






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

import datasets


snake_case_						:			Any													=							"\\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"

snake_case_						:			str													=							"\\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"

snake_case_						:			List[str]													=							"\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(   a__	,a__):
				return float((preds == labels).mean())







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =simple_accuracy(a__	,a__)
				_SCREAMING_SNAKE_CASE			  =float(fa_score(y_true=a__	,y_pred=a__))
				return {
				    "accuracy": acc,
				    "f1": fa,
				}







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =float(pearsonr(a__	,a__)[0])
				_SCREAMING_SNAKE_CASE			  =float(spearmanr(a__	,a__)[0])
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}


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




						def 							__UpperCamelCase				(       self					:      int  )							->     Optional[Any]:


										"""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					:      Union[str, Any]				,    _a					:      str				,    _a					:      List[Any]  )							->     Any:


										"""simple docstring"""





										if self.config_name == "cola":
														return {"matthews_correlation": matthews_corrcoef(A_				,    A_  )}
										elif self.config_name == "stsb":
														return pearson_and_spearman(A_				,    A_  )
										elif self.config_name in ["mrpc", "qqp"]:
														return acc_and_fa(A_				,    A_  )
										elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
														return {"accuracy": simple_accuracy(A_				,    A_  )}
										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"]'''  )



import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LEDTokenizer
						UpperCAmelCase             =						LEDTokenizerFast
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										super().setUp()
										_SCREAMING_SNAKE_CASE			  =[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										_SCREAMING_SNAKE_CASE			  =dict(zip(_a				,    range(len(_a  )  )  )  )
										_SCREAMING_SNAKE_CASE			  =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										_SCREAMING_SNAKE_CASE			  ={'''unk_token''': '''<unk>'''}

										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''merges_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as fp:
														fp.write(json.dumps(_a  ) + '''\n'''  )
										with open(self.merges_file				,    '''w'''				,    encoding='''utf-8'''  ) as fp:
														fp.write('''\n'''.join(_a  )  )




						def 							__UpperCamelCase				(       self					:      str				,    **_a					:      int  )							->     Optional[Any]:


										"""simple docstring"""





										kwargs.update(self.special_tokens_map  )
										return self.tokenizer_class.from_pretrained(self.tmpdirname				,    **_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    **_a					:      List[Any]  )							->     str:


										"""simple docstring"""





										kwargs.update(self.special_tokens_map  )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Any  )							->     Dict:


										"""simple docstring"""





										return "lower newer", "lower newer"




						@cached_property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''  )




						@cached_property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Any:


										"""simple docstring"""





										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    max_length=len(_a  )				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIsInstance(_a				,    _a  )

														self.assertEqual((2, 9)				,    batch.input_ids.shape  )
														self.assertEqual((2, 9)				,    batch.attention_mask.shape  )
														_SCREAMING_SNAKE_CASE			  =batch.input_ids.tolist()[0]
														self.assertListEqual(_a				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIn('''input_ids'''				,    _a  )
														self.assertIn('''attention_mask'''				,    _a  )
														self.assertNotIn('''labels'''				,    _a  )
														self.assertNotIn('''decoder_attention_mask'''				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =[
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    max_length=32				,    padding='''max_length'''				,    return_tensors='''pt'''  )
														self.assertEqual(32				,    targets['''input_ids'''].shape[1]  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Any  )							->     Union[str, Any]:


										"""simple docstring"""





										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(
														    ['''I am a small frog''' * 1024, '''I am a small frog''']				,    padding=_a				,    truncation=_a				,    return_tensors='''pt'''  )
														self.assertIsInstance(_a				,    _a  )
														self.assertEqual(batch.input_ids.shape				,    (2, 5122)  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =inputs['''input_ids''']
														_SCREAMING_SNAKE_CASE			  =targets['''input_ids''']
														self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
														self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
														self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
														self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     str:


										"""simple docstring"""





										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =['''Summary of the text.''', '''Another summary.''']
														_SCREAMING_SNAKE_CASE			  =[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a  )
														_SCREAMING_SNAKE_CASE			  =[[0] * len(_a  ) for x in encoded_output['''input_ids''']]
														_SCREAMING_SNAKE_CASE			  =tokenizer.pad(_a  )
														self.assertSequenceEqual(outputs['''global_attention_mask''']				,    _a  )




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


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
																		_SCREAMING_SNAKE_CASE			  =self.rust_tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  =self.tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  ='''A, <mask> AllenNLP sentence.'''
																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		self.assertEqual(sum(tokens_r['''token_type_ids''']  )				,    sum(tokens_p['''token_type_ids''']  )  )
																		self.assertEqual(
																		    sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )				,    sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )				,    )

																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )
																		self.assertSequenceEqual(tokens_p['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
																		self.assertSequenceEqual(tokens_r['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

																		self.assertSequenceEqual(
																		    _a				,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
																		self.assertSequenceEqual(
																		    _a				,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )