






'''simple docstring'''







import copy

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


UpperCamelCase     :   List[str]	      =		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(a      )
class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       'rag'
		_UpperCamelCase						=       True






		def __init__(   self ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=" / " ,_lowerCAmelCase=" // " ,_lowerCAmelCase=5 ,_lowerCAmelCase=3_00 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=8 ,_lowerCAmelCase="wiki_dpr" ,_lowerCAmelCase="train" ,_lowerCAmelCase="compressed" ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
								super().__init__(
								    bos_token_id=_lowerCAmelCase ,pad_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,decoder_start_token_id=_lowerCAmelCase ,forced_eos_token_id=_lowerCAmelCase ,is_encoder_decoder=_lowerCAmelCase ,prefix=_lowerCAmelCase ,vocab_size=_lowerCAmelCase ,**_lowerCAmelCase ,)
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								lowerCamelCase__								=	kwargs.pop("""question_encoder"""							)
								lowerCamelCase__								=	question_encoder_config.pop("""model_type"""							)
								lowerCamelCase__								=	kwargs.pop("""generator"""							)
								lowerCamelCase__								=	decoder_config.pop("""model_type"""							)

								from ..auto.configuration_auto import AutoConfig

								lowerCamelCase__								=	AutoConfig.for_model(_lowerCAmelCase ,**_lowerCAmelCase							)
								lowerCamelCase__								=	AutoConfig.for_model(_lowerCAmelCase ,**_lowerCAmelCase							)

								lowerCamelCase__								=	reduce_loss
								lowerCamelCase__								=	label_smoothing
								lowerCamelCase__								=	exclude_bos_score
								lowerCamelCase__								=	do_marginalize

								lowerCamelCase__								=	title_sep
								lowerCamelCase__								=	doc_sep
								lowerCamelCase__								=	n_docs
								lowerCamelCase__								=	max_combined_length

								lowerCamelCase__								=	dataset
								lowerCamelCase__								=	dataset_split
								lowerCamelCase__								=	index_name

								lowerCamelCase__								=	retrieval_vector_size
								lowerCamelCase__								=	retrieval_batch_size
								lowerCamelCase__								=	passages_path
								lowerCamelCase__								=	index_path
								lowerCamelCase__								=	use_dummy_dataset

								lowerCamelCase__								=	output_retrieved

								lowerCamelCase__								=	do_deduplication

								lowerCamelCase__								=	use_cache

								if self.forced_eos_token_id is None:
														lowerCamelCase__								=	getattr(self.generator ,"""forced_eos_token_id""" ,_lowerCAmelCase							)






		@classmethod
		def       UpperCamelCase_       (   cls ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							):
								return cls(question_encoder=question_encoder_config.to_dict() ,generator=generator_config.to_dict() ,**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	copy.deepcopy(self.__dict__							)
								lowerCamelCase__								=	self.question_encoder.to_dict()
								lowerCamelCase__								=	self.generator.to_dict()
								lowerCamelCase__								=	self.__class__.model_type
								return output





'''simple docstring'''


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_UpperCamelCase 							=  logging.get_logger(__name__)


_UpperCamelCase 							=  {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
          raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()							)}."							)

     if tokenizer_name is None:
          __A	:       Tuple      =							TOKENIZER_CLASSES
     else:
          __A	:       Dict      =							{tokenizer_name: getattr(SCREAMING_SNAKE_CASE       ,					tokenizer_name + "Fast"							)}

     logger.info(f"Loading tokenizer classes: {tokenizer_names}"							)

     for tokenizer_name in tokenizer_names:
          __A	:       int      =							TOKENIZER_CLASSES[tokenizer_name]

          __A	:       Union[str, Any]      =							True
          if checkpoint_name is None:
               __A	:       Dict      =							list(tokenizer_class.max_model_input_sizes.keys()							)
          else:
               __A	:       str      =							[checkpoint_name]

          logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"							)

          for checkpoint in checkpoint_names:
               logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}"							)

               # Load tokenizer
               __A	:       List[str]      =							tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE       ,					force_download=SCREAMING_SNAKE_CASE							)

               # Save fast tokenizer
               logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"							)

               # For organization names we create sub-directories
               if "/" in checkpoint:
                    __A  ,__A	:       Optional[Any]      =							checkpoint.split("/"							)
                    __A	:       str      =							os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
               elif add_prefix:
                    __A	:       Optional[int]      =							checkpoint
                    __A	:       Any      =							dump_path
               else:
                    __A	:       int      =							None
                    __A	:       Optional[int]      =							dump_path

               logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"							)

               if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()							)[0]:
                    __A	:       Any      =							list(tokenizer.pretrained_vocab_files_map.values()							)[0][checkpoint]
                    __A	:       Optional[int]      =							file_path.split(SCREAMING_SNAKE_CASE							)[-1][0]
                    if next_char == "/":
                         __A	:       Union[str, Any]      =							os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
                         __A	:       Union[str, Any]      =							None

                    logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"							)

               __A	:       Tuple      =							tokenizer.save_pretrained(
                   SCREAMING_SNAKE_CASE       ,					legacy_format=SCREAMING_SNAKE_CASE       ,					filename_prefix=SCREAMING_SNAKE_CASE							)
               logger.info(f"=> File names {file_names}"							)

               for file_name in file_names:
                    if not file_name.endswith("tokenizer.json"							):
                         os.remove(SCREAMING_SNAKE_CASE							)
                         logger.info(f"=> removing {file_name}"							)


if __name__ == "__main__":
      _UpperCamelCase 							=  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
      )
      parser.add_argument(
          """--tokenizer_name""",
          default=None,
          type=str,
          help=(
              F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
              """download and convert all the checkpoints from AWS."""
          ),
      )
      parser.add_argument(
          """--checkpoint_name""",
          default=None,
          type=str,
          help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
      )
      parser.add_argument(
          """--force_download""",
          action="""store_true""",
          help="""Re-download checkpoints.""",
      )
      _UpperCamelCase 							=  parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)

