





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

import datasets


A_												=     "\\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"

A_												=     "\\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"

A_												=     "\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    __UpperCamelCase      (    a,     a) ->Tuple:
					return float((preds == labels).mean())





def    __UpperCamelCase      (    a,     a) ->Optional[Any]:
					lowerCamelCase__       =       simple_accuracy(a,     a)
					lowerCamelCase__       =       float(fa_score(y_true=a,     y_pred=a))
					return {
					    "accuracy": acc,
					    "f1": fa,
					}





def    __UpperCamelCase      (    a,     a) ->str:
					lowerCamelCase__       =       float(pearsonr(a,     a)[0])
					lowerCamelCase__       =       float(spearmanr(a,     a)[0])
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					}







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





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										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\"]"	)








import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       tempfile.mkdtemp()

										# fmt: off
										lowerCamelCase__       =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
										# fmt: on
										lowerCamelCase__       =       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]	)	)

										lowerCamelCase__       =       {
										    "do_resize": True,
										    "size": {"height": 18, "width": 18},
										    "do_normalize": True,
										    "image_mean": [0.5, 0.5, 0.5],
										    "image_std": [0.5, 0.5, 0.5],
										}
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  _lowerCAmelCase	)
										with open(self.image_processor_file	,  "w"	,  encoding="utf-8"	) as fp:
															json.dump(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return BertTokenizer.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										shutil.rmtree(self.tmpdirname	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       [np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta	)]

										lowerCamelCase__       =       [Image.fromarray(np.moveaxis(_lowerCAmelCase	,  0	,  -1	)	) for x in image_inputs]

										return image_inputs





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										processor.save_pretrained(self.tmpdirname	)
										lowerCamelCase__       =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

										self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer.get_vocab()	)
										self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast)	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       VisionTextDualEncoderProcessor(
										    tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()	)
										processor.save_pretrained(self.tmpdirname	)

										lowerCamelCase__       =       self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"	)
										lowerCamelCase__       =       self.get_image_processor(do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										lowerCamelCase__       =       VisionTextDualEncoderProcessor.from_pretrained(
										    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()	)
										self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast)	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       image_processor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       processor(images=_lowerCAmelCase	,  return_tensors="np"	)

										for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1E-2	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	)

										for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

										# test if it raises when no input is passed
										with self.assertRaises(_lowerCAmelCase	):
															processor()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCamelCase__       =       processor.batch_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	)

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  processor.model_input_names	)

