




from typing import List, Union

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


SCREAMING_SNAKE_CASE_    :       int       =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    :       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 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[Any]							=     """led"""
     __UpperCAmelCase						:			Tuple							=     {
         """num_attention_heads""": """encoder_attention_heads""",
         """hidden_size""": """d_model""",
         """attention_probs_dropout_prob""": """attention_dropout""",
         """initializer_range""": """init_std""",
     }

     def __init__(   self			:							str    ,					A			:							Optional[Any]=5_02_65    ,					A			:							List[Any]=1_63_84    ,					A			:							Any=10_24    ,					A			:							List[Any]=12    ,					A			:							Union[str, Any]=40_96    ,					A			:							Any=16    ,					A			:							Optional[Any]=12    ,					A			:							Dict=40_96    ,					A			:							List[Any]=16    ,					A			:							Optional[int]=0.0    ,					A			:							Optional[int]=0.0    ,					A			:							List[Any]=True    ,					A			:							Tuple=True    ,					A			:							int="gelu"    ,					A			:							Union[str, Any]=10_24    ,					A			:							int=0.1    ,					A			:							Optional[int]=0.0    ,					A			:							Any=0.0    ,					A			:							Optional[Any]=0.0_2    ,					A			:							Optional[int]=2    ,					A			:							List[Any]=0.0    ,					A			:							List[Any]=1    ,					A			:							List[str]=0    ,					A			:							List[Any]=2    ,					A			:							Union[List[int], int] = 5_12    ,					**A			:							Optional[int]    ,					)      ->      int:






         """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 itertools import count
from math import asin, pi, sqrt
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    return (
        (1 - 2 * point) * sqrt(point - point**2		) + 2 * point + asin(sqrt(1 - point		)		)
    ) / 4
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							(circles_number + 1 - sqrt(2 * circles_number		)) / (
        2 * (circles_number**2 + 1)
    )
    a__  			=							circles_number * intersection_y

    a__  			=							intersection_x * intersection_y / 2
    a__  			=							circle_bottom_arc_integral(
        1 / 2		) - circle_bottom_arc_integral(UpperCAmelCase_		)

    return triangle_area + concave_region_area
def 							__lowerCAmelCase			(UpperCAmelCase_ = 1 / 1_000		):
    a__  			=							(1 - pi / 4) / 4

    for n in count(1		):
        if concave_triangle_area(UpperCAmelCase_		) / l_section_area < fraction:
            return n

    return -1


if __name__ == "__main__":
 print(F"{solution() = }")






