"""simple docstring"""


import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__     						=  get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							ReformerTokenizer
    _snake_case   :     Optional[Any]   							=							ReformerTokenizerFast
    _snake_case   :     Optional[Any]   							=							True
    _snake_case   :     Union[str, Any]   							=							False
    _snake_case   :     str   							=							True


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      super().setUp()

      _UpperCAmelCase         =					ReformerTokenizer(__lowerCAmelCase      , keep_accents=__lowerCAmelCase    )
      tokenizer.save_pretrained(self.tmpdirname    )


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					"""<s>"""
      _UpperCAmelCase         =					1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase    )      , __lowerCAmelCase    )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase    )      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					list(self.get_tokenizer().get_vocab().keys()    )

      self.assertEqual(vocab_keys[0]      , """<unk>"""    )
      self.assertEqual(vocab_keys[1]      , """<s>"""    )
      self.assertEqual(vocab_keys[-1]      , """j"""    )
      self.assertEqual(len(__lowerCAmelCase    )      , 1000    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      self.assertEqual(self.get_tokenizer().vocab_size      , 1000    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      if not self.test_rust_tokenizer:
        return

      _UpperCAmelCase         =					self.get_tokenizer()
      _UpperCAmelCase         =					self.get_rust_tokenizer()

      _UpperCAmelCase         =					"""I was born in 92000, and this is falsé."""

      _UpperCAmelCase         =					tokenizer.tokenize(__lowerCAmelCase    )
      _UpperCAmelCase         =					rust_tokenizer.tokenize(__lowerCAmelCase    )
      self.assertListEqual(__lowerCAmelCase      , __lowerCAmelCase    )

      _UpperCAmelCase         =					tokenizer.encode(__lowerCAmelCase      , add_special_tokens=__lowerCAmelCase    )
      _UpperCAmelCase         =					rust_tokenizer.encode(__lowerCAmelCase      , add_special_tokens=__lowerCAmelCase    )
      self.assertListEqual(__lowerCAmelCase      , __lowerCAmelCase    )

      _UpperCAmelCase         =					self.get_rust_tokenizer()
      _UpperCAmelCase         =					tokenizer.encode(__lowerCAmelCase    )
      _UpperCAmelCase         =					rust_tokenizer.encode(__lowerCAmelCase    )
      self.assertListEqual(__lowerCAmelCase      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     str=15    ):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
          _UpperCAmelCase         =					self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase      , **__lowerCAmelCase    )

          # Simple input
          _UpperCAmelCase         =					"""This is a simple input"""
          _UpperCAmelCase         =					["""This is a simple input 1""", """This is a simple input 2"""]
          _UpperCAmelCase         =					("""This is a simple input""", """This is a pair""")
          _UpperCAmelCase         =					[
              ("""This is a simple input 1""", """This is a simple input 2"""),
              ("""This is a simple pair 1""", """This is a simple pair 2"""),
          ]

          # Simple input tests
          self.assertRaises(__lowerCAmelCase      , tokenizer_r.encode      , __lowerCAmelCase      , max_length=__lowerCAmelCase      , padding="""max_length"""    )

          # Simple input
          self.assertRaises(__lowerCAmelCase      , tokenizer_r.encode_plus      , __lowerCAmelCase      , max_length=__lowerCAmelCase      , padding="""max_length"""    )

          # Simple input
          self.assertRaises(
              __lowerCAmelCase      , tokenizer_r.batch_encode_plus      , __lowerCAmelCase      , max_length=__lowerCAmelCase      , padding="""max_length"""      , )

          # Pair input
          self.assertRaises(__lowerCAmelCase      , tokenizer_r.encode      , __lowerCAmelCase      , max_length=__lowerCAmelCase      , padding="""max_length"""    )

          # Pair input
          self.assertRaises(__lowerCAmelCase      , tokenizer_r.encode_plus      , __lowerCAmelCase      , max_length=__lowerCAmelCase      , padding="""max_length"""    )

          # Pair input
          self.assertRaises(
              __lowerCAmelCase      , tokenizer_r.batch_encode_plus      , __lowerCAmelCase      , max_length=__lowerCAmelCase      , padding="""max_length"""      , )


    def    lowerCAmelCase_     (    self		:     str    ):
      pass


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					ReformerTokenizer(__lowerCAmelCase      , keep_accents=__lowerCAmelCase    )

      _UpperCAmelCase         =					tokenizer.tokenize("""This is a test"""    )
      self.assertListEqual(__lowerCAmelCase      , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__lowerCAmelCase    )      , [285, 46, 10, 170, 382]      , )

      _UpperCAmelCase         =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
      self.assertListEqual(
          __lowerCAmelCase      , [
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """9""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """é""",
              """.""",
          ]      , )
      _UpperCAmelCase         =					tokenizer.convert_tokens_to_ids(__lowerCAmelCase    )
      self.assertListEqual(
          __lowerCAmelCase      , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      , )

