'''simple docstring'''





import datasets

from .evaluate import evaluate


lowerCAmelCase			:							int     ="\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

lowerCAmelCase			:							Optional[Any]     ="\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

lowerCAmelCase			:							str     ="\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"




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





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Any:


          """simple docstring"""


          return datasets.MetricInfo(
              description=_DESCRIPTION    ,  citation=_CITATION    ,  inputs_description=_KWARGS_DESCRIPTION    ,  features=datasets.Features(
                  {
                      """predictions""": {"""id""": datasets.Value("""string"""), """prediction_text""": datasets.Value("""string""")},
                      """references""": {
                          """id""": datasets.Value("""string"""),
                          """answers""": datasets.features.Sequence(
                              {
                                  """text""": datasets.Value("""string"""),
                                  """answer_start""": datasets.Value("""int32"""),
                              }),
                      },
                  })    ,  codebase_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]    ,  reference_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Union[str, Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
          _lowerCamelCase     :		str 						=						[
              {
                  """paragraphs""": [
                      {
                          """qas""": [
                              {
                                  """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                                  """id""": ref["""id"""],
                              }
                              for ref in references
                          ]
                      }
                  ]
              }
          ]
          _lowerCamelCase     :		List[Any] 						=						evaluate(dataset=_UpperCamelCase    ,  predictions=_UpperCamelCase)
          return score





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
    import torch

    from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-base""")
          _lowerCamelCase     :		Optional[int] 						=						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
          # The dog is cute and lives in the garden house

          _lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		str 						=						torch.tensor(
              [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
          #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
          #  xlmr.eval()
          #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
          with torch.no_grad():
              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase)["""last_hidden_state"""].detach()
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]    ,  _UpperCamelCase    ,  atol=1E-3))




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-large""")
          _lowerCamelCase     :		Optional[Any] 						=						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]])
          # The dog is cute and lives in the garden house

          _lowerCamelCase     :		str 						=						torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
              [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
          #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
          #  xlmr.eval()
          #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
          with torch.no_grad():
              _lowerCamelCase     :		int 						=						model(_UpperCamelCase)["""last_hidden_state"""].detach()
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]    ,  _UpperCamelCase    ,  atol=1E-3))




