


from scipy.stats import spearmanr

import datasets


A			:						int        =  '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

A			:						Any        =  '\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

A			:						Union[str, Any]        =  r'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {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, {\.I}lhan 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, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION				)
class 				A       (			datasets.Metric				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[str]						)		->  int:




							"""simple docstring"""


							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.spearmanr.html"""] ,   )


					def     lowerCamelCase__	(self       :		Tuple ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[str]=False						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						spearmanr(_UpperCAmelCase ,   _UpperCAmelCase						)
							if return_pvalue:
									return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
							else:
									return {"spearmanr": results[0]}




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=1_3			,				_a=3			,				_a=True			,				_a=True			,				_a=0.1			,				_a=0.1			,				_a=2_2_4			,				_a=1_0_0_0			,				_a=[3, 3, 6, 4]			,				_a=[4_8, 5_6, 1_1_2, 2_2_0]			,				)       ->       Tuple:
												_a      :       Dict   =						parent
												_a      :       Optional[int]   =						batch_size
												_a      :       Optional[Any]   =						num_channels
												_a      :       Union[str, Any]   =						is_training
												_a      :       Tuple   =						use_labels
												_a      :       Dict   =						hidden_dropout_prob
												_a      :       List[Any]   =						attention_probs_dropout_prob
												_a      :       Dict   =						num_labels
												_a      :       List[str]   =						image_size
												_a      :       Dict   =						layer_depths
												_a      :       str   =						embed_dims


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

												_a      :       int   =						None
												if self.use_labels:
																	_a      :       Optional[Any]   =						ids_tensor([self.batch_size]			,				self.num_labels			)

												_a      :       Dict   =						self.get_config()

												return config, pixel_values, labels


							def 							__lowercase							(     self			)       ->       int:
												return SwiftFormerConfig(
												    depths=self.layer_depths			,				embed_dims=self.embed_dims			,				mlp_ratio=4			,				downsamples=[True, True, True, True]			,				hidden_act='''gelu'''			,				num_labels=self.num_labels			,				down_patch_size=3			,				down_stride=2			,				down_pad=1			,				drop_rate=0.0			,				drop_path_rate=0.0			,				use_layer_scale=_a			,				layer_scale_init_value=1e-5			,				)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       str:
												_a      :       List[Any]   =						SwiftFormerModel(config=_a			)
												model.to(_a			)
												model.eval()
												_a      :       Optional[int]   =						model(_a			)
												self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.embed_dims[-1], 7, 7)			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       Optional[Any]:
												_a      :       List[str]   =						self.num_labels
												_a      :       Optional[int]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()
												_a      :       List[str]   =						model(_a			,				labels=_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)

												_a      :       Union[str, Any]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()

												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
												_a      :       Optional[Any]   =						model(_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)




							def 							__lowercase							(     self			)       ->       Tuple:
												((_a) ,   (_a) ,   (_a))      :       Optional[int]   =						self.prepare_config_and_inputs()
												_a      :       List[Any]   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							UpperCAmelCase__			:		Optional[int]             =       (
							    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		Optional[Any]             =       False
							UpperCAmelCase__			:		str             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		str             =       False


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Union[str, Any]   =						SwiftFormerModelTester(self			)
												_a      :       int   =						ConfigTester(
												    self			,				config_class=_a			,				has_text_modality=_a			,				hidden_size=3_7			,				num_attention_heads=1_2			,				num_hidden_layers=1_2			,				)


							def 							__lowercase							(     self			)       ->       int:
												self.config_tester.run_common_tests()


							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							def 							__lowercase							(     self			)       ->       Dict:
												_a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       Any   =						model_class(_a			)
																	_a      :       int   =						model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_a			,				nn.Linear			)			)


							def 							__lowercase							(     self			)       ->       str:
												_a ,   _a      :       str   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       Optional[int]   =						model_class(_a			)
																	_a      :       Tuple   =						inspect.signature(model.forward			)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	_a      :       Tuple   =						[*signature.parameters.keys()]

																	_a      :       List[str]   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				_a			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       int   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a			)


							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Any   =						SwiftFormerModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)


							@unittest.skip(reason='''SwiftFormer does not output attentions'''			)
							def 							__lowercase							(     self			)       ->       List[Any]:
												pass


							def 							__lowercase							(     self			)       ->       int:
												def check_hidden_states_output(_a			,				_a			,				_a			):
																	_a      :       Optional[int]   =						model_class(_a			)
																	model.to(_a			)
																	model.eval()

																	with torch.no_grad():
																						_a      :       Union[str, Any]   =						model(**self._prepare_for_class(_a			,				_a			)			)

																	_a      :       Optional[Any]   =						outputs.hidden_states

																	_a      :       Union[str, Any]   =						8
																	self.assertEqual(len(_a			)			,				_a			)  # TODO

																	# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																	# with the width and height being successively divided by 2, after every 2 blocks
																	for i in range(len(_a			)			):
																						self.assertEqual(
																						    hidden_states[i].shape			,				torch.Size(
																						        [
																						            self.model_tester.batch_size,
																						            self.model_tester.embed_dims[i // 2],
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						        ]			)			,				)

												_a ,   _a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       str   =						True
																	check_hidden_states_output(_a			,				_a			,				_a			)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	_a      :       List[str]   =						True

																	check_hidden_states_output(_a			,				_a			,				_a			)


							def 							__lowercase							(     self			)       ->       str:
												def _config_zero_init(_a			):
																	_a      :       List[Any]   =						copy.deepcopy(_a			)
																	for key in configs_no_init.__dict__.keys():
																						if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																											setattr(_a			,				_a			,				1e-1_0			)
																						if isinstance(getattr(_a			,				_a			,				_a			)			,				_a			):
																											_a      :       int   =						_config_zero_init(getattr(_a			,				_a			)			)
																											setattr(_a			,				_a			,				_a			)
																	return configs_no_init

												_a ,   _a      :       Any   =						self.model_tester.prepare_config_and_inputs_for_common()

												_a      :       Dict   =						_config_zero_init(_a			)
												for model_class in self.all_model_classes:
																	_a      :       Dict   =						model_class(config=_a			)
																	for name, param in model.named_parameters():
																						if param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1e9) / 1e9).round().item()			,				[0.0, 1.0]			,				msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""			,				)




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



def   __UpperCAmelCase					(      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image







@require_torch
@require_vision
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@cached_property
							def 							__lowercase							(     self			)       ->       str:
												return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''			) if is_vision_available() else None




							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Any   =						SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''			).to(_a			)

												_a      :       Any   =						self.default_image_processor
												_a      :       Any   =						prepare_img()
												_a      :       Any   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

												# forward pass
												with torch.no_grad():
																	_a      :       Optional[Any]   =						model(**_a			)

												# verify the logits
												_a      :       List[str]   =						torch.Size((1, 1_0_0_0)			)
												self.assertEqual(outputs.logits.shape			,				_a			)

												_a      :       int   =						torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]			).to(_a			)
												self.assertTrue(torch.allclose(outputs.logits[0, :3]			,				_a			,				atol=1e-4			)			)



