from sklearn.metrics import accuracy_score

import datasets


_SCREAMING_SNAKE_CASE     =     '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


_SCREAMING_SNAKE_CASE     =     '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


_SCREAMING_SNAKE_CASE     =     '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'







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




    def     __lowerCamelCase			(       self	: str )						->			str:
      return datasets.MetricInfo(
          description=_DESCRIPTION   ,   citation=_CITATION   ,   inputs_description=_KWARGS_DESCRIPTION   ,   features=datasets.Features(
              {
                  "predictions": datasets.Sequence(datasets.Value("int32" ) ),
                  "references": datasets.Sequence(datasets.Value("int32" ) ),
              }
              if self.config_name == "multilabel"
              else {
                  "predictions": datasets.Value("int32" ),
                  "references": datasets.Value("int32" ),
              } )   ,   reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]   ,   )




    def     __lowerCamelCase			(       self	: Tuple   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: Optional[Any]=True   ,   lowerCAmelCase	: List[Any]=None )						->			Union[str, Any]:
      return {
          "accuracy": float(
              accuracy_score(_A   ,   _A   ,   normalize=_A   ,   sample_weight=_A ) )
      }






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor




class 				lowerCAmelCase__	(						unittest.TestCase    ):







					def __init__(							self   :  int      ,		_A   :  Optional[Any]      ,		_A   :  Optional[int]=7      ,		_A   :  str=3      ,		_A   :  Optional[int]=30      ,		_A   :  List[str]=400      ,		_A   :  Tuple=True      ,		_A   :  List[Any]=None      ,		_A   :  Dict=True      ,		_A   :  Tuple=[0.5, 0.5, 0.5]      ,		_A   :  List[str]=[0.5, 0.5, 0.5]      ,		_A   :  int=False      ,		):
											A__						:				Tuple         =			size if size is not None else {"height": 30, "width": 30}
											A__						:				Tuple         =			parent
											A__						:				str         =			batch_size
											A__						:				int         =			num_channels
											A__						:				Union[str, Any]         =			min_resolution
											A__						:				List[str]         =			max_resolution
											A__						:				int         =			do_resize
											A__						:				Tuple         =			size
											A__						:				Union[str, Any]         =			do_normalize
											A__						:				Optional[int]         =			image_mean
											A__						:				List[Any]         =			image_std
											A__						:				Dict         =			do_reduce_labels







					def 			_lowercase      (							self   :  Optional[int]):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_reduce_labels": self.do_reduce_labels,
											}




def 						snake_case__							(       )		->		Tuple:







						"""simple docstring"""

						A__						:				Union[str, Any]         =			load_dataset("hf-internal-testing/fixtures_ade20k"				,  split="test"							)

						A__						:				Any         =			Image.open(dataset[0]["file"]							)
						A__						:				Any         =			Image.open(dataset[1]["file"]							)

						return image, map
def 						snake_case__							(       )		->		List[str]:







						"""simple docstring"""

						A__						:				Tuple         =			load_dataset("hf-internal-testing/fixtures_ade20k"				,  split="test"							)

						A__						:				Tuple         =			Image.open(dataset[0]["file"]							)
						A__						:				int         =			Image.open(dataset[1]["file"]							)
						A__						:				str         =			Image.open(dataset[2]["file"]							)
						A__						:				List[Any]         =			Image.open(dataset[3]["file"]							)

						return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     int     =    SegformerImageProcessor if is_vision_available() else None







					def 			_lowercase      (							self   :  int):
											A__						:				int         =			SegformerImageProcessingTester(self)







					@property
					def 			_lowercase      (							self   :  str):
											return self.image_processor_tester.prepare_image_processor_dict()







					def 			_lowercase      (							self   :  Optional[Any]):
											A__						:				Any         =			self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(_A      ,		"do_resize"))
											self.assertTrue(hasattr(_A      ,		"size"))
											self.assertTrue(hasattr(_A      ,		"do_normalize"))
											self.assertTrue(hasattr(_A      ,		"image_mean"))
											self.assertTrue(hasattr(_A      ,		"image_std"))
											self.assertTrue(hasattr(_A      ,		"do_reduce_labels"))







					def 			_lowercase      (							self   :  Tuple):
											A__						:				Union[str, Any]         =			self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size      ,		{"height": 30, "width": 30})
											self.assertEqual(image_processor.do_reduce_labels      ,		_A)

