





'''simple docstring'''




import torch

from transformers import AutoModel
class 			_lowerCAmelCase		(			torch.nn.Module	):






     """simple docstring"""






     def __init__(   self							:			str   ,       __snake_case							:			Optional[Any]="sayef/fsner-bert-base-uncased"						)->	List[Any]:
        super(__snake_case   ,       self						).__init__()

        snake_case 						=   AutoModel.from_pretrained(__snake_case   ,       return_dict=__snake_case						)
        snake_case 						=   torch.nn.CosineSimilarity(3   ,       1e-08						)
        snake_case 						=   torch.nn.Softmax(dim=1						)



     def      lowerCAmelCase			(   self							:			List[str]   ,       **__snake_case							:			str						)->	List[str]:
        return self.bert(**__snake_case						).last_hidden_state



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			Optional[Any]						)->	Optional[int]:
        return token_embeddings.sum(2   ,       keepdim=__snake_case						)



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Dict   ,       __snake_case							:			List[Any]=1						)->	Optional[int]:
        return self.softmax(T * self.cos(__snake_case   ,       __snake_case						)						)



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   W_supports["""sizes"""].tolist()
        snake_case 						=   W_supports["""start_token_id"""].item()
        snake_case 						=   W_supports["""end_token_id"""].item()

        del W_supports["sizes"]
        del W_supports["start_token_id"]
        del W_supports["end_token_id"]

        snake_case 						=   self.BERT(**__snake_case						)
        snake_case 						=   self.BERT(**__snake_case						)

        snake_case 						=   None
        snake_case 						=   None

        snake_case 						=   W_supports["""input_ids"""] == start_token_id
        snake_case 						=   W_supports["""input_ids"""] == end_token_id

        for i, size in enumerate(__snake_case						):
           if i == 0:
              snake_case 						=   0
           else:
              snake_case 						=   support_sizes[i - 1]

           snake_case 						=   S[s : s + size][start_token_masks[s : s + size]]
           snake_case 						=   S[s : s + size][end_token_masks[s : s + size]]

           snake_case 						=   torch.matmul(q[i]   ,       s_start.T						).sum(1						).softmax(0						)
           snake_case 						=   torch.matmul(q[i]   ,       s_end.T						).sum(1						).softmax(0						)

           if p_starts is not None:
              snake_case 						=   torch.vstack((p_starts, p_start)						)
              snake_case 						=   torch.vstack((p_ends, p_end)						)
           else:
              snake_case 						=   p_start
              snake_case 						=   p_end

        return p_starts, p_ends








'''simple docstring'''




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


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "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"""






     snake_case_						      =   "trajectory_transformer"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }



     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			int=1_00   ,       __snake_case							:			List[str]=5   ,       __snake_case							:			str=1   ,       __snake_case							:			Union[str, Any]=1   ,       __snake_case							:			Union[str, Any]=2_49   ,       __snake_case							:			List[Any]=6   ,       __snake_case							:			Optional[int]=17   ,       __snake_case							:			Optional[Any]=25   ,       __snake_case							:			Union[str, Any]=4   ,       __snake_case							:			List[Any]=4   ,       __snake_case							:			Optional[int]=1_28   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			int=0.1   ,       __snake_case							:			Union[str, Any]=0.00_06   ,       __snake_case							:			Tuple=5_12   ,       __snake_case							:			int=0.02   ,       __snake_case							:			Any=1e-12   ,       __snake_case							:			Optional[Any]=1   ,       __snake_case							:			List[Any]=True   ,       __snake_case							:			Dict=1   ,       __snake_case							:			Dict=5_02_56   ,       __snake_case							:			Union[str, Any]=5_02_56   ,       **__snake_case							:			Tuple   ,       )->	Optional[int]:
        snake_case 						=   vocab_size
        snake_case 						=   action_weight
        snake_case 						=   reward_weight
        snake_case 						=   value_weight
        snake_case 						=   max_position_embeddings
        snake_case 						=   block_size
        snake_case 						=   action_dim
        snake_case 						=   observation_dim
        snake_case 						=   transition_dim
        snake_case 						=   learning_rate
        snake_case 						=   n_layer
        snake_case 						=   n_head
        snake_case 						=   n_embd
        snake_case 						=   embd_pdrop
        snake_case 						=   attn_pdrop
        snake_case 						=   resid_pdrop
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   kaiming_initializer_range
        snake_case 						=   use_cache
        super().__init__(pad_token_id=__snake_case   ,       bos_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       **__snake_case						)

