


'''simple docstring'''

import copy

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


lowerCamelCase							:   Tuple  						=	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(lowercase_     )
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Any  					=							"""rag"""
      lowerCAmelCase__					:     List[Any]  					=							True



      def __init__(self    :    Dict   ,						UpperCamelCase    :    List[Any]=None   ,						UpperCamelCase    :    str=True   ,						UpperCamelCase    :    List[Any]=None   ,						UpperCamelCase    :    List[str]=None   ,						UpperCamelCase    :    List[Any]=None   ,						UpperCamelCase    :    str=None   ,						UpperCamelCase    :    List[Any]=None   ,						UpperCamelCase    :    str=" / "   ,						UpperCamelCase    :    Union[str, Any]=" // "   ,						UpperCamelCase    :    List[str]=5   ,						UpperCamelCase    :    Tuple=300   ,						UpperCamelCase    :    Optional[int]=768   ,						UpperCamelCase    :    int=8   ,						UpperCamelCase    :    str="wiki_dpr"   ,						UpperCamelCase    :    Optional[Any]="train"   ,						UpperCamelCase    :    Any="compressed"   ,						UpperCamelCase    :    Dict=None   ,						UpperCamelCase    :    List[Any]=None   ,						UpperCamelCase    :    List[Any]=False   ,						UpperCamelCase    :    str=False   ,						UpperCamelCase    :    Optional[int]=0.0   ,						UpperCamelCase    :    str=True   ,						UpperCamelCase    :    int=False   ,						UpperCamelCase    :    Any=False   ,						UpperCamelCase    :    Any=False   ,						UpperCamelCase    :    List[str]=True   ,						UpperCamelCase    :    Optional[int]=None   ,						**UpperCamelCase    :    List[Any]   ,						):

             '''simple docstring'''







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

             from ..auto.configuration_auto import AutoConfig

             lowercase__      			= AutoConfig.for_model(UpperCamelCase   ,						**UpperCamelCase )
             lowercase__      			= AutoConfig.for_model(UpperCamelCase   ,						**UpperCamelCase )

             lowercase__      			= reduce_loss
             lowercase__      			= label_smoothing
             lowercase__      			= exclude_bos_score
             lowercase__      			= do_marginalize

             lowercase__      			= title_sep
             lowercase__      			= doc_sep
             lowercase__      			= n_docs
             lowercase__      			= max_combined_length

             lowercase__      			= dataset
             lowercase__      			= dataset_split
             lowercase__      			= index_name

             lowercase__      			= retrieval_vector_size
             lowercase__      			= retrieval_batch_size
             lowercase__      			= passages_path
             lowercase__      			= index_path
             lowercase__      			= use_dummy_dataset

             lowercase__      			= output_retrieved

             lowercase__      			= do_deduplication

             lowercase__      			= use_cache

             if self.forced_eos_token_id is None:
                    lowercase__      			= getattr(self.generator   ,						'''forced_eos_token_id'''   ,						UpperCamelCase )



      @classmethod
      def       UpperCamelCase__		(cls    :    Optional[int]   ,						UpperCamelCase    :    PretrainedConfig   ,						UpperCamelCase    :    PretrainedConfig   ,						**UpperCamelCase    :    int ):

             '''simple docstring'''







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






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







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








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config',
     default='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




