



'''simple docstring'''






from typing import List, Union

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


A_							:				List[Any]						=			logging.get_logger(__name__)

A_							:				Optional[int]						=			{
    '''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 							lowercase (						UpperCamelCase_       ):


 """simple docstring"""


 UpperCAmelCase					=						"""led"""
 UpperCAmelCase					=						{
     """num_attention_heads""": """encoder_attention_heads""",
     """hidden_size""": """d_model""",
     """attention_probs_dropout_prob""": """attention_dropout""",
     """initializer_range""": """init_std""",
 }







 def __init__(							self						,a_=50_265						,a_=16_384						,a_=1_024						,a_=12						,a_=4_096						,a_=16						,a_=12						,a_=4_096						,a_=16						,a_=0.0						,a_=0.0						,a_=True						,a_=True						,a_="gelu"						,a_=1_024						,a_=0.1						,a_=0.0						,a_=0.0						,a_=0.02						,a_=2						,a_=0.0						,a_=1						,a_=0						,a_=2						,a_ = 512						,**a_						,)	->     str:
        _UpperCAmelCase						:		Tuple      =				vocab_size
        _UpperCAmelCase						:		Tuple      =				max_encoder_position_embeddings
        _UpperCAmelCase						:		Union[str, Any]      =				max_decoder_position_embeddings
        _UpperCAmelCase						:		List[str]      =				d_model
        _UpperCAmelCase						:		List[Any]      =				encoder_ffn_dim
        _UpperCAmelCase						:		List[str]      =				encoder_layers
        _UpperCAmelCase						:		Tuple      =				encoder_attention_heads
        _UpperCAmelCase						:		Any      =				decoder_ffn_dim
        _UpperCAmelCase						:		str      =				decoder_layers
        _UpperCAmelCase						:		List[str]      =				decoder_attention_heads
        _UpperCAmelCase						:		Union[str, Any]      =				dropout
        _UpperCAmelCase						:		str      =				attention_dropout
        _UpperCAmelCase						:		Dict      =				activation_dropout
        _UpperCAmelCase						:		Optional[int]      =				activation_function
        _UpperCAmelCase						:		Optional[Any]      =				init_std
        _UpperCAmelCase						:		int      =				encoder_layerdrop
        _UpperCAmelCase						:		Optional[int]      =				decoder_layerdrop
        _UpperCAmelCase						:		Any      =				classifier_dropout
        _UpperCAmelCase						:		Optional[int]      =				use_cache
        _UpperCAmelCase						:		List[str]      =				encoder_layers
        _UpperCAmelCase						:		Optional[Any]      =				attention_window

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








'''simple docstring'''






def 					snake_case_      (  lowerCAmelCase_	)->	tuple[int, int]:





       '''simple docstring'''


       if len(lowerCAmelCase_	) == 1:
              return a[0], a[0]
       else:
              # split A into half.
              _UpperCAmelCase						:		Union[str, Any]      =				a[: len(lowerCAmelCase_	) // 2]
              _UpperCAmelCase						:		str      =				a[len(lowerCAmelCase_	) // 2 :]

              # 2 sub problems, 1/2 of original size.
              _UpperCAmelCase  ,_UpperCAmelCase						:		str      =				max_difference(lowerCAmelCase_	)
              _UpperCAmelCase  ,_UpperCAmelCase						:		List[str]      =				max_difference(lowerCAmelCase_	)

              # get min of first and max of second
              # linear time
              _UpperCAmelCase						:		Dict      =				min(lowerCAmelCase_	)
              _UpperCAmelCase						:		List[Any]      =				max(lowerCAmelCase_	)

              # 3 cases, either (small1, big1),
              # (min_first, max_second), (small2, big2)
              # constant comparisons
              if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
                     return smalla, biga
              elif biga - smalla > max_second - min_first:
                     return smalla, biga
              else:
                     return min_first, max_second


if __name__ == "__main__":
   import doctest

   doctest.testmod()



