



import copy

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


__UpperCAmelCase          =       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(a__							)
class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					int        =  "rag"
       UpperCAmelCase__				:					int        =  True
       def __init__(			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=" / ",			SCREAMING_SNAKE_CASE_=" // ",			SCREAMING_SNAKE_CASE_=5,			SCREAMING_SNAKE_CASE_=300,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_="wiki_dpr",			SCREAMING_SNAKE_CASE_="train",			SCREAMING_SNAKE_CASE_="compressed",			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          super().__init__(
              bos_token_id=SCREAMING_SNAKE_CASE_,			pad_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			decoder_start_token_id=SCREAMING_SNAKE_CASE_,			forced_eos_token_id=SCREAMING_SNAKE_CASE_,			is_encoder_decoder=SCREAMING_SNAKE_CASE_,			prefix=SCREAMING_SNAKE_CASE_,			vocab_size=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          assert (
              "question_encoder" in kwargs and "generator" in kwargs
          ), "Config has to be initialized with question_encoder and generator config"
          UpperCamelCase    :  Union[str, Any]							=	kwargs.pop('question_encoder'       )
          UpperCamelCase    :  List[Any]							=	question_encoder_config.pop('model_type'       )
          UpperCamelCase    :  Optional[Any]							=	kwargs.pop('generator'       )
          UpperCamelCase    :  Union[str, Any]							=	decoder_config.pop('model_type'       )

          from ..auto.configuration_auto import AutoConfig

          UpperCamelCase    :  int							=	AutoConfig.for_model(SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	AutoConfig.for_model(SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

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

          UpperCamelCase    :  int							=	title_sep
          UpperCamelCase    :  Dict							=	doc_sep
          UpperCamelCase    :  Optional[Any]							=	n_docs
          UpperCamelCase    :  List[Any]							=	max_combined_length

          UpperCamelCase    :  Optional[int]							=	dataset
          UpperCamelCase    :  Tuple							=	dataset_split
          UpperCamelCase    :  int							=	index_name

          UpperCamelCase    :  int							=	retrieval_vector_size
          UpperCamelCase    :  Dict							=	retrieval_batch_size
          UpperCamelCase    :  Optional[int]							=	passages_path
          UpperCamelCase    :  str							=	index_path
          UpperCamelCase    :  List[str]							=	use_dummy_dataset

          UpperCamelCase    :  Union[str, Any]							=	output_retrieved

          UpperCamelCase    :  Union[str, Any]							=	do_deduplication

          UpperCamelCase    :  str							=	use_cache

          if self.forced_eos_token_id is None:
             UpperCamelCase    :  Union[str, Any]							=	getattr(self.generator,			'forced_eos_token_id',			SCREAMING_SNAKE_CASE_       )
       @classmethod
       def 					snake_case_       (			cls,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		PretrainedConfig:
          return cls(question_encoder=question_encoder_config.to_dict(),			generator=generator_config.to_dict(),			**SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  int							=	copy.deepcopy(self.__dict__       )
          UpperCamelCase    :  Dict							=	self.question_encoder.to_dict()
          UpperCamelCase    :  Optional[Any]							=	self.generator.to_dict()
          UpperCamelCase    :  Union[str, Any]							=	self.__class__.model_type
          return output








import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



