



"""simple docstring"""
from scipy.stats import pearsonr

import datasets


__a      =				"\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


__a      =				"\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


__a      =				"\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION				)
class 				lowerCamelCase  (			datasets.Metric				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Tuple:
											return datasets.MetricInfo(
											    description=_DESCRIPTION						,     citation=_CITATION						,     inputs_description=_KWARGS_DESCRIPTION						,     features=datasets.Features(
											        {
											            """predictions""": datasets.Value("""float"""						),
											            """references""": datasets.Value("""float"""						),
											        }						)						,     reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]						,     )






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Optional[int]						,     snake_case:							List[Any]						,     snake_case:							Optional[int]=False						)   ->	int:
											if return_pvalue:
																	snake_case_ :Union[str, Any]						=			pearsonr(snake_case						,     snake_case						)
																	return {"pearsonr": results[0], "p-value": results[1]}
											else:
																	return {"pearsonr": float(pearsonr(snake_case						,     snake_case						)[0]						)}







from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor

class 	A_       :
	def __init__(    self							,					_A							,					_A=1_3							,					_A=3_0							,					_A=2							,					_A=3							,					_A=True							,					_A=True							,					_A=3_2							,					_A=2							,					_A=4							,					_A=3_7							,					_A="gelu"							,					_A=0.1							,					_A=0.1							,					_A=1_0							,					_A=0.02							,					_A=3							,					_A=None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				image_size
				UpperCAmelCase   		=				patch_size
				UpperCAmelCase   		=				num_channels
				UpperCAmelCase   		=				is_training
				UpperCAmelCase   		=				use_labels
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				type_sequence_label_size
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				scope

				# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				UpperCAmelCase   		=				(image_size // patch_size) ** 2
				UpperCAmelCase   		=				num_patches + 1
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				UpperCAmelCase   		=				None
				if self.use_labels:
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)

				UpperCAmelCase   		=				self.get_config()

				return config, pixel_values, labels
	def      _lowercase				(    self		):






				'''simple docstring'''







				return ViTConfig(
				    image_size=self.image_size							,					patch_size=self.patch_size							,					num_channels=self.num_channels							,					hidden_size=self.hidden_size							,					num_hidden_layers=self.num_hidden_layers							,					num_attention_heads=self.num_attention_heads							,					intermediate_size=self.intermediate_size							,					hidden_act=self.hidden_act							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					is_decoder=_A							,					initializer_range=self.initializer_range							,					)
	def      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModel(config=_A		)
				UpperCAmelCase   		=				model(_A							,					training=_A		)
				self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)		)

				# Test with an image with different size than the one specified in config.
				UpperCAmelCase   		=				self.image_size // 2
				UpperCAmelCase   		=				pixel_values[:, :, :image_size, :image_size]
				UpperCAmelCase   		=				model(_A							,					interpolate_pos_encoding=_A							,					training=_A		)
				UpperCAmelCase   		=				(image_size // self.patch_size) ** 2 + 1
				self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, seq_length, self.hidden_size)		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.type_sequence_label_size
				UpperCAmelCase   		=				TFViTForImageClassification(_A		)
				UpperCAmelCase   		=				model(_A							,					labels=_A							,					training=_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)		)

				# Test with an image with different size than the one specified in config.
				UpperCAmelCase   		=				self.image_size // 2
				UpperCAmelCase   		=				pixel_values[:, :, :image_size, :image_size]
				UpperCAmelCase   		=				model(_A							,					interpolate_pos_encoding=_A							,					training=_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)		)

				# test greyscale images
				UpperCAmelCase   		=				1
				UpperCAmelCase   		=				TFViTForImageClassification(_A		)
				UpperCAmelCase   		=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
				UpperCAmelCase   		=				model(_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.prepare_config_and_inputs()
				UpperCAmelCase		,			UpperCAmelCase		,			UpperCAmelCase   		=				config_and_inputs
				UpperCAmelCase   		=				{'''pixel_values''': pixel_values}
				return config, inputs_dict

@require_tf
class 	A_       (a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
	UpperCAmelCase__					=			(
	    {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
	    if is_tf_available()
	    else {}
	)

	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModelTester(self		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A							,					has_text_modality=_A							,					hidden_size=3_7		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	@unittest.skip(reason='''ViT does not use inputs_embeds'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	@unittest.skip(reason='''ViT does not use inputs_embeds'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase   		=				model_class(_A		)
							self.assertIsInstance(model.get_input_embeddings()							,					(tf.keras.layers.Layer)		)
							UpperCAmelCase   		=				model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(_A							,					tf.keras.layers.Layer		)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase   		=				model_class(_A		)
							UpperCAmelCase   		=				inspect.signature(model.call		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							UpperCAmelCase   		=				[*signature.parameters.keys()]

							UpperCAmelCase   		=				['''pixel_values''']
							self.assertListEqual(arg_names[:1]							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_A		)







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModel.from_pretrained('''google/vit-base-patch16-224'''		)
				self.assertIsNotNone(_A		)





def 						__SCREAMING_SNAKE_CASE  (				)  ->				Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
			return image






@require_tf
@require_vision
class 	A_       (unittest.TestCase			):
	@cached_property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''		) if is_vision_available() else None







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''		)

				UpperCAmelCase   		=				self.default_image_processor
				UpperCAmelCase   		=				prepare_img()
				UpperCAmelCase   		=				image_processor(images=_A							,					return_tensors='''tf'''		)

				# forward pass
				UpperCAmelCase   		=				model(**_A		)

				# verify the logits
				UpperCAmelCase   		=				tf.TensorShape((1, 1_0_0_0)		)
				self.assertEqual(outputs.logits.shape							,					_A		)

				UpperCAmelCase   		=				tf.constant([-0.27_44, 0.82_15, -0.08_36]		)

				tf.debugging.assert_near(outputs.logits[0, :3]							,					_A							,					atol=1E-4		)



