


from typing import List, Union

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


_A		:					List[str]          =							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 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	Dict       =						"led"
     UpperCAmelCase__			:	Optional[Any]       =						{
         "num_attention_heads": "encoder_attention_heads",
         "hidden_size": "d_model",
         "attention_probs_dropout_prob": "attention_dropout",
         "initializer_range": "init_std",
     }





     def __init__(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tuple=50265     ,						SCREAMING_SNAKE_CASE_       :					Any=16384     ,						SCREAMING_SNAKE_CASE_       :					Any=1024     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=12     ,						SCREAMING_SNAKE_CASE_       :					str=4096     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]=16     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=12     ,						SCREAMING_SNAKE_CASE_       :					Any=4096     ,						SCREAMING_SNAKE_CASE_       :					str=16     ,						SCREAMING_SNAKE_CASE_       :					Any=0.0     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=0.0     ,						SCREAMING_SNAKE_CASE_       :					Any=True     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=True     ,						SCREAMING_SNAKE_CASE_       :					Any="gelu"     ,						SCREAMING_SNAKE_CASE_       :					Dict=1024     ,						SCREAMING_SNAKE_CASE_       :					Dict=0.1     ,						SCREAMING_SNAKE_CASE_       :					Any=0.0     ,						SCREAMING_SNAKE_CASE_       :					Tuple=0.0     ,						SCREAMING_SNAKE_CASE_       :					Tuple=0.02     ,						SCREAMING_SNAKE_CASE_       :					str=2     ,						SCREAMING_SNAKE_CASE_       :					Dict=0.0     ,						SCREAMING_SNAKE_CASE_       :					Dict=1     ,						SCREAMING_SNAKE_CASE_       :					List[str]=0     ,						SCREAMING_SNAKE_CASE_       :					Tuple=2     ,						SCREAMING_SNAKE_CASE_       :					Union[List[int], int] = 512     ,						**SCREAMING_SNAKE_CASE_       :					Any     ,						):
        __lowerCamelCase:					Any	 =						vocab_size
        __lowerCamelCase:					str	 =						max_encoder_position_embeddings
        __lowerCamelCase:					Any	 =						max_decoder_position_embeddings
        __lowerCamelCase:					Dict	 =						d_model
        __lowerCamelCase:					Dict	 =						encoder_ffn_dim
        __lowerCamelCase:					List[Any]	 =						encoder_layers
        __lowerCamelCase:					str	 =						encoder_attention_heads
        __lowerCamelCase:					List[Any]	 =						decoder_ffn_dim
        __lowerCamelCase:					int	 =						decoder_layers
        __lowerCamelCase:					int	 =						decoder_attention_heads
        __lowerCamelCase:					str	 =						dropout
        __lowerCamelCase:					List[Any]	 =						attention_dropout
        __lowerCamelCase:					Optional[Any]	 =						activation_dropout
        __lowerCamelCase:					List[str]	 =						activation_function
        __lowerCamelCase:					Optional[Any]	 =						init_std
        __lowerCamelCase:					int	 =						encoder_layerdrop
        __lowerCamelCase:					Union[str, Any]	 =						decoder_layerdrop
        __lowerCamelCase:					int	 =						classifier_dropout
        __lowerCamelCase:					str	 =						use_cache
        __lowerCamelCase:					Optional[Any]	 =						encoder_layers
        __lowerCamelCase:					List[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_     ,						)






_A		:					Union[str, Any]          =							1_000



def      __lowerCAmelCase    (				snake_case						:     float			)							->				float:
   return x * x



def      __lowerCAmelCase    (				snake_case						:     Optional[int]    ,   snake_case						:     float    ,   snake_case						:     float    ,   snake_case						:     int = 4			)							->				float:
   assert callable(
       snake_case			), f'the function(object) passed should be callable your input : {function}'
   assert isinstance(snake_case    ,   (float, int)			), f'a should be float or integer your input : {a}'
   assert isinstance(function(snake_case			)    ,   (float, int)			), (
       "the function should return integer or float return type of your function, "
       f'{type(snake_case			)}'
   )
   assert isinstance(snake_case    ,   (float, int)			), f'b should be float or integer your input : {b}'
   assert (
       isinstance(snake_case    ,   snake_case			) and precision > 0
   ), f'precision should be positive integer your input : {precision}'

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   __lowerCamelCase:					List[str]	 =						(b - a) / N_STEPS
   __lowerCamelCase:					Optional[Any]	 =						function(snake_case			) + function(snake_case			)

   for i in range(1    ,   snake_case			):
      __lowerCamelCase:					Dict	 =						a + h * i
      result += function(snake_case			) * (4 if i % 2 else 2)

   result *= h / 3
   return round(snake_case    ,   snake_case			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


