

"""simple docstring"""


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


a					:Any    = logging.get_logger(__name__)

a					:str    = {
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """decision_transformer"""
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:str       = {
         """max_position_embeddings""": """n_positions""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }

     def __init__(						self	,    _a=17	,    _a=4	,    _a=128	,    _a=4_096	,    _a=True	,    _a=1	,    _a=1_024	,    _a=3	,    _a=1	,    _a=None	,    _a="relu"	,    _a=0.1	,    _a=0.1	,    _a=0.1	,    _a=1E-5	,    _a=0.02	,    _a=True	,    _a=True	,    _a=50_256	,    _a=50_256	,    _a=False	,    _a=False	,    **_a	,    )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	state_dim
           SCREAMING_SNAKE_CASE__   :      Dict      		=	act_dim
           SCREAMING_SNAKE_CASE__   :      Dict      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	max_ep_len
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	action_tanh
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	n_positions
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	n_layer
           SCREAMING_SNAKE_CASE__   :      str      		=	n_head
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	n_inner
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	activation_function
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	resid_pdrop
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	embd_pdrop
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	attn_pdrop
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	layer_norm_epsilon
           SCREAMING_SNAKE_CASE__   :      str      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	scale_attn_weights
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	use_cache
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scale_attn_by_inverse_layer_idx
           SCREAMING_SNAKE_CASE__   :      int      		=	reorder_and_upcast_attn

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	bos_token_id
           SCREAMING_SNAKE_CASE__   :      Dict      		=	eos_token_id

           super().__init__(bos_token_id=_a	,    eos_token_id=_a	,    **_a  )







"""simple docstring"""


from typing import Mapping

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


a					:Optional[Any]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """t5"""
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:Any       = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(						self	,    _a=32_128	,    _a=512	,    _a=64	,    _a=2_048	,    _a=6	,    _a=None	,    _a=8	,    _a=32	,    _a=128	,    _a=0.1	,    _a=1E-6	,    _a=1.0	,    _a="relu"	,    _a=True	,    _a=True	,    _a=0	,    _a=1	,    **_a	,    )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	d_model
           SCREAMING_SNAKE_CASE__   :      int      		=	d_kv
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d_ff
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_layers
           SCREAMING_SNAKE_CASE__   :      int      		=	(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	relative_attention_num_buckets
           SCREAMING_SNAKE_CASE__   :      str      		=	relative_attention_max_distance
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dropout_rate
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	layer_norm_epsilon
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	initializer_factor
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	feed_forward_proj
           SCREAMING_SNAKE_CASE__   :      str      		=	use_cache

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.feed_forward_proj.split("""-"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	act_info[-1]
           SCREAMING_SNAKE_CASE__   :      str      		=	act_info[0] == """gated"""

           if len(_a  ) > 1 and act_info[0] != "gated" or len(_a  ) > 2:
                 raise ValueError(
                     f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                     """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                     """'gated-gelu' or 'relu'"""  )

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""gelu_new"""

           super().__init__(
               pad_token_id=_a	,    eos_token_id=_a	,    is_encoder_decoder=_a	,    **_a	,    )





class      __a							(UpperCamelCase_):






     '''simple docstring'''

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""past_encoder_sequence + sequence"""
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{0: """batch"""}
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE__   :      str      		=	{0: """batch""", 1: """decoder_sequence"""}
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                 self.fill_with_past_key_values_(_a	,    direction="""inputs"""  )

           return common_inputs





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 13




