





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


UpperCAmelCase	:	int		     =       logging.get_logger(__name__)

UpperCAmelCase	:	str		     =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase	:	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"""
        ),
    },
}
UpperCAmelCase	:	Optional[Any]		     =       {
    """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"""
        ),
    },
}
UpperCAmelCase	:	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"""
        ),
    },
}

UpperCAmelCase	:	str		     =       {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
UpperCAmelCase	:	Union[str, Any]		     =       {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
UpperCAmelCase	:	str		     =       {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


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







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Dict						=			VOCAB_FILES_NAMES
      _lowercase				:					List[Any]						=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					Optional[Any]						=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					str						=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      _lowercase				:					Dict						=			DPRContextEncoderTokenizer







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					List[str]						=			VOCAB_FILES_NAMES
      _lowercase				:					Any						=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					int						=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					Dict						=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      _lowercase				:					str						=			DPRQuestionEncoderTokenizer
UpperCAmelCase	:	List[str]		     =       collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

UpperCAmelCase	:	List[str]		     =       collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


UpperCAmelCase	:	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(UpperCamelCase__)
class 				__lowerCAmelCase       :







      def __call__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						BatchEncoding:



        '''simple docstring'''




        if titles is None and texts is None:
          return super().__call__(
              lowerCAmelCase__		,      padding=lowerCAmelCase__		,      truncation=lowerCAmelCase__		,      max_length=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      return_attention_mask=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        elif titles is None or texts is None:
          a__	:					Optional[Any]										=titles if texts is None else texts
          return super().__call__(
              lowerCAmelCase__		,      lowerCAmelCase__		,      padding=lowerCAmelCase__		,      truncation=lowerCAmelCase__		,      max_length=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      return_attention_mask=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        a__	:					Union[str, Any]										=titles if not isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else [titles]
        a__	:					List[str]										=texts if not isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else [texts]
        a__	:					int										=len(lowerCAmelCase__							)
        a__	:					str										=questions if not isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else [questions] * n_passages
        assert len(lowerCAmelCase__							) == len(
            lowerCAmelCase__							), F'''There should be as many titles than texts but got {len(lowerCAmelCase__							)} titles and {len(lowerCAmelCase__							)} texts.'''
        a__	:					int										=super().__call__(lowerCAmelCase__		,      lowerCAmelCase__		,      padding=lowerCAmelCase__		,      truncation=lowerCAmelCase__							)["input_ids"]
        a__	:					str										=super().__call__(lowerCAmelCase__		,      add_special_tokens=lowerCAmelCase__		,      padding=lowerCAmelCase__		,      truncation=lowerCAmelCase__							)["input_ids"]
        a__	:					Optional[Any]										={
            "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(lowerCAmelCase__		,      lowerCAmelCase__							)
            ]
        }
        if return_attention_mask is not False:
          a__	:					List[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__	:					Tuple										=attention_mask
        return self.pad(lowerCAmelCase__		,      padding=lowerCAmelCase__		,      max_length=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__ = 1_6		,      lowerCAmelCase__ = 6_4		,      lowerCAmelCase__ = 4		,      )   ->						List[DPRSpanPrediction]:



        '''simple docstring'''




        a__	:					Optional[Any]										=reader_input["input_ids"]
        a__					,							a__					,							a__	:					Union[str, Any]										=reader_output[:3]
        a__	:					Optional[int]										=len(lowerCAmelCase__							)
        a__	:					Optional[Any]										=sorted(range(lowerCAmelCase__							)		,      reverse=lowerCAmelCase__		,      key=relevance_logits.__getitem__							)
        a__	:					List[DPRReaderOutput]										=[]
        for doc_id in sorted_docs:
          a__	:					List[str]										=list(input_ids[doc_id]							)
          # assuming question & title information is at the beginning of the sequence
          a__	:					Union[str, Any]										=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[Any]										=len(lowerCAmelCase__							)

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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      )   ->						List[DPRSpanPrediction]:



        '''simple docstring'''




        a__	:					Optional[int]										=[]
        for start_index, start_score in enumerate(lowerCAmelCase__							):
          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__	:					str										=sorted(lowerCAmelCase__		,      key=lambda lowerCAmelCase__							: x[1]		,      reverse=lowerCAmelCase__							)
        a__	:					Any										=[]
        for (start_index, end_index), score in scores:
          assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
          a__	:					List[Any]										=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(lowerCAmelCase__							) == top_spans:
            break
        return chosen_span_intervals







@add_end_docstrings(UpperCamelCase__)
class 				__lowerCAmelCase       (    UpperCamelCase__						,				UpperCamelCase__):
      _lowercase				:					List[Any]						=			VOCAB_FILES_NAMES
      _lowercase				:					List[str]						=			READER_PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					str						=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					List[str]						=			READER_PRETRAINED_INIT_CONFIGURATION
      _lowercase				:					str						=			["""input_ids""", """attention_mask"""]
      _lowercase				:					List[str]						=			DPRReaderTokenizer









'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def        _lowerCAmelCase					(						)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =ArgumentParser(
         description=(
             'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
         )				)

     # Optional arguments for the launch helper
     parser.add_argument('--num_cores'							, type=_UpperCamelCase							, default=1							, help='Number of TPU cores to use (1 or 8).'				)

     # positional
     parser.add_argument(
         'training_script'							, type=_UpperCamelCase							, help=(
             'The full path to the single TPU training '
             'program/script to be launched in parallel, '
             'followed by all the arguments for the '
             'training script'
         )							, )

     # rest from the training program
     parser.add_argument('training_script_args'							, nargs=_UpperCamelCase				)

     return parser.parse_args()


def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =parse_args()

     # Import training_script as a module.
     _SCREAMING_SNAKE_CASE							    =Path(args.training_script				)
     sys.path.append(str(script_fpath.parent.resolve()				)				)
     _SCREAMING_SNAKE_CASE							    =script_fpath.stem
     _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)

     # Patch sys.argv
     _SCREAMING_SNAKE_CASE							    =[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores				)]

     xmp.spawn(mod._mp_fn							, args=()							, nprocs=args.num_cores				)


if __name__ == "__main__":
     main()
