"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowercase__					       =	r'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'




@add_start_docstrings(__lowerCAmelCase				)
class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""rag"""
      a__        =		True



      def __init__(						self    ,			lowercase=None    ,			lowercase=True    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=" / "    ,			lowercase=" // "    ,			lowercase=5    ,			lowercase=3_00    ,			lowercase=7_68    ,			lowercase=8    ,			lowercase="wiki_dpr"    ,			lowercase="train"    ,			lowercase="compressed"    ,			lowercase=None    ,			lowercase=None    ,			lowercase=False    ,			lowercase=False    ,			lowercase=0.0    ,			lowercase=True    ,			lowercase=False    ,			lowercase=False    ,			lowercase=False    ,			lowercase=True    ,			lowercase=None    ,			**lowercase    ,			)	->			str:





        '''simple docstring'''





        super().__init__(
            bos_token_id=lowercase    ,			pad_token_id=lowercase    ,			eos_token_id=lowercase    ,			decoder_start_token_id=lowercase    ,			forced_eos_token_id=lowercase    ,			is_encoder_decoder=lowercase    ,			prefix=lowercase    ,			vocab_size=lowercase    ,			**lowercase    ,			)
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        a__:     int										=	kwargs.pop('question_encoder')
        a__:     int										=	question_encoder_config.pop('model_type')
        a__:     str										=	kwargs.pop('generator')
        a__:     Dict										=	decoder_config.pop('model_type')

        from ..auto.configuration_auto import AutoConfig

        a__:     Union[str, Any]										=	AutoConfig.for_model(lowercase    ,			**lowercase)
        a__:     List[Any]										=	AutoConfig.for_model(lowercase    ,			**lowercase)

        a__:     int										=	reduce_loss
        a__:     Optional[int]										=	label_smoothing
        a__:     List[Any]										=	exclude_bos_score
        a__:     Union[str, Any]										=	do_marginalize

        a__:     Dict										=	title_sep
        a__:     Dict										=	doc_sep
        a__:     Union[str, Any]										=	n_docs
        a__:     Optional[Any]										=	max_combined_length

        a__:     Dict										=	dataset
        a__:     List[str]										=	dataset_split
        a__:     Optional[Any]										=	index_name

        a__:     Tuple										=	retrieval_vector_size
        a__:     Any										=	retrieval_batch_size
        a__:     Union[str, Any]										=	passages_path
        a__:     int										=	index_path
        a__:     int										=	use_dummy_dataset

        a__:     Dict										=	output_retrieved

        a__:     Tuple										=	do_deduplication

        a__:     List[str]										=	use_cache

        if self.forced_eos_token_id is None:
          a__:     List[str]										=	getattr(self.generator    ,			'forced_eos_token_id'    ,			lowercase)



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase    ,			**lowercase)	->			PretrainedConfig:





        '''simple docstring'''





        return cls(question_encoder=question_encoder_config.to_dict()    ,			generator=generator_config.to_dict()    ,			**lowercase)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	copy.deepcopy(self.__dict__)
        a__:     Optional[int]										=	self.question_encoder.to_dict()
        a__:     Optional[Any]										=	self.generator.to_dict()
        a__:     Optional[int]										=	self.__class__.model_type
        return output



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    '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    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""decision_transformer"""
      a__        =		["""past_key_values"""]
      a__        =		{
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }



      def __init__(						self    ,			lowercase=17    ,			lowercase=4    ,			lowercase=1_28    ,			lowercase=40_96    ,			lowercase=True    ,			lowercase=1    ,			lowercase=10_24    ,			lowercase=3    ,			lowercase=1    ,			lowercase=None    ,			lowercase="relu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=1e-5    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=5_02_56    ,			lowercase=5_02_56    ,			lowercase=False    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





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

        a__:     Any										=	bos_token_id
        a__:     int										=	eos_token_id

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


