
from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerModel,
						)
						from transformers.models.roformer.modeling_tf_roformer import (
						    TFRoFormerSelfAttention,
						    TFRoFormerSinusoidalPositionalEmbedding,
						)
class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[str]=1_3			,				SCREAMING_SNAKE_CASE_    :     Dict=7			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     List[str]=True			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=True			,				SCREAMING_SNAKE_CASE_    :     Dict=9_9			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=3_2			,				SCREAMING_SNAKE_CASE_    :     Any=2			,				SCREAMING_SNAKE_CASE_    :     Dict=4			,				SCREAMING_SNAKE_CASE_    :     Tuple=3_7			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="gelu"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.1			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Dict=1_6			,				SCREAMING_SNAKE_CASE_    :     Dict=2			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0.02			,				SCREAMING_SNAKE_CASE_    :     Any=3			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=4			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				)			->	int:
					lowercase_	    =   parent
					lowercase_	    =   1_3
					lowercase_	    =   7
					lowercase_	    =   True
					lowercase_	    =   True
					lowercase_	    =   True
					lowercase_	    =   True
					lowercase_	    =   9_9
					lowercase_	    =   3_2
					lowercase_	    =   2
					lowercase_	    =   4
					lowercase_	    =   3_7
					lowercase_	    =   '''gelu'''
					lowercase_	    =   0.1
					lowercase_	    =   0.1
					lowercase_	    =   5_1_2
					lowercase_	    =   1_6
					lowercase_	    =   2
					lowercase_	    =   0.02
					lowercase_	    =   3
					lowercase_	    =   4
					lowercase_	    =   None
	def 		_lowercase    (			self    :     Optional[int]			)			->	List[Any]:
					lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)

					lowercase_	    =   None
					if self.use_input_mask:
									lowercase_	    =   random_attention_mask([self.batch_size, self.seq_length]			)

					lowercase_	    =   None
					if self.use_token_type_ids:
									lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size			)

					lowercase_	    =   None
					lowercase_	    =   None
					lowercase_	    =   None
					if self.use_labels:
									lowercase_	    =   ids_tensor([self.batch_size]			,				self.type_sequence_label_size			)
									lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels			)
									lowercase_	    =   ids_tensor([self.batch_size]			,				self.num_choices			)

					lowercase_	    =   RoFormerConfig(
					    vocab_size=self.vocab_size			,				hidden_size=self.hidden_size			,				num_hidden_layers=self.num_hidden_layers			,				num_attention_heads=self.num_attention_heads			,				intermediate_size=self.intermediate_size			,				hidden_act=self.hidden_act			,				hidden_dropout_prob=self.hidden_dropout_prob			,				attention_probs_dropout_prob=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				type_vocab_size=self.type_vocab_size			,				initializer_range=self.initializer_range			,				return_dict=SCREAMING_SNAKE_CASE_			,				)

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	str:
					lowercase_	    =   TFRoFormerModel(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

					lowercase_	    =   [input_ids, input_mask]
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)

					self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     str			)			->	List[Any]:
					lowercase_	    =   True
					lowercase_	    =   TFRoFormerForCausalLM(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)['''logits''']
					self.parent.assertListEqual(
					    list(prediction_scores.numpy().shape			)			,				[self.batch_size, self.seq_length, self.vocab_size]			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	Optional[Any]:
					lowercase_	    =   TFRoFormerForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Union[str, Any]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   TFRoFormerForSequenceClassification(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			)			->	int:
					lowercase_	    =   self.num_choices
					lowercase_	    =   TFRoFormerForMultipleChoice(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_			,				1			)			,				(1, self.num_choices, 1)			)
					lowercase_	    =   tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_			,				1			)			,				(1, self.num_choices, 1)			)
					lowercase_	    =   tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_			,				1			)			,				(1, self.num_choices, 1)			)
					lowercase_	    =   {
					    '''input_ids''': multiple_choice_inputs_ids,
					    '''attention_mask''': multiple_choice_input_mask,
					    '''token_type_ids''': multiple_choice_token_type_ids,
					}
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_choices)			)
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	List[Any]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   TFRoFormerForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels)			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	Optional[int]:
					lowercase_	    =   TFRoFormerForQuestionAnswering(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.start_logits.shape			,				(self.batch_size, self.seq_length)			)
					self.parent.assertEqual(result.end_logits.shape			,				(self.batch_size, self.seq_length)			)




	def 		_lowercase    (			self    :     int			)			->	Union[str, Any]:
					lowercase_	    =   self.prepare_config_and_inputs()
					(
					    (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      
					)	    =   config_and_inputs
					lowercase_	    =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
					return config, inputs_dict

