
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A							     = logging.get_logger(__name__)

A							     = {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			'plbart'
  _UpperCamelCase							=			['past_key_values']
  _UpperCamelCase							=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(self	:			List[str]						,			lowerCAmelCase_	:			str=5_00_05						,			lowerCAmelCase_	:			Any=10_24						,			lowerCAmelCase_	:			Dict=6						,			lowerCAmelCase_	:			int=30_72						,			lowerCAmelCase_	:			Union[str, Any]=12						,			lowerCAmelCase_	:			Union[str, Any]=6						,			lowerCAmelCase_	:			Any=30_72						,			lowerCAmelCase_	:			List[Any]=12						,			lowerCAmelCase_	:			str=0.0						,			lowerCAmelCase_	:			Any=0.0						,			lowerCAmelCase_	:			Optional[int]=True						,			lowerCAmelCase_	:			Optional[Any]=True						,			lowerCAmelCase_	:			Optional[int]="gelu"						,			lowerCAmelCase_	:			Tuple=7_68						,			lowerCAmelCase_	:			str=0.1						,			lowerCAmelCase_	:			Union[str, Any]=0.1						,			lowerCAmelCase_	:			str=0.0						,			lowerCAmelCase_	:			int=0.02						,			lowerCAmelCase_	:			List[Any]=0.0						,			lowerCAmelCase_	:			Optional[Any]=True						,			lowerCAmelCase_	:			List[str]=1						,			lowerCAmelCase_	:			Union[str, Any]=0						,			lowerCAmelCase_	:			List[str]=2						,			lowerCAmelCase_	:			Union[str, Any]=2						,			**lowerCAmelCase_	:			List[str]						,			) ->							Dict:
        snake_case_    							=				vocab_size
        snake_case_    							=				max_position_embeddings
        snake_case_    							=				d_model
        snake_case_    							=				encoder_ffn_dim
        snake_case_    							=				encoder_layers
        snake_case_    							=				encoder_attention_heads
        snake_case_    							=				decoder_ffn_dim
        snake_case_    							=				decoder_layers
        snake_case_    							=				decoder_attention_heads
        snake_case_    							=				dropout
        snake_case_    							=				attention_dropout
        snake_case_    							=				activation_dropout
        snake_case_    							=				activation_function
        snake_case_    							=				init_std
        snake_case_    							=				encoder_layerdrop
        snake_case_    							=				decoder_layerdrop
        snake_case_    							=				classifier_dropout
        snake_case_    							=				use_cache
        snake_case_    							=				encoder_layers
        snake_case_    							=				scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase_						,			bos_token_id=lowerCAmelCase_						,			eos_token_id=lowerCAmelCase_						,			is_encoder_decoder=lowerCAmelCase_						,			forced_eos_token_id=lowerCAmelCase_						,			**lowerCAmelCase_						,			)



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  @property
  def 		_lowerCamelCase						(self	:			Any     ) ->							Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "sequence"}),
                ("attention_mask", {0: "batch", 1: "sequence"}),
            ]     )


  @property
  def 		_lowerCamelCase						(self	:			Dict     ) ->							Mapping[str, Mapping[int, str]]:
        if self.use_past:
              return OrderedDict(
                  [
                      ("last_hidden_state", {0: "batch", 1: "sequence"}),
                      ("past_keys", {0: "batch", 2: "sequence"}),
                      ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                  ]     )
        else:
              return OrderedDict(
                  [
                      ("last_hidden_state", {0: "batch", 1: "sequence"}),
                      ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                  ]     )


"""simple docstring"""


def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				sorted(A     ,					key=lambda A   : value[2]     ,					reverse=A   )

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

     # Finding the maximum profit and the count of jobs
     lowercase__								=				0
     lowercase__								=				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:
                    lowercase__								=				job[0]
                    count += 1
                    max_profit += job[2]
                    break
     return [count, max_profit]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




