
import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Dict			  =       ProphetNetTokenizer
		__lowercase : Tuple			  =       False



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				super().setUp()

				UpperCamelCase						=  [
				    '[UNK]',
				    '[CLS]',
				    '[SEP]',
				    '[PAD]',
				    '[MASK]',
				    'want',
				    '##want',
				    '##ed',
				    'wa',
				    'un',
				    'runn',
				    '##ing',
				    ',',
				    'low',
				    'lowest',
				]
				UpperCamelCase						=  os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file'] )
				with open(self.vocab_file       ,   'w'       ,   encoding='utf-8' ) as vocab_writer:
						vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )



		def __UpperCamelCase				(				self       ,   A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  'UNwant\u00E9d,running'
				UpperCamelCase						=  'unwanted, running'
				return input_text, output_text



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.tokenizer_class(self.vocab_file )

				UpperCamelCase						=  tokenizer.tokenize('UNwant\u00E9d,running' )
				self.assertListEqual(A_       ,   ['un', '##want', '##ed', ',', 'runn', '##ing'] )
				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )       ,   [9, 6, 7, 12, 10, 11] )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer()

				self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' )       ,   ['ah', '\u535A', '\u63A8', 'zz'] )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer(do_lower_case=A_ )

				self.assertListEqual(
				    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ' )       ,   ['hello', '!', 'how', 'are', 'you', '?'] )
				self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )       ,   ['hello'] )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer(do_lower_case=A_       ,   strip_accents=A_ )

				self.assertListEqual(
				    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )       ,   ['hällo', '!', 'how', 'are', 'you', '?'] )
				self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )       ,   ['h\u00E9llo'] )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer(do_lower_case=A_       ,   strip_accents=A_ )

				self.assertListEqual(
				    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )       ,   ['hallo', '!', 'how', 'are', 'you', '?'] )
				self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )       ,   ['hello'] )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer(do_lower_case=A_ )

				self.assertListEqual(
				    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )       ,   ['hallo', '!', 'how', 'are', 'you', '?'] )
				self.assertListEqual(tokenizer.tokenize('H\u00E9llo' )       ,   ['hello'] )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer(do_lower_case=A_ )

				self.assertListEqual(
				    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ' )       ,   ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer(do_lower_case=A_       ,   strip_accents=A_ )

				self.assertListEqual(
				    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )       ,   ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer(do_lower_case=A_       ,   strip_accents=A_ )

				self.assertListEqual(
				    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ' )       ,   ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  BasicTokenizer(do_lower_case=A_       ,   never_split=['[UNK]'] )

				self.assertListEqual(
				    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]' )       ,   ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

				UpperCamelCase						=  {}
				for i, token in enumerate(A_ ):
						UpperCamelCase						=  i
				UpperCamelCase						=  WordpieceTokenizer(vocab=A_       ,   unk_token='[UNK]' )

				self.assertListEqual(tokenizer.tokenize('' )       ,   [] )

				self.assertListEqual(tokenizer.tokenize('unwanted running' )       ,   ['un', '##want', '##ed', 'runn', '##ing'] )

				self.assertListEqual(tokenizer.tokenize('unwantedX running' )       ,   ['[UNK]', 'runn', '##ing'] )



		@require_torch
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )

				UpperCamelCase						=  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
				UpperCamelCase						=  [1_037, 2_146, 20_423, 2_005, 7_680, 7_849, 3_989, 1_012, 102]
				UpperCamelCase						=  tokenizer(A_       ,   padding=A_       ,   return_tensors='pt' )
				self.assertIsInstance(A_       ,   A_ )
				UpperCamelCase						=  list(batch.input_ids.numpy()[0] )
				self.assertListEqual(A_       ,   A_ )

				self.assertEqual((2, 9)       ,   batch.input_ids.shape )
				self.assertEqual((2, 9)       ,   batch.attention_mask.shape )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				self.assertTrue(_is_whitespace(' ' ) )
				self.assertTrue(_is_whitespace('\t' ) )
				self.assertTrue(_is_whitespace('\r' ) )
				self.assertTrue(_is_whitespace('\n' ) )
				self.assertTrue(_is_whitespace('\u00A0' ) )

				self.assertFalse(_is_whitespace('A' ) )
				self.assertFalse(_is_whitespace('-' ) )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				self.assertTrue(_is_control('\u0005' ) )

				self.assertFalse(_is_control('A' ) )
				self.assertFalse(_is_control(' ' ) )
				self.assertFalse(_is_control('\t' ) )
				self.assertFalse(_is_control('\r' ) )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				self.assertTrue(_is_punctuation('-' ) )
				self.assertTrue(_is_punctuation('$' ) )
				self.assertTrue(_is_punctuation('`' ) )
				self.assertTrue(_is_punctuation('.' ) )

				self.assertFalse(_is_punctuation('A' ) )
				self.assertFalse(_is_punctuation(' ' ) )



		@slow
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )

				UpperCamelCase						=  tokenizer.encode('sequence builders'       ,   add_special_tokens=A_ )
				UpperCamelCase						=  tokenizer.encode('multi-sequence build'       ,   add_special_tokens=A_ )

				UpperCamelCase						=  tokenizer.build_inputs_with_special_tokens(A_ )
				UpperCamelCase						=  tokenizer.build_inputs_with_special_tokens(A_       ,   A_ )

				assert encoded_sentence == text + [102]
				assert encoded_pair == text + [102] + text_a + [102]


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

import datasets


_UpperCAmelCase      :				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"

_UpperCAmelCase      :				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"

_UpperCAmelCase      :				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    A (      lowercase					, lowercase				)     ->						List[str]:
		'''simple docstring'''

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


def    A (      lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

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


def    A (      lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

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


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



		def __UpperCamelCase				(				self )						->      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       ,   A_       ,   A_ )						->      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"]' )
