


'''simple docstring'''






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 	a__       (							a__						):





       '''simple docstring'''






       lowercase__    :     Optional[Any]					=   "trajectory_transformer"
       lowercase__    :     Optional[int]					=   ["past_key_values"]
       lowercase__    :     Optional[int]					=   {
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }







       def __init__(	self      ,					lowerCamelCase_=1_00      ,					lowerCamelCase_=5      ,					lowerCamelCase_=1      ,					lowerCamelCase_=1      ,					lowerCamelCase_=2_49      ,					lowerCamelCase_=6      ,					lowerCamelCase_=17      ,					lowerCamelCase_=25      ,					lowerCamelCase_=4      ,					lowerCamelCase_=4      ,					lowerCamelCase_=1_28      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.0_006      ,					lowerCamelCase_=5_12      ,					lowerCamelCase_=0.02      ,					lowerCamelCase_=1e-12      ,					lowerCamelCase_=1      ,					lowerCamelCase_=True      ,					lowerCamelCase_=1      ,					lowerCamelCase_=5_02_56      ,					lowerCamelCase_=5_02_56      ,					**lowerCamelCase_      ,					) ->							Any:
              lowerCAmelCase__      =			vocab_size
              lowerCAmelCase__      =			action_weight
              lowerCAmelCase__      =			reward_weight
              lowerCAmelCase__      =			value_weight
              lowerCAmelCase__      =			max_position_embeddings
              lowerCAmelCase__      =			block_size
              lowerCAmelCase__      =			action_dim
              lowerCAmelCase__      =			observation_dim
              lowerCAmelCase__      =			transition_dim
              lowerCAmelCase__      =			learning_rate
              lowerCAmelCase__      =			n_layer
              lowerCAmelCase__      =			n_head
              lowerCAmelCase__      =			n_embd
              lowerCAmelCase__      =			embd_pdrop
              lowerCAmelCase__      =			attn_pdrop
              lowerCAmelCase__      =			resid_pdrop
              lowerCAmelCase__      =			initializer_range
              lowerCAmelCase__      =			layer_norm_eps
              lowerCAmelCase__      =			kaiming_initializer_range
              lowerCAmelCase__      =			use_cache
              super().__init__(pad_token_id=lowerCamelCase_      ,					bos_token_id=lowerCamelCase_      ,					eos_token_id=lowerCamelCase_      ,					**lowerCamelCase_     )



'''simple docstring'''






import doctest
from collections import deque

import numpy as np




class 	a__       :





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														lowerCAmelCase__      =			[2, 1, 2, -1]
														lowerCAmelCase__      =			[1, 2, 3, 4]







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							list[float]:
														lowerCAmelCase__      =			len(self.first_signal     )
														lowerCAmelCase__      =			len(self.second_signal     )

														lowerCAmelCase__      =			max(lowerCamelCase_      ,					lowerCamelCase_     )

														# create a zero matrix of max_length x max_length
														lowerCAmelCase__      =			[[0] * max_length for i in range(lowerCamelCase_     )]

														# fills the smaller signal with zeros to make both signals of same length
														if length_first_signal < length_second_signal:
																					self.first_signal += [0] * (max_length - length_first_signal)
														elif length_first_signal > length_second_signal:
																					self.second_signal += [0] * (max_length - length_second_signal)
														for i in range(lowerCamelCase_     ):
																					lowerCAmelCase__      =			deque(self.second_signal     )
																					rotated_signal.rotate(lowerCamelCase_     )
																					for j, item in enumerate(lowerCamelCase_     ):
																												matrix[i][j] += item

        # multiply the matrix with the first signal
														lowerCAmelCase__      =			np.matmul(np.transpose(lowerCamelCase_     )      ,					np.transpose(self.first_signal     )     )

														# rounding-off to two decimal places
														return [round(lowerCamelCase_      ,					2     ) for i in final_signal]


if __name__ == "__main__":
		doctest.testmod()