
'''simple docstring'''






from scipy.stats import spearmanr

import datasets


UpperCAmelCase_							: Any =							'\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'

UpperCAmelCase_							: Union[str, 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'

UpperCAmelCase_							: List[str] =							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 			UpperCAmelCase__    (							datasets.Metric			):



    def 	lowerCamelCase_	(						self   :							int  ):
          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   :							List[Any],__A   :							Optional[Any],__A   :							Tuple,__A   :							Tuple=False  ):
          _lowerCamelCase					: Any		    = spearmanr(__A,__A  )
          if return_pvalue:
                return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
          else:
                return {"spearmanr": results[0]}





'''simple docstring'''



import inspect
import unittest

from transformers import MobileViTConfig
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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
      from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileViTImageProcessor






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.config_class(**self.inputs_dict  )
             self.parent.assertTrue(hasattr(A,							'hidden_sizes'  )  )
             self.parent.assertTrue(hasattr(A,							'neck_hidden_sizes'  )  )
             self.parent.assertTrue(hasattr(A,							'num_attention_heads'  )  )






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A,							A=13,							A=32,							A=2,							A=3,							A=640,							A=4,							A="silu",							A=3,							A=32,							A=0.1,							A=0.1,							A=0.1,							A=0.02,							A=True,							A=True,							A=10,							A=None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						parent
             SCREAMING_SNAKE_CASE   :      int    					=						batch_size
             SCREAMING_SNAKE_CASE   :      int    					=						image_size
             SCREAMING_SNAKE_CASE   :      str    					=						patch_size
             SCREAMING_SNAKE_CASE   :      Tuple    					=						num_channels
             SCREAMING_SNAKE_CASE   :      int    					=						last_hidden_size
             SCREAMING_SNAKE_CASE   :      Any    					=						num_attention_heads
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						hidden_act
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						conv_kernel_size
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						output_stride
             SCREAMING_SNAKE_CASE   :      Any    					=						hidden_dropout_prob
             SCREAMING_SNAKE_CASE   :      Dict    					=						attention_probs_dropout_prob
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						classifier_dropout_prob
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						use_labels
             SCREAMING_SNAKE_CASE   :      int    					=						is_training
             SCREAMING_SNAKE_CASE   :      Dict    					=						num_labels
             SCREAMING_SNAKE_CASE   :      Dict    					=						initializer_range
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scope







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

             SCREAMING_SNAKE_CASE   :      int    					=						self.get_config()

             return config, pixel_values, labels, pixel_labels







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return MobileViTConfig(
                 image_size=self.image_size,							patch_size=self.patch_size,							num_channels=self.num_channels,							num_attention_heads=self.num_attention_heads,							hidden_act=self.hidden_act,							conv_kernel_size=self.conv_kernel_size,							output_stride=self.output_stride,							hidden_dropout_prob=self.hidden_dropout_prob,							attention_probs_dropout_prob=self.attention_probs_dropout_prob,							classifier_dropout_prob=self.classifier_dropout_prob,							initializer_range=self.initializer_range,							)







       def        UpperCamelCase_							(      self,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						MobileViTModel(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model(A  )
             self.parent.assertEqual(
                 result.last_hidden_state.shape,							(
                     self.batch_size,
                     self.last_hidden_size,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 ),							)







       def        UpperCamelCase_							(      self,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.num_labels
             SCREAMING_SNAKE_CASE   :      Tuple    					=						MobileViTForImageClassification(A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						model(A,							labels=A  )
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.num_labels)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.num_labels
             SCREAMING_SNAKE_CASE   :      str    					=						MobileViTForSemanticSegmentation(A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      str    					=						model(A  )
             self.parent.assertEqual(
                 result.logits.shape,							(
                     self.batch_size,
                     self.num_labels,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 ),							)
             SCREAMING_SNAKE_CASE   :      int    					=						model(A,							labels=A  )
             self.parent.assertEqual(
                 result.logits.shape,							(
                     self.batch_size,
                     self.num_labels,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 ),							)







