





'''simple docstring'''




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowercase__       : Union[str, Any]         =       logging.get_logger(__name__)

lowercase__       : Union[str, Any]         =       {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__       : Optional[Any]         =       {
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
lowercase__       : int         =       {
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
lowercase__       : Tuple         =       {
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

lowercase__       : Any         =       {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
lowercase__       : int         =       {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
lowercase__       : Tuple         =       {
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


lowercase__       : Union[str, Any]         =       {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowercase__       : Any         =       {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowercase__       : Optional[Any]         =       {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
     lowerCAmelCase						     =			DPRContextEncoderTokenizer




class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
     lowerCAmelCase						     =			DPRQuestionEncoderTokenizer




lowercase__       : Union[str, Any]         =       collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowercase__       : List[Any]         =       collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowercase__       : Tuple         =       r'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''


@add_start_docstrings(a__				)
class 					SCREAMING_SNAKE_CASE       :


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = False		,      _UpperCAmelCase = False		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          if titles is None and texts is None:
               return super().__call__(
                   _UpperCAmelCase		,      padding=_UpperCAmelCase		,      truncation=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      return_tensors=_UpperCAmelCase		,      return_attention_mask=_UpperCAmelCase		,      **_UpperCAmelCase		,      )
          elif titles is None or texts is None:
               __A		:					List[str]						=			titles if texts is None else texts
               return super().__call__(
                   _UpperCAmelCase		,      _UpperCAmelCase		,      padding=_UpperCAmelCase		,      truncation=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      return_tensors=_UpperCAmelCase		,      return_attention_mask=_UpperCAmelCase		,      **_UpperCAmelCase		,      )
          __A		:					Optional[int]						=			titles if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else [titles]
          __A		:					Optional[int]						=			texts if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else [texts]
          __A		:					Union[str, Any]						=			len(_UpperCAmelCase)
          __A		:					Any						=			questions if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else [questions] * n_passages
          assert len(_UpperCAmelCase) == len(
              _UpperCAmelCase), F'There should be as many titles than texts but got {len(_UpperCAmelCase)} titles and {len(_UpperCAmelCase)} texts.'
          __A		:					Union[str, Any]						=			super().__call__(_UpperCAmelCase		,      _UpperCAmelCase		,      padding=_UpperCAmelCase		,      truncation=_UpperCAmelCase)['input_ids']
          __A		:					Optional[int]						=			super().__call__(_UpperCAmelCase		,      add_special_tokens=_UpperCAmelCase		,      padding=_UpperCAmelCase		,      truncation=_UpperCAmelCase)['input_ids']
          __A		:					List[str]						=			{
              'input_ids': [
                  (encoded_question_and_title + encoded_text)[:max_length]
                  if max_length is not None and truncation
                  else encoded_question_and_title + encoded_text
                  for encoded_question_and_title, encoded_text in zip(_UpperCAmelCase		,      _UpperCAmelCase)
              ]
          }
          if return_attention_mask is not False:
               __A		:					Any						=			[]
               for input_ids in encoded_inputs["input_ids"]:
                    attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
               __A		:					List[Any]						=			attention_mask
          return self.pad(_UpperCAmelCase		,      padding=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      return_tensors=_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = 16		,      _UpperCAmelCase = 64		,      _UpperCAmelCase = 4		,      ):

          '''simple docstring'''






          __A		:					int						=			reader_input['input_ids']
          __A					,__A					,__A		:					Optional[Any]						=			reader_output[:3]
          __A		:					str						=			len(_UpperCAmelCase)
          __A		:					Any						=			sorted(range(_UpperCAmelCase)		,      reverse=_UpperCAmelCase		,      key=relevance_logits.__getitem__)
          __A		:					List[DPRReaderOutput]						=			[]
          for doc_id in sorted_docs:
               __A		:					Union[str, Any]						=			list(input_ids[doc_id])
               # assuming question & title information is at the beginning of the sequence
               __A		:					Tuple						=			sequence_ids.index(self.sep_token_id		,      2) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                    __A		:					int						=			sequence_ids.index(self.pad_token_id)
               else:
                    __A		:					Optional[int]						=			len(_UpperCAmelCase)

               __A		:					Union[str, Any]						=			self._get_best_spans(
                   start_logits=start_logits[doc_id][passage_offset:sequence_len]		,      end_logits=end_logits[doc_id][passage_offset:sequence_len]		,      max_answer_length=_UpperCAmelCase		,      top_spans=_UpperCAmelCase		,      )
               for start_index, end_index in best_spans:
                    start_index += passage_offset
                    end_index += passage_offset
                    nbest_spans_predictions.append(
                        DPRSpanPrediction(
                            span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]		,      relevance_score=relevance_logits[doc_id]		,      doc_id=_UpperCAmelCase		,      start_index=_UpperCAmelCase		,      end_index=_UpperCAmelCase		,      text=self.decode(sequence_ids[start_index : end_index + 1])		,      ))
               if len(_UpperCAmelCase) >= num_spans:
                    break
          return nbest_spans_predictions[:num_spans]


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					List[str]						=			[]
          for start_index, start_score in enumerate(_UpperCAmelCase):
               for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
                    scores.append(((start_index, start_index + answer_length), start_score + end_score))
          __A		:					Any						=			sorted(_UpperCAmelCase		,      key=lambda _UpperCAmelCase: x[1]		,      reverse=_UpperCAmelCase)
          __A		:					List[str]						=			[]
          for (start_index, end_index), score in scores:
               assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
               __A		:					int						=			end_index - start_index + 1
               assert length <= max_answer_length, F'Span is too long: {length} > {max_answer_length}'
               if any(
                   start_index <= prev_start_index <= prev_end_index <= end_index
                   or prev_start_index <= start_index <= end_index <= prev_end_index
                   for (prev_start_index, prev_end_index) in chosen_span_intervals):
                    continue
               chosen_span_intervals.append((start_index, end_index))

               if len(_UpperCAmelCase) == top_spans:
                    break
          return chosen_span_intervals




@add_end_docstrings(a__				)
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			READER_PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			READER_PRETRAINED_INIT_CONFIGURATION
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']
     lowerCAmelCase						     =			DPRReaderTokenizer






'''simple docstring'''




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)