






'''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     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Tuple			  =    '''trajectory_transformer'''
 __a      :    Optional[Any]			  =    ['''past_key_values''']
 __a      :    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=0.00_06      ,  __lowerCAmelCase=5_12      ,  __lowerCAmelCase=0.02      ,  __lowerCAmelCase=1E-12      ,  __lowerCAmelCase=1      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=1      ,  __lowerCAmelCase=5_02_56      ,  __lowerCAmelCase=5_02_56      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				vocab_size
       UpperCamelCase_					: Union[str, Any]            =				action_weight
       UpperCamelCase_					: List[Any]            =				reward_weight
       UpperCamelCase_					: Tuple            =				value_weight
       UpperCamelCase_					: int            =				max_position_embeddings
       UpperCamelCase_					: Any            =				block_size
       UpperCamelCase_					: str            =				action_dim
       UpperCamelCase_					: str            =				observation_dim
       UpperCamelCase_					: List[Any]            =				transition_dim
       UpperCamelCase_					: Any            =				learning_rate
       UpperCamelCase_					: Any            =				n_layer
       UpperCamelCase_					: Optional[Any]            =				n_head
       UpperCamelCase_					: Optional[int]            =				n_embd
       UpperCamelCase_					: List[str]            =				embd_pdrop
       UpperCamelCase_					: str            =				attn_pdrop
       UpperCamelCase_					: Optional[int]            =				resid_pdrop
       UpperCamelCase_					: List[str]            =				initializer_range
       UpperCamelCase_					: Tuple            =				layer_norm_eps
       UpperCamelCase_					: Dict            =				kaiming_initializer_range
       UpperCamelCase_					: Optional[Any]            =				use_cache
       super().__init__(pad_token_id=__lowerCAmelCase      ,  bos_token_id=__lowerCAmelCase      ,  eos_token_id=__lowerCAmelCase      ,  **__lowerCAmelCase			)














'''simple docstring'''

from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    DistilBertTokenizer
 __a      :    Union[str, Any]			  =    DistilBertTokenizerFast
 __a      :    Any			  =    True
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""			)

       UpperCamelCase_					: Dict            =				tokenizer.encode("""sequence builders"""      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: int            =				tokenizer.encode("""multi-sequence build"""      ,  add_special_tokens=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase      ,  __lowerCAmelCase			)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






