



from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase								=   logging.get_logger(__name__)

__lowerCamelCase								=   {
    """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/config.json""",
    # See all LED models at https://huggingface.co/models?filter=led
}



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     '''led'''
 lowerCamelCase_       =     {
     '''num_attention_heads''': '''encoder_attention_heads''',
     '''hidden_size''': '''d_model''',
     '''attention_probs_dropout_prob''': '''attention_dropout''',
     '''initializer_range''': '''init_std''',
 }
 def __init__(  self		,      a=5_0_2_6_5		,      a=1_6_3_8_4		,      a=1_0_2_4		,      a=1_2		,      a=4_0_9_6		,      a=1_6		,      a=1_2		,      a=4_0_9_6		,      a=1_6		,      a=0.0		,      a=0.0		,      a=True		,      a=True		,      a="gelu"		,      a=1_0_2_4		,      a=0.1		,      a=0.0		,      a=0.0		,      a=0.02		,      a=2		,      a=0.0		,      a=1		,      a=0		,      a=2		,      a = 5_1_2		,      **a		,      )		->							List[str]:




   """simple docstring"""







   _A          =      vocab_size
   _A          =      max_encoder_position_embeddings
   _A          =      max_decoder_position_embeddings
   _A          =      d_model
   _A          =      encoder_ffn_dim
   _A          =      encoder_layers
   _A          =      encoder_attention_heads
   _A          =      decoder_ffn_dim
   _A          =      decoder_layers
   _A          =      decoder_attention_heads
   _A          =      dropout
   _A          =      attention_dropout
   _A          =      activation_dropout
   _A          =      activation_function
   _A          =      init_std
   _A          =      encoder_layerdrop
   _A          =      decoder_layerdrop
   _A          =      classifier_dropout
   _A          =      use_cache
   _A          =      encoder_layers
   _A          =      attention_window

   super().__init__(
       pad_token_id=a		,      bos_token_id=a		,      eos_token_id=a		,      is_encoder_decoder=a		,      decoder_start_token_id=a		,      **a		,      )




from __future__ import annotations




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case							)				->	int:

  # Base Case
  if counter == 0 or capacity == 0:
    return 0

  # If weight of the nth item is more than Knapsack of capacity,
  #   then this item cannot be included in the optimal solution,
  # else return the maximum of two cases:
  #   (1) nth item included
  #   (2) not included
  if weights[counter - 1] > capacity:
    return knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
  else:
    _A          =      capacity - weights[counter - 1]
    _A          =      values[counter - 1] + knapsack(
        __snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    _A          =      knapsack(__snake_case  ,			__snake_case  ,			__snake_case  ,			counter - 1							)
    return max(__snake_case  ,			__snake_case							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()