





"""simple docstring"""






import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowerCAmelCase_			=							'''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowerCAmelCase_			=							'''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

lowerCAmelCase_			=							'''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,	_KWARGS_DESCRIPTION							)
class 	lowerCAmelCase					(      datasets.Metric							):






       def 							__A (   self     ):
              if version.parse(scb.__version__     ) < version.parse('1.4.12'     ):
                     raise ImportWarning(
                         'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                         'You can install it with `pip install "sacrebleu>=1.4.12"`.'     )
              return datasets.MetricInfo(
                  description=_DESCRIPTION    ,  citation=_CITATION    ,  homepage='http://www.cs.umd.edu/~snover/tercom/'    ,  inputs_description=_KWARGS_DESCRIPTION    ,  features=datasets.Features(
                      {
                          'predictions': datasets.Value('string'    ,  id='sequence'     ),
                          'references': datasets.Sequence(datasets.Value('string'    ,  id='sequence'     )    ,  id='references'     ),
                      }     )    ,  codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']    ,  reference_urls=[
                      'https://github.com/jhclark/tercom',
                  ]    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = False    ,  a__ = False    ,  a__ = False    ,  a__ = False    ,  ):
              _UpperCAmelCase     =     len(references[0]     )
              if any(len(a__     ) != references_per_prediction for refs in references     ):
                     raise ValueError('Sacrebleu requires the same number of references for each prediction'     )
              _UpperCAmelCase     =     [[refs[i] for refs in references] for i in range(a__     )]

              _UpperCAmelCase     =     TER(
                  normalized=a__    ,  no_punct=a__    ,  asian_support=a__    ,  case_sensitive=a__    ,  )
              _UpperCAmelCase     =     sb_ter.corpus_score(a__    ,  a__     )

              return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}













"""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


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_			=							{
    '''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'''
        ),
    },
}
lowerCAmelCase_			=							{
    '''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'''
        ),
    },
}
lowerCAmelCase_			=							{
    '''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'''
        ),
    },
}

lowerCAmelCase_			=							{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
lowerCAmelCase_			=							{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
lowerCAmelCase_			=							{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


lowerCAmelCase_			=							{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase_			=							{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase_			=							{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class 	lowerCAmelCase					(      snake_case							):
       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 	lowerCAmelCase					(      snake_case							):
       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


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

lowerCAmelCase_			=							collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowerCAmelCase_			=							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(snake_case							)
class 	lowerCAmelCase					:






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






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = 16    ,  a__ = 64    ,  a__ = 4    ,  ):
              _UpperCAmelCase     =     reader_input['input_ids']
              _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     reader_output[:3]
              _UpperCAmelCase     =     len(a__     )
              _UpperCAmelCase     =     sorted(range(a__     )    ,  reverse=a__    ,  key=relevance_logits.__getitem__     )
              _UpperCAmelCase     =     []
              for doc_id in sorted_docs:
                     _UpperCAmelCase     =     list(input_ids[doc_id]     )
                     # assuming question & title information is at the beginning of the sequence
                     _UpperCAmelCase     =     sequence_ids.index(self.sep_token_id    ,  2     ) + 1  # second sep id
                     if sequence_ids[-1] == self.pad_token_id:
                            _UpperCAmelCase     =     sequence_ids.index(self.pad_token_id     )
                     else:
                            _UpperCAmelCase     =     len(a__     )

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






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     []
              for start_index, start_score in enumerate(a__     ):
                     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)     )
              _UpperCAmelCase     =     sorted(a__    ,  key=lambda a__     : x[1]    ,  reverse=a__     )
              _UpperCAmelCase     =     []
              for (start_index, end_index), score in scores:
                     assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
                     _UpperCAmelCase     =     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(a__     ) == top_spans:
                            break
              return chosen_span_intervals



@add_end_docstrings(snake_case							)
class 	lowerCAmelCase					(      snake_case			,	snake_case							):
       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






