





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


A   : Tuple			=				logging.get_logger(__name__)

A   : List[str]			=				{
    """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'''




   __lowerCamelCase     :	Union[str, Any]          =   """trajectory_transformer"""
   __lowerCamelCase     :	List[str]          =   ["""past_key_values"""]
   __lowerCamelCase     :	Optional[Any]          =   {
       """hidden_size""": """n_embd""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }






   def __init__(      self       :   Any							,     __lowerCAmelCase       :   Tuple=1_00							,     __lowerCAmelCase       :   Optional[int]=5							,     __lowerCAmelCase       :   str=1							,     __lowerCAmelCase       :   int=1							,     __lowerCAmelCase       :   Any=2_49							,     __lowerCAmelCase       :   str=6							,     __lowerCAmelCase       :   Tuple=17							,     __lowerCAmelCase       :   Dict=25							,     __lowerCAmelCase       :   Union[str, Any]=4							,     __lowerCAmelCase       :   Dict=4							,     __lowerCAmelCase       :   int=1_28							,     __lowerCAmelCase       :   str=0.1							,     __lowerCAmelCase       :   Union[str, Any]=0.1							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   Union[str, Any]=0.0_0_0_6							,     __lowerCAmelCase       :   Optional[int]=5_12							,     __lowerCAmelCase       :   int=0.0_2							,     __lowerCAmelCase       :   List[Any]=1e-12							,     __lowerCAmelCase       :   Union[str, Any]=1							,     __lowerCAmelCase       :   Optional[int]=True							,     __lowerCAmelCase       :   Optional[int]=1							,     __lowerCAmelCase       :   List[str]=5_02_56							,     __lowerCAmelCase       :   Tuple=5_02_56							,     **__lowerCAmelCase       :   Optional[Any]							,     )     ->     Tuple:







     """simple docstring"""






     A__  				=       vocab_size
     A__  				=       action_weight
     A__  				=       reward_weight
     A__  				=       value_weight
     A__  				=       max_position_embeddings
     A__  				=       block_size
     A__  				=       action_dim
     A__  				=       observation_dim
     A__  				=       transition_dim
     A__  				=       learning_rate
     A__  				=       n_layer
     A__  				=       n_head
     A__  				=       n_embd
     A__  				=       embd_pdrop
     A__  				=       attn_pdrop
     A__  				=       resid_pdrop
     A__  				=       initializer_range
     A__  				=       layer_norm_eps
     A__  				=       kaiming_initializer_range
     A__  				=       use_cache
     super().__init__(pad_token_id=_lowercase							,     bos_token_id=_lowercase							,     eos_token_id=_lowercase							,     **_lowercase       )










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   : Tuple			=				logging.get_logger(__name__)

A   : Optional[int]			=				{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Union[str, Any]          =   '''roberta'''






			def __init__(      self       :   Any							,     __lowerCAmelCase       :   Tuple=5_02_65							,     __lowerCAmelCase       :   Optional[int]=7_68							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[Any]=30_72							,     __lowerCAmelCase       :   Any="gelu"							,     __lowerCAmelCase       :   Optional[Any]=0.1							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   Optional[Any]=5_12							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   List[str]=0.0_2							,     __lowerCAmelCase       :   Dict=1e-12							,     __lowerCAmelCase       :   Optional[int]=1							,     __lowerCAmelCase       :   Tuple=0							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   Dict="absolute"							,     __lowerCAmelCase       :   Optional[Any]=True							,     __lowerCAmelCase       :   Union[str, Any]=None							,     **__lowerCAmelCase       :   str							,     )     ->     str:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,     bos_token_id=__lowerCAmelCase							,     eos_token_id=__lowerCAmelCase							,     **__lowerCAmelCase       )

					A__  				=       vocab_size
					A__  				=       hidden_size
					A__  				=       num_hidden_layers
					A__  				=       num_attention_heads
					A__  				=       hidden_act
					A__  				=       intermediate_size
					A__  				=       hidden_dropout_prob
					A__  				=       attention_probs_dropout_prob
					A__  				=       max_position_embeddings
					A__  				=       type_vocab_size
					A__  				=       initializer_range
					A__  				=       layer_norm_eps
					A__  				=       position_embedding_type
					A__  				=       use_cache
					A__  				=       classifier_dropout


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@property
			def        a_	(      self       :   int       )     ->     Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__  				=       {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__  				=       {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]       )



