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

import datasets


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

SCREAMING_SNAKE_CASE__   =					"\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

SCREAMING_SNAKE_CASE__   =					"\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Tuple   )   ->			Tuple:





						"""simple docstring"""






						return float((preds == labels).mean()   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Union[str, Any]   )   ->			Union[str, Any]:





						"""simple docstring"""






						snake_case        =     simple_accuracy(_UpperCamelCase	,     _UpperCamelCase   )
						snake_case        =     float(fa_score(y_true=_UpperCamelCase	,     y_pred=_UpperCamelCase   )   )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int   )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     float(pearsonr(_UpperCamelCase	,     _UpperCamelCase   )[0]   )
						snake_case        =     float(spearmanr(_UpperCamelCase	,     _UpperCamelCase   )[0]   )
						return {
						    "pearson": pearson_corr,
						    "spearmanr": spearman_corr,
						}



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



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""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"]'      )



"""simple docstring"""
import os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


