

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


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "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   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)





def 		_lowercase (     lowercase__   ,	lowercase__						):
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def 		_lowercase (     lowercase__   ,	lowercase__=0						):
      return sorted(lowercase__   ,	key=lambda lowercase__						: x[column]						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=float('''inf'''						)						):

      for i in range(points_counts - 1						):
            for j in range(i + 1   ,	lowercase__						):
                  __lowerCAmelCase					:						List[str]					=					euclidean_distance_sqr(points[i]   ,	points[j]						)
                  if current_dis < min_dis:
                        __lowerCAmelCase					:						Tuple					=					current_dis
      return min_dis







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=float('''inf'''						)						):

      for i in range(min(6   ,	points_counts - 1						)   ,	lowercase__						):
            for j in range(max(0   ,	i - 6						)   ,	lowercase__						):
                  __lowerCAmelCase					:						Union[str, Any]					=					euclidean_distance_sqr(points[i]   ,	points[j]						)
                  if current_dis < min_dis:
                        __lowerCAmelCase					:						int					=					current_dis
      return min_dis







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):

      # base case
      if points_counts <= 3:
            return dis_between_closest_pair(lowercase__   ,	lowercase__						)

      # recursion
      __lowerCAmelCase					:						Optional[Any]					=					points_counts // 2
      __lowerCAmelCase					:						Optional[Any]					=					closest_pair_of_points_sqr(
          lowercase__   ,	points_sorted_on_y[:mid]   ,	lowercase__						)
      __lowerCAmelCase					:						str					=					closest_pair_of_points_sqr(
          lowercase__   ,	points_sorted_on_y[mid:]   ,	points_counts - mid						)
      __lowerCAmelCase					:						Optional[int]					=					min(lowercase__   ,	lowercase__						)
      __lowerCAmelCase					:						Tuple					=					[]
      for point in points_sorted_on_x:
            if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
                  cross_strip.append(lowercase__						)

      __lowerCAmelCase					:						List[Any]					=					dis_between_closest_in_strip(
          lowercase__   ,	len(lowercase__						)   ,	lowercase__						)
      return min(lowercase__   ,	lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					column_based_sort(lowercase__   ,	column=0						)
      __lowerCAmelCase					:						Any					=					column_based_sort(lowercase__   ,	column=1						)
      return (
          closest_pair_of_points_sqr(
              lowercase__   ,	lowercase__   ,	lowercase__						)
      ) ** 0.5


if __name__ == "__main__":
 _UpperCamelCase 			=		[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
 print("Distance:", closest_pair_of_points(points, len(points)))


