






'''simple docstring'''

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


A:      int  				=				logging.get_logger(__name__)

A:      Optional[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 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  'trajectory_transformer'
	SCREAMING_SNAKE_CASE_					:      int        =  ['past_key_values']
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}
	def __init__(							self  ,      _lowercase=100  ,      _lowercase=5  ,      _lowercase=1  ,      _lowercase=1  ,      _lowercase=249  ,      _lowercase=6  ,      _lowercase=17  ,      _lowercase=25  ,      _lowercase=4  ,      _lowercase=4  ,      _lowercase=128  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=0.00_06  ,      _lowercase=512  ,      _lowercase=0.02  ,      _lowercase=1E-1_2  ,      _lowercase=1  ,      _lowercase=True  ,      _lowercase=1  ,      _lowercase=5_0256  ,      _lowercase=5_0256  ,      **_lowercase  ,      )						->  Tuple:
							lowercase_      :   Optional[Any]        =			vocab_size
							lowercase_      :   Union[str, Any]        =			action_weight
							lowercase_      :   Any        =			reward_weight
							lowercase_      :   str        =			value_weight
							lowercase_      :   List[str]        =			max_position_embeddings
							lowercase_      :   Dict        =			block_size
							lowercase_      :   Union[str, Any]        =			action_dim
							lowercase_      :   Tuple        =			observation_dim
							lowercase_      :   Any        =			transition_dim
							lowercase_      :   Optional[int]        =			learning_rate
							lowercase_      :   Optional[int]        =			n_layer
							lowercase_      :   Tuple        =			n_head
							lowercase_      :   int        =			n_embd
							lowercase_      :   List[str]        =			embd_pdrop
							lowercase_      :   Optional[Any]        =			attn_pdrop
							lowercase_      :   Tuple        =			resid_pdrop
							lowercase_      :   List[str]        =			initializer_range
							lowercase_      :   List[str]        =			layer_norm_eps
							lowercase_      :   int        =			kaiming_initializer_range
							lowercase_      :   int        =			use_cache
							super().__init__(pad_token_id=_lowercase  ,      bos_token_id=_lowercase  ,      eos_token_id=_lowercase  ,      **_lowercase       )








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	list[list[float]]							)			->			list[list[float]]:





						"""simple docstring"""
						lowercase_      :   list[list[float]]        =			[]
						for data in source_data:
												for i, el in enumerate(a							):
																		if len(a							) < i + 1:
																								data_lists.append([]							)
																		data_lists[i].append(float(a							)							)
						return data_lists






def 	_UpperCAmelCase    (  a		:	list[list[float]]						,			a		:	list[int]							)			->			list[list[float]]:





						"""simple docstring"""
						lowercase_      :   list[list[float]]        =			[]
						for dlist, weight in zip(a						,			a							):
												lowercase_      :   Tuple        =			min(a							)
												lowercase_      :   Any        =			max(a							)

												lowercase_      :   list[float]        =			[]
												# for weight 0 score is 1 - actual score
												if weight == 0:
																		for item in dlist:
																								try:
																														score.append(1 - ((item - mind) / (maxd - mind))							)
																								except ZeroDivisionError:
																														score.append(1							)

												elif weight == 1:
																		for item in dlist:
																								try:
																														score.append((item - mind) / (maxd - mind)							)
																								except ZeroDivisionError:
																														score.append(0							)

        # weight not 0 or 1
												else:
																		lowercase_      :   str        =			f"Invalid weight of {weight:f} provided"
																		raise ValueError(a							)

												score_lists.append(a							)

						return score_lists






def 	_UpperCAmelCase    (  a		:	list[list[float]]							)			->			list[float]:





						"""simple docstring"""
						lowercase_      :   list[float]        =			[0 for i in range(len(score_lists[0]							)							)]

						for slist in score_lists:
												for j, ele in enumerate(a							):
																		lowercase_      :   List[Any]        =			final_scores[j] + ele

						return final_scores






def 	_UpperCAmelCase    (  a		:	list[list[float]]						,			a		:	list[int]							)			->			list[list[float]]:





						"""simple docstring"""
						lowercase_      :   int        =			get_data(a							)
						lowercase_      :   Optional[int]        =			calculate_each_score(a						,			a							)
						lowercase_      :   Dict        =			generate_final_scores(a							)

						# append scores to source data
						for i, ele in enumerate(a							):
												source_data[i].append(a							)

						return source_data
