




from typing import List, Union

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


SCREAMING_SNAKE_CASE__ :   Any						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__ :   List[str]						=     {
    '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 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			Union[str, Any]			  =   """led"""
    lowerCamelCase_   :			Any			  =   {
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
        """initializer_range""": """init_std""",
    }







    def __init__(					self  ,						UpperCamelCase__=5_0265  ,						UpperCamelCase__=1_6384  ,						UpperCamelCase__=1024  ,						UpperCamelCase__=12  ,						UpperCamelCase__=4096  ,						UpperCamelCase__=16  ,						UpperCamelCase__=12  ,						UpperCamelCase__=4096  ,						UpperCamelCase__=16  ,						UpperCamelCase__=0.0  ,						UpperCamelCase__=0.0  ,						UpperCamelCase__=True  ,						UpperCamelCase__=True  ,						UpperCamelCase__="gelu"  ,						UpperCamelCase__=1024  ,						UpperCamelCase__=0.1  ,						UpperCamelCase__=0.0  ,						UpperCamelCase__=0.0  ,						UpperCamelCase__=0.02  ,						UpperCamelCase__=2  ,						UpperCamelCase__=0.0  ,						UpperCamelCase__=1  ,						UpperCamelCase__=0  ,						UpperCamelCase__=2  ,						UpperCamelCase__ = 512  ,						**UpperCamelCase__  ,						)  ->   Optional[int]:
           lowerCamelCase     :   Any    				=      vocab_size
           lowerCamelCase     :   Dict    				=      max_encoder_position_embeddings
           lowerCamelCase     :   Optional[Any]    				=      max_decoder_position_embeddings
           lowerCamelCase     :   int    				=      d_model
           lowerCamelCase     :   Any    				=      encoder_ffn_dim
           lowerCamelCase     :   Any    				=      encoder_layers
           lowerCamelCase     :   str    				=      encoder_attention_heads
           lowerCamelCase     :   List[Any]    				=      decoder_ffn_dim
           lowerCamelCase     :   Dict    				=      decoder_layers
           lowerCamelCase     :   Optional[int]    				=      decoder_attention_heads
           lowerCamelCase     :   Optional[int]    				=      dropout
           lowerCamelCase     :   Tuple    				=      attention_dropout
           lowerCamelCase     :   Optional[Any]    				=      activation_dropout
           lowerCamelCase     :   Dict    				=      activation_function
           lowerCamelCase     :   Any    				=      init_std
           lowerCamelCase     :   Optional[int]    				=      encoder_layerdrop
           lowerCamelCase     :   Union[str, Any]    				=      decoder_layerdrop
           lowerCamelCase     :   int    				=      classifier_dropout
           lowerCamelCase     :   Union[str, Any]    				=      use_cache
           lowerCamelCase     :   Optional[Any]    				=      encoder_layers
           lowerCamelCase     :   int    				=      attention_window

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












from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:
       assert (
           isinstance(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) and mask > 0
       ), f'''mask needs to be positive integer, your input {mask}'''
       lowerCamelCase     :   str    				=      []
       lowerCamelCase     :   Any    				=      mask

       while submask:
              all_submasks.append(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Union[str, Any]    				=      (submask - 1) & mask

       return all_submasks


if __name__ == "__main__":
       import doctest

       doctest.testmod()






