
"""simple docstring"""






import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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__ :       Tuple						     =			"▁"

lowerCamelCase__ :       str						     =			get_tests_dir("fixtures/test_sentencepiece.model")




@require_sentencepiece
@require_tokenizers
class 				lowercase__(				_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  BigBirdTokenizer
	UpperCamelCase				=  BigBirdTokenizerFast
	UpperCamelCase				=  True
	UpperCamelCase				=  True





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







			'''simple docstring'''




			super().setUp()

			SCREAMING_SNAKE_CASE       :     Tuple				= self.tokenizer_class(lowerCamelCase_		, keep_accents=lowerCamelCase_					)
			tokenizer.save_pretrained(self.tmpdirname					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= '''<s>'''
			SCREAMING_SNAKE_CASE       :     Dict				= 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    :List[Any]					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= list(self.get_tokenizer().get_vocab().keys()					)

			self.assertEqual(vocab_keys[0]		, '''<unk>'''					)
			self.assertEqual(vocab_keys[1]		, '''<s>'''					)
			self.assertEqual(vocab_keys[-1]		, '''[MASK]'''					)
			self.assertEqual(len(lowerCamelCase_					)		, 10_04					)





	def   __lowerCAmelCase (						self    :Any					)							->			int:







			'''simple docstring'''




			self.assertEqual(self.get_tokenizer().vocab_size		, 10_00					)





	def   __lowerCAmelCase (						self    :Dict					)							->			Optional[Any]:







			'''simple docstring'''




			if not self.test_rust_tokenizer:
					return

			SCREAMING_SNAKE_CASE       :     int				= self.get_tokenizer()
			SCREAMING_SNAKE_CASE       :     List[str]				= self.get_rust_tokenizer()

			SCREAMING_SNAKE_CASE       :     str				= '''I was born in 92000, and this is falsé.'''

			SCREAMING_SNAKE_CASE       :     List[str]				= tokenizer.tokenize(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= rust_tokenizer.tokenize(lowerCamelCase_					)
			self.assertListEqual(lowerCamelCase_		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[str]				= tokenizer.encode(lowerCamelCase_		, add_special_tokens=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= rust_tokenizer.encode(lowerCamelCase_		, add_special_tokens=lowerCamelCase_					)
			self.assertListEqual(lowerCamelCase_		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= self.get_rust_tokenizer()
			SCREAMING_SNAKE_CASE       :     Tuple				= tokenizer.encode(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Tuple				= rust_tokenizer.encode(lowerCamelCase_					)
			self.assertListEqual(lowerCamelCase_		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= BigBirdTokenizer(lowerCamelCase_		, keep_accents=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= tokenizer.tokenize('''This is a test'''					)
			self.assertListEqual(lowerCamelCase_		, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']					)

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(lowerCamelCase_					)		, [2_85, 46, 10, 1_70, 3_82]		, )

			SCREAMING_SNAKE_CASE       :     List[Any]				= 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''',
			        '''.''',
			    ]		, )
			SCREAMING_SNAKE_CASE       :     List[Any]				= tokenizer.convert_tokens_to_ids(lowerCamelCase_					)
			self.assertListEqual(
			    lowerCamelCase_		, [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]		, )

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''					)





	@slow
	def   __lowerCAmelCase (						self    :int					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= '''Hello World!'''
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [65, 1_85_36, 22_60, 1_01, 66]

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





	@slow
	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (
			    '''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'''
			)
			# fmt: off
			SCREAMING_SNAKE_CASE       :     Any				= [65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
			# fmt: on
			self.assertListEqual(lowerCamelCase_		, self.big_tokenizer.encode(lowerCamelCase_					)					)





	@require_torch
	@slow
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Tuple:







			'''simple docstring'''




			import torch

			from transformers import BigBirdConfig, BigBirdModel

			# Build sequence
			SCREAMING_SNAKE_CASE       :     int				= list(self.big_tokenizer.get_vocab().keys()					)[:10]
			SCREAMING_SNAKE_CASE       :     Dict				= ''' '''.join(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Tuple				= self.big_tokenizer.encode_plus(lowerCamelCase_		, return_tensors='''pt'''		, return_token_type_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Any				= self.big_tokenizer.batch_encode_plus(
			    [sequence + ''' ''' + sequence]		, return_tensors='''pt'''		, return_token_type_ids=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= BigBirdConfig(attention_type='''original_full'''					)
			SCREAMING_SNAKE_CASE       :     Dict				= BigBirdModel(lowerCamelCase_					)

			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    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= tokenizer.decode(tokenizer('''Paris is the [MASK].'''					).input_ids					)

			self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]'''					)







	@slow
	def   __lowerCAmelCase (						self    :Any					)							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= {'''input_ids''': [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 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, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
			# fmt: on

			self.tokenizer_integration_test_util(
			    expected_encoding=lowerCamelCase_		, model_name='''google/bigbird-roberta-base'''		, revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''		, )







"""simple docstring"""






from sklearn.metrics import fa_score

import datasets


lowerCamelCase__ :       List[Any]						     =			"\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


lowerCamelCase__ :       str						     =			"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. 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 `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - '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. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


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




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION   )
class 				lowercase__(				datasets.Metric   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			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.f1_score.html''']		, )







	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :int=None		, lowerCamelCase_    :str=1		, lowerCamelCase_    :Union[str, Any]="binary"		, lowerCamelCase_    :Dict=None					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= fa_score(
			    lowerCamelCase_		, lowerCamelCase_		, labels=lowerCamelCase_		, pos_label=lowerCamelCase_		, average=lowerCamelCase_		, sample_weight=lowerCamelCase_					)
			return {"f1": float(lowerCamelCase_					) if score.size == 1 else score}





