





"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
       from PIL import Image
else:

       class 		lowercase					:





             @staticmethod
             def     _snake_case			(       *lowercase      ,						**lowercase )		->     Union[str, Any]:
                   pass


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Image			):







      '''simple docstring'''




      lowerCAmelCase  						=       hashlib.mda(image.tobytes()			)
      return m.hexdigest()[:10]


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Image			):







      '''simple docstring'''




      lowerCAmelCase  						=       np.array(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       npimg.shape
      return {"hash": hashimage(SCREAMING_SNAKE_CASE			), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 		lowercase					(					unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      dict(
          (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()					) if MODEL_FOR_MASK_GENERATION_MAPPING else [])					)
      _SCREAMING_SNAKE_CASE					=      dict(
          (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()					) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])					)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase )		->     Optional[Any]:
            lowerCAmelCase  						=       MaskGenerationPipeline(model=lowercase      ,						image_processor=lowercase )
            return image_segmenter, [
                "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "./tests/fixtures/tests_samples/COCO/000000039769.png",
            ]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     List[Any]:
            pass





      @require_tf
      @unittest.skip("""Image segmentation not implemented in TF""" )
      def     _snake_case			(       self )		->     Dict:
            pass





      @slow
      @require_torch
      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       pipeline("""mask-generation"""      ,						model="""facebook/sam-vit-huge""" )

            lowerCAmelCase  						=       image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""      ,						points_per_batch=256 )

            # Shortening by hashing
            lowerCAmelCase  						=       []
            for i, o in enumerate(outputs["""masks"""] ):
                  new_outupt += [{"mask": mask_to_test_readable(lowercase ), "scores": outputs["scores"][i]}]

            # fmt: off
            self.assertEqual(
                nested_simplify(lowercase      ,						decimals=4 )      ,						[
                    {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0_444},
                    {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
                    {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_167},
                    {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_132},
                    {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_053},
                    {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9_967},
                    {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
                    {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9_909},
                    {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9_879},
                    {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9_834},
                    {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9_716},
                    {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9_612},
                    {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9_599},
                    {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9_552},
                    {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9_532},
                    {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9_516},
                    {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9_499},
                    {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9_483},
                    {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9_464},
                    {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
                    {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
                    {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9_408},
                    {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9_335},
                    {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9_326},
                    {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9_262},
                    {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8_999},
                    {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8_986},
                    {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8_984},
                    {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8_873},
                    {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8_871}
                ]      ,						)
            # fmt: on





      @require_torch
      @slow
      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       """facebook/sam-vit-huge"""
            lowerCAmelCase  						=       pipeline("""mask-generation"""      ,						model=lowercase )

            lowerCAmelCase  						=       image_segmenter(
                """http://images.cocodataset.org/val2017/000000039769.jpg"""      ,						pred_iou_thresh=1      ,						points_per_batch=256 )

            # Shortening by hashing
            lowerCAmelCase  						=       []
            for i, o in enumerate(outputs["""masks"""] ):
                  new_outupt += [{"mask": mask_to_test_readable(lowercase ), "scores": outputs["scores"][i]}]

            self.assertEqual(
                nested_simplify(lowercase      ,						decimals=4 )      ,						[
                    {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0_444},
                    {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_210},
                    {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_167},
                    {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_132},
                    {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_053},
                ]      ,						)













"""simple docstring"""





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

import datasets


SCREAMING_SNAKE_CASE__					=   "\\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"

SCREAMING_SNAKE_CASE__					=   "\\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"

SCREAMING_SNAKE_CASE__					=   "\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__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Any			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						Tuple			):







      '''simple docstring'''




      lowerCAmelCase  						=       float(pearsonr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)[0]			)
      lowerCAmelCase  						=       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 		lowercase					(					datasets.Metric					):





      def     _snake_case			(       self )		->     List[str]:
            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     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Any:
            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\"]""" )






