






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


__UpperCAmelCase							:   Optional[int]							=  logging.get_logger(__name__)

__UpperCAmelCase							:   Tuple							=  {
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}


class 					_snake_case						(			snake_case__ ):
	_A          =   'trajectory_transformer'
	_A          =   ['past_key_values']
	_A          =   {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}






	def __init__(		self					,UpperCamelCase=100					,UpperCamelCase=5					,UpperCamelCase=1					,UpperCamelCase=1					,UpperCamelCase=249					,UpperCamelCase=6					,UpperCamelCase=17					,UpperCamelCase=25					,UpperCamelCase=4					,UpperCamelCase=4					,UpperCamelCase=128					,UpperCamelCase=0.1					,UpperCamelCase=0.1					,UpperCamelCase=0.1					,UpperCamelCase=0.0006					,UpperCamelCase=512					,UpperCamelCase=0.02					,UpperCamelCase=1E-12					,UpperCamelCase=1					,UpperCamelCase=True					,UpperCamelCase=1					,UpperCamelCase=50_256					,UpperCamelCase=50_256					,**UpperCamelCase					,)						->    Tuple:
					snake_case__      :Dict         =   vocab_size
					snake_case__      :Union[str, Any]         =   action_weight
					snake_case__      :Tuple         =   reward_weight
					snake_case__      :Optional[Any]         =   value_weight
					snake_case__      :Union[str, Any]         =   max_position_embeddings
					snake_case__      :Tuple         =   block_size
					snake_case__      :Optional[int]         =   action_dim
					snake_case__      :str         =   observation_dim
					snake_case__      :Dict         =   transition_dim
					snake_case__      :Optional[Any]         =   learning_rate
					snake_case__      :Optional[int]         =   n_layer
					snake_case__      :int         =   n_head
					snake_case__      :Any         =   n_embd
					snake_case__      :str         =   embd_pdrop
					snake_case__      :List[Any]         =   attn_pdrop
					snake_case__      :Any         =   resid_pdrop
					snake_case__      :Union[str, Any]         =   initializer_range
					snake_case__      :List[str]         =   layer_norm_eps
					snake_case__      :int         =   kaiming_initializer_range
					snake_case__      :int         =   use_cache
					super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE					,bos_token_id=_SCREAMING_SNAKE_CASE					,eos_token_id=_SCREAMING_SNAKE_CASE					,**_SCREAMING_SNAKE_CASE       )







from __future__ import annotations

import math





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							list[int]	,	__snake_case     :							float      )     -> int:





				'''simple docstring'''


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

				if len(__snake_case      ) == 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	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)

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





def 					lowercase_ (    )     -> None:





				'''simple docstring'''


				snake_case__      :List[Any]         =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
				snake_case__      :int         =   math.log(len(__snake_case      )	,	2      )
				print("Optimal value : "	,	end=""      )
				print(minimax(0	,	0	,	__snake_case	,	__snake_case	,	__snake_case      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()