@require_tf
class 					lowercase__(							UpperCAmelCase					,		UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :List[str]       	=      (
	    (
	        TFRoFormerModel,
	        TFRoFormerForCausalLM,
	        TFRoFormerForMaskedLM,
	        TFRoFormerForQuestionAnswering,
	        TFRoFormerForSequenceClassification,
	        TFRoFormerForTokenClassification,
	        TFRoFormerForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	a     :str       	=      (
	    {
	        'feature-extraction': TFRoFormerModel,
	        'fill-mask': TFRoFormerForMaskedLM,
	        'question-answering': TFRoFormerForQuestionAnswering,
	        'text-classification': TFRoFormerForSequenceClassification,
	        'text-generation': TFRoFormerForCausalLM,
	        'token-classification': TFRoFormerForTokenClassification,
	        'zero-shot': TFRoFormerForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	a     :Tuple       	=      False
	a     :Any       	=      False
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			)			->	str:
					if pipeline_test_casse_name == "TextGenerationPipelineTests":
									return True

					return False
	def 		_lowercase    (			self    :     Tuple			)			->	Dict:
					lowercase_	    =   TFRoFormerModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				hidden_size=3_7			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	List[str]:
					self.config_tester.run_common_tests()
	def 		_lowercase    (			self    :     Any			)			->	Tuple:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Tuple			)			->	Union[str, Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[int]			)			->	Optional[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_lm_head(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			)			->	List[str]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			)			->	List[str]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_			)




	@slow
	def 		_lowercase    (			self    :     List[str]			)			->	List[str]:
					lowercase_	    =   TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''			)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)

@require_tf
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@slow
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					lowercase_	    =   TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''			)
					lowercase_	    =   tf.constant([[0, 1, 2, 3, 4, 5]]			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)[0]

					# TODO Replace vocab size
					lowercase_	    =   5_0_0_0_0

					lowercase_	    =   [1, 6, vocab_size]
					self.assertEqual(output.shape			,				SCREAMING_SNAKE_CASE_			)

					print(output[:, :3, :3]			)

					# TODO Replace values below with what was printed above.
					lowercase_	    =   tf.constant(
					    [
					        [
					            [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
					            [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
					            [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
					        ]
					    ]			)
					tf.debugging.assert_near(output[:, :3, :3]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-4			)

@require_tf
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	a     :List[Any]       	=      1e-4
	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					lowercase_	    =   tf.constant([[4, 1_0]]			)
					lowercase_	    =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=6			,				embedding_dim=6			)

					lowercase_	    =   emba(input_ids.shape			)
					lowercase_	    =   tf.constant(
					    [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]			)

					tf.debugging.assert_near(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				atol=self.tolerance			)




	def 		_lowercase    (			self    :     Optional[Any]			)			->	Optional[int]:
					lowercase_	    =   tf.constant(
					    [
					        [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
					        [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
					        [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
					    ]			)
					lowercase_	    =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2			,				embedding_dim=5_1_2			)
					emba([2, 1_6, 5_1_2]			)
					lowercase_	    =   emba.weight[:3, :5]

					tf.debugging.assert_near(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				atol=self.tolerance			)

@require_tf
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	a     :Any       	=      1e-4
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	List[str]:
					# 2,12,16,64
					lowercase_	    =   tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4			,				dtype=tf.floataa			)			,				shape=(2, 1_2, 1_6, 6_4)			) / 1_0_0

					lowercase_	    =   -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4			,				dtype=tf.floataa			)			,				shape=(2, 1_2, 1_6, 6_4)			) / 1_0_0

					lowercase_	    =   TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2			,				embedding_dim=6_4			)
					lowercase_	    =   embed_positions([2, 1_6, 7_6_8]			)[None, None, :, :]

					lowercase_    ,      lowercase_	    =   TFRoFormerSelfAttention.apply_rotary_position_embeddings(
					    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   tf.constant(
					    [
					        [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
					        [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
					        [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
					        [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
					        [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
					        [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
					    ]			)
					lowercase_	    =   tf.constant(
					    [
					        [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
					        [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
					        [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
					        [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
					        [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
					        [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
					    ]			)

					tf.debugging.assert_near(query_layer[0, 0, :6, :8]			,				SCREAMING_SNAKE_CASE_			,				atol=self.tolerance			)
					tf.debugging.assert_near(key_layer[0, 0, :6, :8]			,				SCREAMING_SNAKE_CASE_			,				atol=self.tolerance			)












'''simple docstring'''



from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__UpperCamelCase   :							Optional[int]         =   """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

__UpperCamelCase   :							Tuple         =   """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

__UpperCamelCase   :							Optional[int]         =   """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str     )	->      List[Any]:

   """simple docstring"""
   return float((preds == labels).mean()     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     simple_accuracy(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   __a					  =     float(fa_score(y_true=SCREAMING_SNAKE_CASE__,       y_pred=SCREAMING_SNAKE_CASE__     )     )
   return {
       "accuracy": acc,
       "f1": fa,
   }



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      int:

   """simple docstring"""
   __a					  =     float(pearsonr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   __a					  =     float(spearmanr(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )[0]     )
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }



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




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         if self.config_name not in [
             "sst2",
             "mnli",
             "mnli_mismatched",
             "mnli_matched",
             "cola",
             "stsb",
             "mrpc",
             "qqp",
             "qnli",
             "rte",
             "wnli",
             "hans",
         ]:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'   )
         return datasets.MetricInfo(
             description=_DESCRIPTION			, citation=_CITATION			, inputs_description=_KWARGS_DESCRIPTION			, features=datasets.Features(
                 {
                     'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'   ),
                     'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'   ),
                 }   )			, codebase_urls=[]			, reference_urls=[]			, format='numpy'			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(lowerCamelCase			, lowerCamelCase   )}
         elif self.config_name == "stsb":
            return pearson_and_spearman(lowerCamelCase			, lowerCamelCase   )
         elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(lowerCamelCase			, lowerCamelCase   )
         elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(lowerCamelCase			, lowerCamelCase   )}
         else:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'   )