





"""simple docstring"""




import copy

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


__A     =    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(snake_case__   )
class        _SCREAMING_SNAKE_CASE						(     snake_case__   ):







 '''simple docstring'''
 lowercase_		=      "rag"
 lowercase_		=      True

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



   '''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"
   lowerCamelCase__:				Any        =kwargs.pop("question_encoder")
   lowerCamelCase__:				Optional[Any]        =question_encoder_config.pop("model_type")
   lowerCamelCase__:				Optional[Any]        =kwargs.pop("generator")
   lowerCamelCase__:				str        =decoder_config.pop("model_type")

   from ..auto.configuration_auto import AutoConfig

   lowerCamelCase__:				Union[str, Any]        =AutoConfig.for_model(UpperCAmelCase_	,			**UpperCAmelCase_)
   lowerCamelCase__:				Optional[Any]        =AutoConfig.for_model(UpperCAmelCase_	,			**UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        =reduce_loss
   lowerCamelCase__:				Tuple        =label_smoothing
   lowerCamelCase__:				Dict        =exclude_bos_score
   lowerCamelCase__:				int        =do_marginalize

   lowerCamelCase__:				Any        =title_sep
   lowerCamelCase__:				Optional[Any]        =doc_sep
   lowerCamelCase__:				str        =n_docs
   lowerCamelCase__:				Any        =max_combined_length

   lowerCamelCase__:				Union[str, Any]        =dataset
   lowerCamelCase__:				Any        =dataset_split
   lowerCamelCase__:				Optional[int]        =index_name

   lowerCamelCase__:				str        =retrieval_vector_size
   lowerCamelCase__:				int        =retrieval_batch_size
   lowerCamelCase__:				List[Any]        =passages_path
   lowerCamelCase__:				List[str]        =index_path
   lowerCamelCase__:				Optional[int]        =use_dummy_dataset

   lowerCamelCase__:				Optional[int]        =output_retrieved

   lowerCamelCase__:				Dict        =do_deduplication

   lowerCamelCase__:				List[str]        =use_cache

   if self.forced_eos_token_id is None:
     lowerCamelCase__:				Union[str, Any]        =getattr(self.generator	,			"forced_eos_token_id"	,			UpperCAmelCase_)

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Any	,			UpperCAmelCase_    :       PretrainedConfig	,			UpperCAmelCase_    :       PretrainedConfig	,			**UpperCAmelCase_    :       Any)	->Tuple:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				str        =copy.deepcopy(self.__dict__)
   lowerCamelCase__:				List[str]        =self.question_encoder.to_dict()
   lowerCamelCase__:				Tuple        =self.generator.to_dict()
   lowerCamelCase__:				Tuple        =self.__class__.model_type
   return output








import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A     =    logging.getLogger(__name__)

if __name__ == "__main__":
     __A     =    argparse.ArgumentParser(
         description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
     )
     parser.add_argument(
         "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
     )
     parser.add_argument(
         "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
     )
     parser.add_argument("--vocab_size", default=3_0522, type=int)
     __A     =    parser.parse_args()

     logger.info(f'Loading data from {args.data_file}')
     with open(args.data_file, "rb") as fp:
          __A     =    pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     __A     =    Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     __A     =    [0] * args.vocab_size
     for k, v in counter.items():
          __A     =    v

     logger.info(f'Dump to {args.token_counts_dump}')
     with open(args.token_counts_dump, "wb") as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)

