						=      nn.Dropout(p=lowerCamelCase   )

        snake_case_       						=      TaConfig(
            d_model=lowerCamelCase							,  num_heads=lowerCamelCase							,  d_kv=lowerCamelCase							,  d_ff=lowerCamelCase							,  feed_forward_proj=lowerCamelCase							,  dropout_rate=lowerCamelCase							,  is_decoder=lowerCamelCase							,  is_encoder_decoder=lowerCamelCase							,  )
        snake_case_       						=      nn.ModuleList()
        for lyr_num in range(lowerCamelCase   ):
          snake_case_       						=      TaBlock(lowerCamelCase   )
          self.encoders.append(lowerCamelCase   )

        snake_case_       						=      TaLayerNorm(lowerCamelCase   )
        snake_case_       						=      nn.Dropout(p=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       Any:
        snake_case_       						=      self.input_proj(lowerCamelCase   )

        # terminal relative positional encodings
        snake_case_       						=      encoder_inputs.shape[1]
        snake_case_       						=      torch.arange(lowerCamelCase							,  device=encoder_inputs.device   )

        snake_case_       						=      encoder_inputs_mask.sum(-1   )
        snake_case_       						=      torch.roll(input_positions.unsqueeze(0   )							,  tuple(seq_lens.tolist()   )							,  dims=0   )
        x += self.position_encoding(lowerCamelCase   )

        snake_case_       						=      self.dropout_pre(lowerCamelCase   )

        # inverted the attention mask
        snake_case_       						=      encoder_inputs.size()
        snake_case_       						=      self.get_extended_attention_mask(lowerCamelCase							,  lowerCamelCase   )

        for lyr in self.encoders:
          snake_case_       						=      lyr(lowerCamelCase							,  lowerCamelCase   )[0]
        snake_case_       						=      self.layer_norm(lowerCamelCase   )

        return self.dropout_post(lowerCamelCase   ), encoder_inputs_mask






def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						list:
       UpperCamelCase_      				=     sorted(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[2]    ,						reverse=UpperCamelCase_     )

       # Create a list of size equal to the maximum deadline
       # and initialize it with -1
       UpperCamelCase_      				=     max(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[1]     )[1]
       UpperCamelCase_      				=     [-1] * max_deadline

       # Finding the maximum profit and the count of jobs
       UpperCamelCase_      				=     0
       UpperCamelCase_      				=     0
       for job in jobs:
              # Find a free time slot for this job
              # (Note that we start from the last possible slot)
              for i in range(job[1] - 1    ,						-1    ,						-1     ):
                     if time_slots[i] == -1:
                            UpperCamelCase_      				=     job[0]
                            count += 1
                            max_profit += job[2]
                            break
       return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

