"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
lowercase__ 	=     pytest.mark.integration


lowercase__ 	=     {'comet'}
lowercase__ 	=     importlib.util.find_spec('fairseq') is not None

lowercase__ 	=     {'code_eval'}
lowercase__ 	=     os.name == 'nt'

lowercase__ 	=     {'bertscore', 'frugalscore', 'perplexity'}
lowercase__ 	=     importlib.util.find_spec('transformers') is not None







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[Any]:
  @wraps(a_       )
  def wrapper(self		,					_SCREAMING_SNAKE_CASE       ):
    if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
      self.skipTest('"test requires Fairseq"'       )
    else:
      test_case(self		,					a_       )

  return wrapper







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  @wraps(a_       )
  def wrapper(self		,					_SCREAMING_SNAKE_CASE       ):
    if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
      self.skipTest('"test requires transformers"'       )
    else:
      test_case(self		,					a_       )

  return wrapper







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->int:
  @wraps(a_       )
  def wrapper(self		,					_SCREAMING_SNAKE_CASE       ):
    if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
      self.skipTest('"test not supported on Windows"'       )
    else:
      test_case(self		,					a_       )

  return wrapper







def 						__a       (     )     ->Any:
  a__:					Union[str, Any]      							=     [metric_dir.split(os.sep       )[-2] for metric_dir in glob.glob('./metrics/*/'       )]
  return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished




@parameterized.named_parameters(get_local_metric_names()    )
@for_all_test_methods(
    a_	,				a_	,				a_    )
@local
class        __snake_case	(					parameterized.TestCase    ):
      a__				=		{}
      a__				=		None



      @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
      @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning')
      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					str      							=     '[...]'
        a__:					Any      							=     importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('metrics'   ,      lowercase_)).module_path)
        a__:					List[Any]      							=     datasets.load.import_main_class(metric_module.__name__   ,      dataset=lowercase_)
        # check parameters
        a__:					List[Any]      							=     inspect.signature(metric._compute).parameters
        self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()))  # no **kwargs
        # run doctest
        with self.patch_intensive_calls(lowercase_   ,      metric_module.__name__):
          with self.use_local_metrics():
            try:
              a__:					str      							=     doctest.testmod(lowercase_   ,      verbose=lowercase_   ,      raise_on_error=lowercase_)
            except doctest.UnexpectedException as e:
              raise e.exc_info[1]  # raise the exception that doctest caught
        self.assertEqual(results.failed   ,      0)
        self.assertGreater(results.attempted   ,      1)



      @slow
      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					str      							=     '[...]'
        a__:					List[Any]      							=     importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('metrics'   ,      lowercase_)).module_path)
        # run doctest
        with self.use_local_metrics():
          a__:					Tuple      							=     doctest.testmod(lowercase_   ,      verbose=lowercase_   ,      raise_on_error=lowercase_)
        self.assertEqual(results.failed   ,      0)
        self.assertGreater(results.attempted   ,      1)



      @contextmanager
      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      Any:





        '''simple docstring'''





        if metric_name in self.INTENSIVE_CALLS_PATCHER:
          with self.INTENSIVE_CALLS_PATCHER[metric_name](lowercase_):
            yield
        else:
          yield



      @contextmanager
      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        def load_local_metric(lowercase   ,      *lowercase   ,      **lowercase):
          return load_metric(os.path.join('metrics'   ,      lowercase_)   ,      *lowercase_   ,      **lowercase_)

        with patch('datasets.load_metric') as mock_load_metric:
          a__:					int      							=     load_local_metric
          yield



      @classmethod
      def   lowerCamelCase_       (						cls   ,      lowercase)     ->      Any:





        '''simple docstring'''





        def wrapper(lowercase):
          a__:					List[str]      							=     contextmanager(lowercase_)
          a__:					Any      							=     patcher
          return patcher

        return wrapper







@LocalMetricTest.register_intensive_calls_patcher('bleurt'       )
def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[Any]:
  import tensorflow.compat.va as tf
  from bleurt.score import Predictor

  tf.flags.DEFINE_string('sv'		,					''		,					''       )  # handle pytest cli flags




  class        __snake_case	(					a_    ):



        def   lowerCamelCase_       (						self   ,      lowercase)     ->      Optional[Any]:





          '''simple docstring'''





          assert len(input_dict['input_ids']) == 2
          return np.array([1.03, 1.04])

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
  with patch('bleurt.score._create_predictor'       ) as mock_create_predictor:
    a__:					Any      							=     MockedPredictor()
    yield







@LocalMetricTest.register_intensive_calls_patcher('bertscore'       )
def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Dict:
  import torch

  def bert_cos_score_idf(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					*_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE       ):
    return torch.tensor([[1.0, 1.0, 1.0]] * len(a_       )       )

  # mock get_model which is supposed to do download a bert model
  # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
  with patch('bert_score.scorer.get_model'       ), patch(
      'bert_score.scorer.bert_cos_score_idf'       ) as mock_bert_cos_score_idf:
    a__:					Dict      							=     bert_cos_score_idf
    yield







@LocalMetricTest.register_intensive_calls_patcher('comet'       )
def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Union[str, Any]:
  def load_from_checkpoint(_SCREAMING_SNAKE_CASE       ):




    class        __snake_case	:



          def   lowerCamelCase_       (						self   ,      lowercase   ,      *lowercase   ,      **lowercase)     ->      Tuple:





            '''simple docstring'''





            assert len(lowercase_) == 2
            a__:					str      							=     [0.19, 0.92]
            return scores, sum(lowercase_) / len(lowercase_)







    return Model()

  # mock load_from_checkpoint which is supposed to do download a bert model
  # mock load_from_checkpoint which is supposed to do download a bert model
  with patch('comet.download_model'       ) as mock_download_model:
    a__:					Union[str, Any]      							=     None
    with patch('comet.load_from_checkpoint'       ) as mock_load_from_checkpoint:
      a__:					Tuple      							=     load_from_checkpoint
      yield







def 						__a       (     )     ->str:
  a__:					List[Any]      							=     load_metric(os.path.join('metrics'		,					'seqeval'       )       )
  a__:					int      							=     'ERROR'
  a__:					int      							=     F'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'
  with pytest.raises(a_		,					match=re.escape(a_       )       ):
    metric.compute(predictions=[]		,					references=[]		,					scheme=a_       )



"""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


lowercase__ 	=     logging.get_logger(__name__)

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

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


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




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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

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


lowercase__ 	=     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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(__lowerCAmelCase    )
class        __snake_case	:



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





        '''simple docstring'''





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



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





        '''simple docstring'''





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

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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      )     ->      List[DPRSpanPrediction]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        for start_index, start_score in enumerate(lowercase):
          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(lowercase   ,      key=lambda lowercase: x[1]   ,      reverse=lowercase)
        a__:					Optional[Any]      							=     []
        for (start_index, end_index), score in scores:
          if start_index > end_index:
            raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]')
          a__:					str      							=     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(lowercase) == top_spans:
            break
        return chosen_span_intervals







@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		READER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		READER_PRETRAINED_INIT_CONFIGURATION
      a__				=		["""input_ids""", """attention_mask"""]


