





"""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": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
lowerCamelCase_								=		{
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
lowerCamelCase_								=		{
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


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 					_SCREAMING_SNAKE_CASE(   A__  ):
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_   :      Tuple							=  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_   :      int							=  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE_   :      int							=  DPRContextEncoderTokenizer






class 					_SCREAMING_SNAKE_CASE(   A__  ):
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_   :      Tuple							=  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_   :      Any							=  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  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"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "






@add_start_docstrings(A__  )
class 					_SCREAMING_SNAKE_CASE:




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





             """simple docstring"""






             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:
                   __SCREAMING_SNAKE_CASE   :Tuple						=  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		,)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  titles if not isinstance(_a		,_a							) else [titles]
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  texts if not isinstance(_a		,_a							) else [texts]
             __SCREAMING_SNAKE_CASE   :int						=  len(_a							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  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.'''
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  super().__call__(_a		,_a		,padding=_a		,truncation=_a							)['''input_ids''']
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  super().__call__(_a		,add_special_tokens=_a		,padding=_a		,truncation=_a							)['''input_ids''']
             __SCREAMING_SNAKE_CASE   :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(_a		,_a							)
                 ]
             }
             if return_attention_mask is not False:
                   __SCREAMING_SNAKE_CASE   :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]							)
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  attention_mask
             return self.pad(_a		,padding=_a		,max_length=_a		,return_tensors=_a							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 64		,SCREAMING_SNAKE_CASE__ = 4		,)			->      List[DPRSpanPrediction]:





             """simple docstring"""






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

                   __SCREAMING_SNAKE_CASE   :List[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=_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     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,)			->      List[DPRSpanPrediction]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  []
             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)							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  sorted(_a		,key=lambda SCREAMING_SNAKE_CASE__							: x[1]		,reverse=_a							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  []
             for (start_index, end_index), score in scores:
                   assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
                   __SCREAMING_SNAKE_CASE   :Optional[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(_a							) == top_spans:
                         break
             return chosen_span_intervals






@add_end_docstrings(A__  )
class 					_SCREAMING_SNAKE_CASE(   A__    ,	A__  ):
       SCREAMING_SNAKE_CASE_   :      int							=  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_   :      Dict							=  READER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  READER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  ['''input_ids''', '''attention_mask''']
       SCREAMING_SNAKE_CASE_   :      Optional[Any]							=  DPRReaderTokenizer


'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase :      List[Any]					 =     version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
     from nltk import word_tokenize


lowerCamelCase :      Any					 =     "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowerCamelCase :      Optional[Any]					 =     "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowerCamelCase :      Optional[Any]					 =     "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"







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





  def 						A  (		self      :	Tuple )	->    str:





       '''simple docstring'''






       return datasets.MetricInfo(
           description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
               {
                   'predictions': datasets.Value('string'							,					id='sequence' ),
                   'references': datasets.Value('string'							,					id='sequence' ),
               } )							,					codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']							,					reference_urls=[
               'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
               'https://en.wikipedia.org/wiki/METEOR',
           ]							,					)





  def 						A  (		self      :	Union[str, Any]							,					_a      :	Union[str, Any] )	->    Optional[int]:





       '''simple docstring'''






       import nltk

       nltk.download('wordnet' )
       if NLTK_VERSION >= version.Version('3.6.5' ):
            nltk.download('punkt' )
       if NLTK_VERSION >= version.Version('3.6.6' ):
            nltk.download('omw-1.4' )





  def 						A  (		self      :	int							,					_a      :	Tuple							,					_a      :	List[str]							,					_a      :	List[str]=0.9							,					_a      :	Dict=3							,					_a      :	Optional[int]=0.5 )	->    Optional[int]:





       '''simple docstring'''






       if NLTK_VERSION >= version.Version('3.6.5' ):
            _SCREAMING_SNAKE_CASE							    =[
                meteor_score.single_meteor_score(
                    word_tokenize(_a )							,					word_tokenize(_a )							,					alpha=_a							,					beta=_a							,					gamma=_a )
                for ref, pred in zip(_a							,					_a )
            ]
       else:
            _SCREAMING_SNAKE_CASE							    =[
                meteor_score.single_meteor_score(_a							,					_a							,					alpha=_a							,					beta=_a							,					gamma=_a )
                for ref, pred in zip(_a							,					_a )
            ]

       return {"meteor": np.mean(_a )}
