
"""simple docstring"""




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


UpperCAmelCase__      :    str    							= logging.get_logger(__name__)

UpperCAmelCase__      :    int    							= {
    '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 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   '''trajectory_transformer'''
				__UpperCamelCase      :						str					       =   ['''past_key_values''']
				__UpperCamelCase      :						Optional[Any]					       =   {
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=1_00   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=2_49   ,       SCREAMING_SNAKE_CASE__=6   ,       SCREAMING_SNAKE_CASE__=17   ,       SCREAMING_SNAKE_CASE__=25   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=1_28   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.0006   ,       SCREAMING_SNAKE_CASE__=5_12   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=5_02_56   ,       SCREAMING_SNAKE_CASE__=5_02_56   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   vocab_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   action_weight
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   reward_weight
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   value_weight
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   max_position_embeddings
											SCREAMING_SNAKE_CASE__						:       Dict           =   block_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   action_dim
											SCREAMING_SNAKE_CASE__						:       List[str]           =   observation_dim
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   transition_dim
											SCREAMING_SNAKE_CASE__						:       List[str]           =   learning_rate
											SCREAMING_SNAKE_CASE__						:       Dict           =   n_layer
											SCREAMING_SNAKE_CASE__						:       Dict           =   n_head
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   n_embd
											SCREAMING_SNAKE_CASE__						:       str           =   embd_pdrop
											SCREAMING_SNAKE_CASE__						:       int           =   attn_pdrop
											SCREAMING_SNAKE_CASE__						:       Any           =   resid_pdrop
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       str           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   kaiming_initializer_range
											SCREAMING_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__    )











import random


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       List[Any]  )->tuple:
       A__					,	A__					,	A__											=						[], [], []
       for element in data:
              if element < pivot:
                     less.append(UpperCamelCase__  )
              elif element > pivot:
                     greater.append(UpperCamelCase__  )
              else:
                     equal.append(UpperCamelCase__  )
       return less, equal, greater


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       int  )->Optional[int]:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(UpperCamelCase__  ) or index < 0:
              return None

       A__											=						items[random.randint(0 ,     len(UpperCamelCase__  ) - 1  )]
       A__											=						0
       A__					,	A__					,	A__											=						_partition(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(UpperCamelCase__ ,     UpperCamelCase__  )
       # must be in larger
       else:
              return quick_select(UpperCamelCase__ ,     index - (m + count)  )





