





'''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 import BertTokenizer


A__							:  Optional[Any]    =logging.get_logger(__name__)

A__							:  Optional[Any]    ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__							:  Dict    ={
    '''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'''
        ),
    },
}
A__							:  Tuple    ={
    '''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'''
        ),
    },
}
A__							:  Union[str, Any]    ={
    '''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'''
        ),
    },
}

A__							:  Union[str, Any]    ={
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
A__							:  Union[str, Any]    ={
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
A__							:  int    ={
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


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




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     str			=  VOCAB_FILES_NAMES
      _lowercase:     List[Any]			=  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase:     Any			=  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:     str			=  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  VOCAB_FILES_NAMES
      _lowercase:     List[str]			=  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase:     Any			=  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:     int			=  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




A__							:  Dict    =collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


A__							:  str    =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)

    Returns:
        `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(snake_case_  )
class 					UpperCAmelCase	:




      def __call__(     self     :					List[str]     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					Optional[str] = None     ,     __snake_case     :					Optional[str] = None     ,     __snake_case     :					Union[bool, str] = False     ,     __snake_case     :					Union[bool, str] = False     ,     __snake_case     :					Optional[int] = None     ,     __snake_case     :					Optional[Union[str, TensorType]] = None     ,     __snake_case     :					Optional[bool] = None     ,     **__snake_case     :					Optional[int]     ,     )							-> BatchEncoding:
            if titles is None and texts is None:
                  return super().__call__(
                      __snake_case     ,     padding=__snake_case     ,     truncation=__snake_case     ,     max_length=__snake_case     ,     return_tensors=__snake_case     ,     return_attention_mask=__snake_case     ,     **__snake_case     ,     )
            elif titles is None or texts is None:
                  _lowerCAmelCase					=				titles if texts is None else texts
                  return super().__call__(
                      __snake_case     ,     __snake_case     ,     padding=__snake_case     ,     truncation=__snake_case     ,     max_length=__snake_case     ,     return_tensors=__snake_case     ,     return_attention_mask=__snake_case     ,     **__snake_case     ,     )
            _lowerCAmelCase					=				titles if not isinstance(__snake_case     ,     __snake_case    ) else [titles]
            _lowerCAmelCase					=				texts if not isinstance(__snake_case     ,     __snake_case    ) else [texts]
            _lowerCAmelCase					=				len(__snake_case    )
            _lowerCAmelCase					=				questions if not isinstance(__snake_case     ,     __snake_case    ) else [questions] * n_passages
            if len(__snake_case    ) != len(__snake_case    ):
                  raise ValueError(
                      f"There should be as many titles than texts but got {len(__snake_case    )} titles and {len(__snake_case    )} texts."    )
            _lowerCAmelCase					=				super().__call__(__snake_case     ,     __snake_case     ,     padding=__snake_case     ,     truncation=__snake_case    )["""input_ids"""]
            _lowerCAmelCase					=				super().__call__(__snake_case     ,     add_special_tokens=__snake_case     ,     padding=__snake_case     ,     truncation=__snake_case    )["""input_ids"""]
            _lowerCAmelCase					=				{
                """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(__snake_case     ,     __snake_case    )
                ]
            }
            if return_attention_mask is not False:
                  _lowerCAmelCase					=				[]
                  for input_ids in encoded_inputs["input_ids"]:
                        attention_mask.append([int(input_id != self.pad_token_id    ) for input_id in input_ids]    )
                  _lowerCAmelCase					=				attention_mask
            return self.pad(__snake_case     ,     padding=__snake_case     ,     max_length=__snake_case     ,     return_tensors=__snake_case    )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					BatchEncoding     ,     __snake_case     :					DPRReaderOutput     ,     __snake_case     :					int = 16     ,     __snake_case     :					int = 64     ,     __snake_case     :					int = 4     ,     )							-> List[DPRSpanPrediction]:
            _lowerCAmelCase					=				reader_input["""input_ids"""]
            _lowerCAmelCase	,				_lowerCAmelCase	,				_lowerCAmelCase					=				reader_output[:3]
            _lowerCAmelCase					=				len(__snake_case    )
            _lowerCAmelCase					=				sorted(range(__snake_case    )     ,     reverse=__snake_case     ,     key=relevance_logits.__getitem__    )
            _lowerCAmelCase					=				[]
            for doc_id in sorted_docs:
                  _lowerCAmelCase					=				list(input_ids[doc_id]    )
                  # assuming question & title information is at the beginning of the sequence
                  _lowerCAmelCase					=				sequence_ids.index(self.sep_token_id     ,     2    ) + 1  # second sep id
                  if sequence_ids[-1] == self.pad_token_id:
                        _lowerCAmelCase					=				sequence_ids.index(self.pad_token_id    )
                  else:
                        _lowerCAmelCase					=				len(__snake_case    )

