
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 __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      tempfile.mkdtemp()

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

           lowercase							:     Dict       	=      {
               """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],
           }
           lowercase							:     Dict       	=      os.path.join(self.tmpdirname		,snake_case      )
           with open(self.image_processor_file		,"""w"""		,encoding="""utf-8"""      ) as fp:
                  json.dump(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           return BertTokenizer.from_pretrained(self.tmpdirname		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           return ViTImageProcessor.from_pretrained(self.tmpdirname		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      [np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta      )]

           lowercase							:     Optional[Any]       	=      [Image.fromarray(np.moveaxis(snake_case		,0		,-1      )      ) for x in image_inputs]

           return image_inputs
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.get_tokenizer()
           lowercase							:     Optional[int]       	=      self.get_image_processor()

           lowercase							:     Any       	=      VisionTextDualEncoderProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           processor.save_pretrained(self.tmpdirname      )
           lowercase							:     Optional[int]       	=      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		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      VisionTextDualEncoderProcessor(
               tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()      )
           processor.save_pretrained(self.tmpdirname      )

           lowercase							:     Tuple       	=      self.get_tokenizer(bos_token="""(BOS)"""		,eos_token="""(EOS)"""      )
           lowercase							:     Tuple       	=      self.get_image_processor(do_normalize=snake_case		,padding_value=1.0      )

           lowercase							:     Dict       	=      VisionTextDualEncoderProcessor.from_pretrained(
               self.tmpdirname		,bos_token="""(BOS)"""		,eos_token="""(EOS)"""		,do_normalize=snake_case		,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		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.get_image_processor()
           lowercase							:     List[str]       	=      self.get_tokenizer()

           lowercase							:     Union[str, Any]       	=      VisionTextDualEncoderProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     Optional[int]       	=      self.prepare_image_inputs()

           lowercase							:     int       	=      image_processor(snake_case		,return_tensors="""np"""      )
           lowercase							:     Dict       	=      processor(images=snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.get_image_processor()
           lowercase							:     str       	=      self.get_tokenizer()

           lowercase							:     str       	=      VisionTextDualEncoderProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     int       	=      """lower newer"""

           lowercase							:     Tuple       	=      processor(text=snake_case      )

           lowercase							:     Optional[int]       	=      tokenizer(snake_case      )

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



           '''simple docstring'''







           lowercase							:     str       	=      self.get_image_processor()
           lowercase							:     int       	=      self.get_tokenizer()

           lowercase							:     List[str]       	=      VisionTextDualEncoderProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     str       	=      """lower newer"""
           lowercase							:     Tuple       	=      self.prepare_image_inputs()

           lowercase							:     Optional[int]       	=      processor(text=snake_case		,images=snake_case      )

           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(snake_case      ):
                  processor()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.get_image_processor()
           lowercase							:     Optional[int]       	=      self.get_tokenizer()

           lowercase							:     Union[str, Any]       	=      VisionTextDualEncoderProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     Optional[int]       	=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:     List[str]       	=      processor.batch_decode(snake_case      )
           lowercase							:     str       	=      tokenizer.batch_decode(snake_case      )

           self.assertListEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.get_image_processor()
           lowercase							:     Optional[Any]       	=      self.get_tokenizer()

           lowercase							:     Any       	=      VisionTextDualEncoderProcessor(tokenizer=snake_case		,image_processor=snake_case      )

           lowercase							:     int       	=      """lower newer"""
           lowercase							:     int       	=      self.prepare_image_inputs()

           lowercase							:     Optional[int]       	=      processor(text=snake_case		,images=snake_case      )

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


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

import datasets


lowercase  :					str						    =							"""\
@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}
}
"""

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

lowercase  :					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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return float((preds == labels).mean()				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Any       	=      simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      float(fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      float(pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       lowercase							:     Dict       	=      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 __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







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