

"""simple docstring"""





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

import datasets


__UpperCamelCase    : List[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}
}
'''

__UpperCamelCase    : Union[str, Any]				 =						'''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

__UpperCamelCase    : Union[str, Any]				 =						'''
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     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	return float((preds == labels).mean()					)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		int    							=      simple_accuracy(A_		,	A_					)
	lowerCAmelCase__      :		List[str]    							=      float(fa_score(y_true=A_		,	y_pred=A_					)					)
	return {
	    "accuracy": acc,
	    "f1": fa,
	}

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Union[str, Any]    							=      float(pearsonr(A_		,	A_					)[0]					)
	lowerCAmelCase__      :		str    							=      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 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						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 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[Any]  ,lowercase_     :							Tuple						):
						if self.config_name == "cola":
							return {"matthews_correlation": matthews_corrcoef(lowercase_  ,lowercase_						)}
						elif self.config_name == "stsb":
							return pearson_and_spearman(lowercase_  ,lowercase_						)
						elif self.config_name in ["mrpc", "qqp"]:
							return acc_and_fa(lowercase_  ,lowercase_						)
						elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
							return {"accuracy": simple_accuracy(lowercase_  ,lowercase_						)}
						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 unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:







							class 							SCREAMING_SNAKE_CASE   :






												"""simple docstring"""



												@staticmethod
												def 			__lowerCAmelCase					(			*lowercase_     :							Dict  ,**lowercase_     :							str						):
													pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			MODEL_FOR_OBJECT_DETECTION_MAPPING







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							int  ,lowercase_     :							str  ,lowercase_     :							List[str]						):
						lowerCAmelCase__      :		int    							=      ObjectDetectionPipeline(model=lowercase_  ,image_processor=lowercase_						)
						return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							str  ,lowercase_     :							str						):
						lowerCAmelCase__      :		List[str]    							=      object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  ,threshold=0.0						)

						self.assertGreater(len(lowercase_						)  ,0						)
						for detected_object in outputs:
							self.assertEqual(
							    lowercase_  ,{
							        '''score''': ANY(lowercase_						),
							        '''label''': ANY(lowercase_						),
							        '''box''': {'''xmin''': ANY(lowercase_						), '''ymin''': ANY(lowercase_						), '''xmax''': ANY(lowercase_						), '''ymax''': ANY(lowercase_						)},
							    }  ,)

						import datasets

						lowerCAmelCase__      :		str    							=      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''  ,'''image'''  ,split='''test'''						)

						lowerCAmelCase__      :		Dict    							=      [
						    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						),
						    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    # RGBA
						    dataset[0]['''file'''],
						    # LA
						    dataset[1]['''file'''],
						    # L
						    dataset[2]['''file'''],
						]
						lowerCAmelCase__      :		Optional[Any]    							=      object_detector(lowercase_  ,threshold=0.0						)

						self.assertEqual(len(lowercase_						)  ,len(lowercase_						)						)
						for outputs in batch_outputs:
							self.assertGreater(len(lowercase_						)  ,0						)
							for detected_object in outputs:
								self.assertEqual(
								    lowercase_  ,{
								        '''score''': ANY(lowercase_						),
								        '''label''': ANY(lowercase_						),
								        '''box''': {'''xmin''': ANY(lowercase_						), '''ymin''': ANY(lowercase_						), '''xmax''': ANY(lowercase_						), '''ymax''': ANY(lowercase_						)},
								    }  ,)







					@require_tf
					@unittest.skip('''Object detection not implemented in TF'''						)
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						pass







					@require_torch
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''hf-internal-testing/tiny-detr-mobilenetsv3'''

						lowerCAmelCase__      :		Union[str, Any]    							=      AutoModelForObjectDetection.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Any    							=      AutoFeatureExtractor.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      ObjectDetectionPipeline(model=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		Any    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,threshold=0.0						)

						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						    ]  ,)

						lowerCAmelCase__      :		List[Any]    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]  ,threshold=0.0  ,)

						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        ],
						        [
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		Any    							=      AutoModelForObjectDetection.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      AutoFeatureExtractor.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Any    							=      ObjectDetectionPipeline(model=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)

						lowerCAmelCase__      :		List[Any]    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Tuple    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		Tuple    							=      pipeline('''object-detection'''  ,model=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)

						lowerCAmelCase__      :		Any    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      0.9985
						lowerCAmelCase__      :		Dict    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		List[Any]    							=      pipeline('''object-detection'''  ,model=lowercase_						)

						lowerCAmelCase__      :		int    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,threshold=lowercase_						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)







					@require_torch
					@require_pytesseract
					@slow
					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''Narsil/layoutlmv3-finetuned-funsd'''
						lowerCAmelCase__      :		List[Any]    							=      0.9993

						lowerCAmelCase__      :		Any    							=      pipeline('''object-detection'''  ,model=lowercase_  ,threshold=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      object_detector(
						    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
						        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
						    ]  ,)