                  _lowerCAmelCase					=				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=__snake_case     ,     top_spans=__snake_case     ,     )
                  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=__snake_case     ,     start_index=__snake_case     ,     end_index=__snake_case     ,     text=self.decode(sequence_ids[start_index : end_index + 1]    )     ,     )    )
                  if len(__snake_case    ) >= num_spans:
                        break
            return nbest_spans_predictions[:num_spans]




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					List[int]     ,     __snake_case     :					List[int]     ,     __snake_case     :					int     ,     __snake_case     :					int     ,     )							-> List[DPRSpanPrediction]:
            _lowerCAmelCase					=				[]
            for start_index, start_score in enumerate(__snake_case    ):
                  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)    )
            _lowerCAmelCase					=				sorted(__snake_case     ,     key=lambda __snake_case    : x[1]     ,     reverse=__snake_case    )
            _lowerCAmelCase					=				[]
            for (start_index, end_index), score in scores:
                  if start_index > end_index:
                        raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"    )
                  _lowerCAmelCase					=				end_index - start_index + 1
                  if length > max_answer_length:
                        raise ValueError(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(__snake_case    ) == top_spans:
                        break
            return chosen_span_intervals




@add_end_docstrings(snake_case_  )
class 					UpperCAmelCase	(     snake_case_				,						snake_case_  ):
      _lowercase:     int			=  VOCAB_FILES_NAMES
      _lowercase:     Union[str, Any]			=  READER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase:     Optional[int]			=  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:     Any			=  READER_PRETRAINED_INIT_CONFIGURATION
      _lowercase:     List[str]			=  ['''input_ids''', '''attention_mask''']













'''simple docstring'''






import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
A__							:  List[str]    =logging.getLogger(__name__)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				argparse.ArgumentParser(
          description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""    )
      parser.add_argument("""--file_path"""				,      type=lowerCAmelCase				,      default="""data/dump.txt"""				,      help="""The path to the data."""    )
      parser.add_argument("""--tokenizer_type"""				,      type=lowerCAmelCase				,      default="""bert"""				,      choices=["""bert""", """roberta""", """gpt2"""]    )
      parser.add_argument("""--tokenizer_name"""				,      type=lowerCAmelCase				,      default="""bert-base-uncased"""				,      help="""The tokenizer to use."""    )
      parser.add_argument("""--dump_file"""				,      type=lowerCAmelCase				,      default="""data/dump"""				,      help="""The dump file prefix."""    )
      _lowerCAmelCase					=				parser.parse_args()

      logger.info(f"Loading Tokenizer ({args.tokenizer_name})"    )
      if args.tokenizer_type == "bert":
            _lowerCAmelCase					=				BertTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            _lowerCAmelCase					=				RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            _lowerCAmelCase					=				GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

      logger.info(f"Loading text from {args.file_path}"    )
      with open(args.file_path				,      """r"""				,      encoding="""utf8"""    ) as fp:
            _lowerCAmelCase					=				fp.readlines()

      logger.info("""Start encoding"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples to process."    )

      _lowerCAmelCase					=				[]
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				1_00_00
      _lowerCAmelCase					=				time.time()
      for text in data:
            _lowerCAmelCase					=				f"{bos} {text.strip()} {sep}"
            _lowerCAmelCase					=				tokenizer.encode(lowerCAmelCase				,      add_special_tokens=lowerCAmelCase    )
            rslt.append(lowerCAmelCase    )

            iter += 1
            if iter % interval == 0:
                  _lowerCAmelCase					=				time.time()
                  logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"    )
                  _lowerCAmelCase					=				time.time()
      logger.info("""Finished binarization"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples processed."    )

      _lowerCAmelCase					=				f"{args.dump_file}.{args.tokenizer_name}.pickle"
      _lowerCAmelCase					=				tokenizer.vocab_size
      if vocab_size < (1 << 16):
            _lowerCAmelCase					=				[np.uintaa(lowerCAmelCase    ) for d in rslt]
      else:
            _lowerCAmelCase					=				[np.intaa(lowerCAmelCase    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(f"Dump to {dp_file}"    )
      with open(lowerCAmelCase				,      """wb"""    ) as handle:
            pickle.dump(rslt_				,      lowerCAmelCase				,      protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
      main()