											A__						:				Union[str, Any]         =			self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42      ,		reduce_labels=_A)
											self.assertEqual(image_processor.size      ,		{"height": 42, "width": 42})
											self.assertEqual(image_processor.do_reduce_labels      ,		_A)







					def 			_lowercase      (							self   :  Union[str, Any]):
											pass







					def 			_lowercase      (							self   :  Dict):
											# Initialize image_processing
											A__						:				Tuple         =			self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											A__						:				Union[str, Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		Image.Image)

											# Test not batched input
											A__						:				Dict         =			image_processing(image_inputs[0]      ,		return_tensors="pt").pixel_values
											self.assertEqual(
											    encoded_images.shape      ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)

											# Test batched
											A__						:				List[Any]         =			image_processing(_A      ,		return_tensors="pt").pixel_values
											self.assertEqual(
											    encoded_images.shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)







					def 			_lowercase      (							self   :  Optional[int]):
											# Initialize image_processing
											A__						:				Tuple         =			self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											A__						:				Tuple         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		np.ndarray)

											# Test not batched input
											A__						:				Optional[Any]         =			image_processing(image_inputs[0]      ,		return_tensors="pt").pixel_values
											self.assertEqual(
											    encoded_images.shape      ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)

											# Test batched
											A__						:				Optional[int]         =			image_processing(_A      ,		return_tensors="pt").pixel_values
											self.assertEqual(
											    encoded_images.shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)







					def 			_lowercase      (							self   :  Tuple):
											# Initialize image_processing
											A__						:				str         =			self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__						:				str         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		torch.Tensor)

											# Test not batched input
											A__						:				int         =			image_processing(image_inputs[0]      ,		return_tensors="pt").pixel_values
											self.assertEqual(
											    encoded_images.shape      ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)

											# Test batched
											A__						:				List[str]         =			image_processing(_A      ,		return_tensors="pt").pixel_values
											self.assertEqual(
											    encoded_images.shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)







					def 			_lowercase      (							self   :  Optional[int]):
											# Initialize image_processing
											A__						:				str         =			self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__						:				Optional[Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A)
											A__						:				List[str]         =			[]
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		torch.Tensor)
																	maps.append(torch.zeros(image.shape[-2:]).long())

											# Test not batched input
											A__						:				Any         =			image_processing(image_inputs[0]      ,		maps[0]      ,		return_tensors="pt")
											self.assertEqual(
											    encoding["pixel_values"].shape      ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(
											    encoding["labels"].shape      ,		(
											        1,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(encoding["labels"].dtype      ,		torch.long)
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)

											# Test batched
											A__						:				int         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertEqual(
											    encoding["pixel_values"].shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(
											    encoding["labels"].shape      ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(encoding["labels"].dtype      ,		torch.long)
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)

											# Test not batched input (PIL images)
											A__     ,							A__						:				List[str]         =			prepare_semantic_single_inputs()

											A__						:				Any         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertEqual(
											    encoding["pixel_values"].shape      ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(
											    encoding["labels"].shape      ,		(
											        1,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(encoding["labels"].dtype      ,		torch.long)
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)

											# Test batched input (PIL images)
											A__     ,							A__						:				Union[str, Any]         =			prepare_semantic_batch_inputs()

											A__						:				str         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertEqual(
											    encoding["pixel_values"].shape      ,		(
											        2,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(
											    encoding["labels"].shape      ,		(
											        2,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )      ,		)
											self.assertEqual(encoding["labels"].dtype      ,		torch.long)
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)







					def 			_lowercase      (							self   :  Union[str, Any]):
											# Initialize image_processing
											A__						:				List[str]         =			self.image_processing_class(**self.image_processor_dict)

											# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
											A__     ,							A__						:				Tuple         =			prepare_semantic_single_inputs()
											A__						:				Dict         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 150)

											A__						:				str         =			True
											A__						:				int         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)