



'''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


lowerCamelCase							:Union[str, Any]				    =					get_tests_dir('''fixtures/test_sentencepiece.model''')







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


	def     _a	(self							):
				super().setUp()

				A_	:						Optional[int]	=     ReformerTokenizer(__lowerCAmelCase       ,      keep_accents=__lowerCAmelCase							)
				tokenizer.save_pretrained(self.tmpdirname							)


	def     _a	(self							):
				A_	:						List[str]	=     """<s>"""
				A_	:						Any	=     1

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


	def     _a	(self							):
				A_	:						str	=     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     _a	(self							):
				self.assertEqual(self.get_tokenizer().vocab_size       ,      1000							)


	def     _a	(self							):
				if not self.test_rust_tokenizer:
							return

				A_	:						Union[str, Any]	=     self.get_tokenizer()
				A_	:						Tuple	=     self.get_rust_tokenizer()

				A_	:						int	=     """I was born in 92000, and this is falsé."""

				A_	:						str	=     tokenizer.tokenize(__lowerCAmelCase							)
				A_	:						str	=     rust_tokenizer.tokenize(__lowerCAmelCase							)
				self.assertListEqual(__lowerCAmelCase       ,      __lowerCAmelCase							)

				A_	:						Any	=     tokenizer.encode(__lowerCAmelCase       ,      add_special_tokens=__lowerCAmelCase							)
				A_	:						Any	=     rust_tokenizer.encode(__lowerCAmelCase       ,      add_special_tokens=__lowerCAmelCase							)
				self.assertListEqual(__lowerCAmelCase       ,      __lowerCAmelCase							)

				A_	:						List[Any]	=     self.get_rust_tokenizer()
				A_	:						Any	=     tokenizer.encode(__lowerCAmelCase							)
				A_	:						Optional[int]	=     rust_tokenizer.encode(__lowerCAmelCase							)
				self.assertListEqual(__lowerCAmelCase       ,      __lowerCAmelCase							)


	def     _a	(self       ,      lowercase=15							):
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
										A_	:						Any	=     self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,      **__lowerCAmelCase							)

										# Simple input
										A_	:						int	=     """This is a simple input"""
										A_	:						Dict	=     ["""This is a simple input 1""", """This is a simple input 2"""]
										A_	:						Optional[Any]	=     ("""This is a simple input""", """This is a pair""")
										A_	:						str	=     [
										    ("""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     _a	(self							):
				pass


	def     _a	(self							):
				A_	:						Optional[Any]	=     ReformerTokenizer(__lowerCAmelCase       ,      keep_accents=__lowerCAmelCase							)

				A_	:						str	=     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]       ,      )

				A_	:						str	=     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""",
				        """.""",
				    ]       ,      )
				A_	:						Tuple	=     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]       ,      )

				A_	:						Optional[int]	=     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     _a	(self							):
				return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""							)


	@slow
	def     _a	(self							):
				A_	:						Tuple	=     """Hello World!"""
				A_	:						Tuple	=     [126, 32, 262, 152, 38, 72, 287]

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


	@slow
	def     _a	(self							):
				A_	:						Tuple	=     (
				    """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"""
				)
				A_	:						Tuple	=     [
				    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     _a	(self							):
				import torch

				from transformers import ReformerConfig, ReformerModel

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

				A_	:						Union[str, Any]	=     ReformerConfig()
				# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
				A_	:						Dict	=     encoded_sequence["""input_ids"""].shape
				A_	:						Tuple	=     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     _a	(self							):
				# fmt: off
				A_	:						Union[str, Any]	=     {"""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
				A_	:						Optional[Any]	=     [
				    """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}
