




"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Dict     =      '''pegasus'''
 UpperCAmelCase       :	Any     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[Any]=50_265       ,				_UpperCAmelCase			:			Optional[int]=1_024       ,				_UpperCAmelCase			:			Any=12       ,				_UpperCAmelCase			:			Optional[Any]=4_096       ,				_UpperCAmelCase			:			str=16       ,				_UpperCAmelCase			:			Any=12       ,				_UpperCAmelCase			:			List[str]=4_096       ,				_UpperCAmelCase			:			Any=16       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Optional[Any]=0.0       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			Optional[Any]="gelu"       ,				_UpperCAmelCase			:			Any=1_024       ,				_UpperCAmelCase			:			List[str]=0.1       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			List[Any]=0.0       ,				_UpperCAmelCase			:			Optional[Any]=0.02       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Optional[Any]=False       ,				_UpperCAmelCase			:			Union[str, Any]=0       ,				_UpperCAmelCase			:			Any=1       ,				_UpperCAmelCase			:			Tuple=1       ,				**_UpperCAmelCase			:			str       ,				):
       _A     =  vocab_size
       _A     =  max_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     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				is_encoder_decoder=_UpperCAmelCase       ,				decoder_start_token_id=_UpperCAmelCase       ,				forced_eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

 @property
 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       return self.encoder_attention_heads



 @property
 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       return self.d_model








'''simple docstring'''

from __future__ import annotations

import math





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   bool		,			_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   float			):

    if depth < 0:
        raise ValueError("Depth cannot be less than 0"			)

    if len(_lowerCamelCase			) == 0:
        raise ValueError("Scores cannot be empty"			)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)

    return min(
        minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)





def 		UpperCamelCase							(				):
    A__							=     [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
    A__							=     math.log(len(_lowerCamelCase			)		,			2			)
    print("Optimal value : "		,			end=""			)
    print(minimax(0		,			0		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



