






"""simple docstring"""






import copy

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


SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       R'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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





@add_start_docstrings(_lowerCamelCase       )
class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""rag"""
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					True


	def __init__(      self  ,					__a=None  ,					__a=True  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a=" / "  ,					__a=" // "  ,					__a=5  ,					__a=300  ,					__a=768  ,					__a=8  ,					__a="wiki_dpr"  ,					__a="train"  ,					__a="compressed"  ,					__a=None  ,					__a=None  ,					__a=False  ,					__a=False  ,					__a=0.0  ,					__a=True  ,					__a=False  ,					__a=False  ,					__a=False  ,					__a=True  ,					__a=None  ,					**__a  ,					):


					"""simple docstring"""

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

					from ..auto.configuration_auto import AutoConfig

					A__							 =       AutoConfig.for_model(__a  ,					**__a						)
					A__							 =       AutoConfig.for_model(__a  ,					**__a						)

					A__							 =       reduce_loss
					A__							 =       label_smoothing
					A__							 =       exclude_bos_score
					A__							 =       do_marginalize

					A__							 =       title_sep
					A__							 =       doc_sep
					A__							 =       n_docs
					A__							 =       max_combined_length

					A__							 =       dataset
					A__							 =       dataset_split
					A__							 =       index_name

					A__							 =       retrieval_vector_size
					A__							 =       retrieval_batch_size
					A__							 =       passages_path
					A__							 =       index_path
					A__							 =       use_dummy_dataset

					A__							 =       output_retrieved

					A__							 =       do_deduplication

					A__							 =       use_cache

					if self.forced_eos_token_id is None:
									A__							 =       getattr(self.generator  ,					'forced_eos_token_id'  ,					__a						)


	@classmethod
	def       _UpperCAmelCase						(      cls  ,					__a  ,					__a  ,					**__a						):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       copy.deepcopy(self.__dict__						)
					A__							 =       self.question_encoder.to_dict()
					A__							 =       self.generator.to_dict()
					A__							 =       self.__class__.model_type
					return output










"""simple docstring"""






from typing import Union

import fire
import torch
from tqdm import tqdm



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = "cpu"       ,lowerCAmelCase__ = None				):
				A__							 =       torch.load(lowerCAmelCase__       ,map_location=lowerCAmelCase__				)
				for k, v in tqdm(state_dict.items()				):
								if not isinstance(lowerCAmelCase__       ,torch.Tensor				):
												raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'				)
								A__							 =       v.half()
				if save_path is None:  # overwrite src_path
								A__							 =       src_path
				torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)


if __name__ == "__main__":
					fire.Fire(convert)


