




import unittest

import numpy as np

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 EfficientNetImageProcessor




class     a__ (      unittest.TestCase   ):







					def __init__(			self						,	A						,	A=13						,	A=3						,	A=18						,	A=30						,	A=400						,	A=True						,	A=None						,	A=True						,	A=[0.5, 0.5, 0.5]						,	A=[0.5, 0.5, 0.5]						,	)						->					Any:



										'''simple docstring'''



										a											=   size if size is not None else {"height": 18, "width": 18}
										a											=   parent
										a											=   batch_size
										a											=   num_channels
										a											=   image_size
										a											=   min_resolution
										a											=   max_resolution
										a											=   do_resize
										a											=   size
										a											=   do_normalize
										a											=   image_mean
										a											=   image_std







					def     lowerCAmelCase_       (			self				)						->					int:



										'''simple docstring'''



										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}







@require_torch
@require_vision
class     a__ (      UpperCamelCase__     ,      unittest.TestCase   ):
					a	:    int	      =			EfficientNetImageProcessor if is_vision_available() else None







					def     lowerCAmelCase_       (			self				)						->					List[str]:



										'''simple docstring'''



										a											=   EfficientNetImageProcessorTester(self				)







					@property
					def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



										'''simple docstring'''



										return self.image_processor_tester.prepare_image_processor_dict()







					def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



										'''simple docstring'''



										a											=   self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(A						,	"image_mean"				)				)
										self.assertTrue(hasattr(A						,	"image_std"				)				)
										self.assertTrue(hasattr(A						,	"do_normalize"				)				)
										self.assertTrue(hasattr(A						,	"do_resize"				)				)
										self.assertTrue(hasattr(A						,	"size"				)				)







					def     lowerCAmelCase_       (			self				)						->					Any:



										'''simple docstring'''



										a											=   self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size						,	{"height": 18, "width": 18}				)

										a											=   self.image_processing_class.from_dict(self.image_processor_dict						,	size=42				)
										self.assertEqual(image_processor.size						,	{"height": 42, "width": 42}				)







					def     lowerCAmelCase_       (			self				)						->					List[str]:



										'''simple docstring'''



										a											=   self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										a											=   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											=   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											=   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     lowerCAmelCase_       (			self				)						->					Tuple:



										'''simple docstring'''



										a											=   self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										a											=   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											=   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											=   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     lowerCAmelCase_       (			self				)						->					int:



										'''simple docstring'''



										a											=   self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										a											=   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											=   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											=   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"],
										    )						,	)







from sklearn.metrics import mean_absolute_error

import datasets


lowercase__   :    int 					=      "\\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"

lowercase__   :    str 					=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


lowercase__   :    Tuple 					=      "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"




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







     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



          return datasets.MetricInfo(
              description=_DESCRIPTION						,	citation=_CITATION						,	inputs_description=_KWARGS_DESCRIPTION						,	features=datasets.Features(self._get_feature_types()				)						,	reference_urls=[
                  "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
              ]						,	)







     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value("float"				)				),
                   "references": datasets.Sequence(datasets.Value("float"				)				),
               }
          else:
               return {
                   "predictions": datasets.Value("float"				),
                   "references": datasets.Value("float"				),
               }







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A=None						,	A="uniform_average"				)						->					List[Any]:



          '''simple docstring'''



          a											=   mean_absolute_error(A						,	A						,	sample_weight=A						,	multioutput=A				)

          return {"mae": mae_score}