      _UpperCAmelCase         =					tokenizer.convert_ids_to_tokens(__lowerCAmelCase    )
      self.assertListEqual(
          __lowerCAmelCase      , [
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """<unk>""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """<unk>""",
              """.""",
          ]      , )


    @cached_property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""    )


    @slow
    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					"""Hello World!"""
      _UpperCAmelCase         =					[126, 32, 262, 152, 38, 72, 287]

      self.assertListEqual(__lowerCAmelCase      , self.big_tokenizer.encode(__lowerCAmelCase    )    )


    @slow
    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					(
          """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
          """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
      )
      _UpperCAmelCase         =					[
          108,
          265,
          24,
          111,
          4,
          258,
          156,
          35,
          28,
          275,
          3,
          259,
          297,
          260,
          84,
          4,
          35,
          110,
          44,
          8,
          259,
          91,
          268,
          21,
          11,
          209,
          274,
          109,
          266,
          277,
          117,
          86,
          93,
          315,
          258,
          278,
          258,
          277,
          258,
          0,
          258,
          288,
          258,
          319,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          0,
          258,
          287,
          258,
          315,
          258,
          289,
          258,
          278,
          99,
          269,
          266,
          262,
          8,
          259,
          241,
          4,
          217,
          230,
          268,
          266,
          55,
          168,
          106,
          75,
          193,
          266,
          223,
          27,
          49,
          26,
          282,
          25,
          264,
          299,
          19,
          26,
          0,
          258,
          277,
          117,
          86,
          93,
          176,
          183,
          270,
          11,
          262,
          42,
          61,
          265,
      ]

      self.assertListEqual(__lowerCAmelCase      , self.big_tokenizer.encode(__lowerCAmelCase    )    )


    @require_torch
    @slow
    def    lowerCAmelCase_     (    self		:     str    ):
      import torch

      from transformers import ReformerConfig, ReformerModel

      # Build sequence
      _UpperCAmelCase         =					list(self.big_tokenizer.get_vocab().keys()    )[:10]
      _UpperCAmelCase         =					""" """.join(__lowerCAmelCase    )
      _UpperCAmelCase         =					self.big_tokenizer.encode_plus(__lowerCAmelCase      , return_tensors="""pt"""    )
      _UpperCAmelCase         =					self.big_tokenizer.batch_encode_plus([sequence, sequence]      , return_tensors="""pt"""    )

      _UpperCAmelCase         =					ReformerConfig()
      # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
      _UpperCAmelCase         =					encoded_sequence["""input_ids"""].shape
      _UpperCAmelCase         =					ReformerModel(__lowerCAmelCase    )

      # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

      with torch.no_grad():
        model(**__lowerCAmelCase    )
        model(**__lowerCAmelCase    )


    @slow
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      # fmt: off
      _UpperCAmelCase         =					{"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # This tokenizer does not know some characters like ")".
      # That is the reason why we use very simple texts here.
      # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
      _UpperCAmelCase         =					[
          """This is a very simple sentence.""",
          """The quick brown fox jumps over the lazy dog.""",
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=__lowerCAmelCase      , model_name="""google/reformer-crime-and-punishment"""      , revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""      , padding=__lowerCAmelCase      , sequences=__lowerCAmelCase      , )

"""simple docstring"""


from sklearn.metrics import recall_score

import datasets


UpperCAmelCase__     						=  """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


UpperCAmelCase__     						=  """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


UpperCAmelCase__     						=  """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""


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


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return datasets.MetricInfo(
          description=_DESCRIPTION      , citation=_CITATION      , inputs_description=_KWARGS_DESCRIPTION      , features=datasets.Features(
              {
                  """predictions""": datasets.Sequence(datasets.Value("""int32"""    )    ),
                  """references""": datasets.Sequence(datasets.Value("""int32"""    )    ),
              }
              if self.config_name == """multilabel"""
              else {
                  """predictions""": datasets.Value("""int32"""    ),
                  """references""": datasets.Value("""int32"""    ),
              }    )      , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]      , )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Optional[Any]      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     Dict=1      , __lowerCAmelCase		:     List[str]="binary"      , __lowerCAmelCase		:     Any=None      , __lowerCAmelCase		:     int="warn"      , ):
      _UpperCAmelCase         =					recall_score(
          __lowerCAmelCase      , __lowerCAmelCase      , labels=__lowerCAmelCase      , pos_label=__lowerCAmelCase      , average=__lowerCAmelCase      , sample_weight=__lowerCAmelCase      , zero_division=__lowerCAmelCase      , )
      return {"recall": float(__lowerCAmelCase    ) if score.size == 1 else score}