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.prepare_config_and_inputs()
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						config_and_inputs
             SCREAMING_SNAKE_CASE   :      str    					=						{'pixel_values': pixel_values}
             return config, inputs_dict






@require_torch
class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Tuple        =						(
           (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       A						:				List[Any]        =						(
           {
               '''feature-extraction''': MobileViTModel,
               '''image-classification''': MobileViTForImageClassification,
               '''image-segmentation''': MobileViTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       A						:				Optional[int]        =						False
       A						:				Dict        =						False
       A						:				List[Any]        =						False
       A						:				Optional[int]        =						False







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						MobileViTModelTester(self  )
             SCREAMING_SNAKE_CASE   :      str    					=						MobileViTConfigTester(self,							config_class=A,							has_text_modality=A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self.config_tester.run_common_tests()







       @unittest.skip(reason='MobileViT does not use inputs_embeds'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       @unittest.skip(reason='MobileViT does not support input and output embeddings'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       @unittest.skip(reason='MobileViT does not output attentions'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Tuple    					=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model_class(A  )
                   SCREAMING_SNAKE_CASE   :      str    					=						inspect.signature(model.forward  )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   SCREAMING_SNAKE_CASE   :      Any    					=						[*signature.parameters.keys()]

                   SCREAMING_SNAKE_CASE   :      Any    					=						['pixel_values']
                   self.assertListEqual(arg_names[:1],							A  )







       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             def check_hidden_states_output(A,							A,							A  ):
                   SCREAMING_SNAKE_CASE   :      Any    					=						model_class(A  )
                   model.to(A  )
                   model.eval()

                   with torch.no_grad():
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						model(**self._prepare_for_class(A,							A  )  )

                   SCREAMING_SNAKE_CASE   :      Dict    					=						outputs.hidden_states

                   SCREAMING_SNAKE_CASE   :      List[str]    					=						5
                   self.assertEqual(len(A  ),							A  )

                   # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                   # with the width and height being successively divided by 2.
                   SCREAMING_SNAKE_CASE   :      int    					=						2
                   for i in range(len(A  )  ):
                         self.assertListEqual(
                             list(hidden_states[i].shape[-2:]  ),							[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],							)
                         divisor *= 2

                   self.assertEqual(self.model_tester.output_stride,							divisor // 2  )

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

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						True
                   check_hidden_states_output(A,							A,							A  )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						True

                   check_hidden_states_output(A,							A,							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   SCREAMING_SNAKE_CASE   :      int    					=						MobileViTModel.from_pretrained(A  )
                   self.assertIsNotNone(A  )




def   lowercase__(			):

      """simple docstring"""






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






@require_torch
@require_vision
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'  ) if is_vision_available() else None







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'  ).to(A  )

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

             # forward pass
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						model(**A  )

             # verify the logits
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.Size((1, 1_000)  )
             self.assertEqual(outputs.logits.shape,							A  )

             SCREAMING_SNAKE_CASE   :      int    					=						torch.tensor([-1.93_64, -1.23_27, -0.46_53]  ).to(A  )

             self.assertTrue(torch.allclose(outputs.logits[0, :3],							A,							atol=1E-4  )  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model.to(A  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'  )

             SCREAMING_SNAKE_CASE   :      str    					=						prepare_img()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						image_processor(images=A,							return_tensors='pt'  ).to(A  )

             # forward pass
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Dict    					=						model(**A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						outputs.logits

             # verify the logits
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.Size((1, 21, 32, 32)  )
             self.assertEqual(logits.shape,							A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.tensor(
                 [
                     [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
                     [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
                     [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
                 ],							device=A,							)

             self.assertTrue(torch.allclose(logits[0, :3, :3, :3],							A,							atol=1E-4  )  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						model.to(A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						prepare_img()
             SCREAMING_SNAKE_CASE   :      Any    					=						image_processor(images=A,							return_tensors='pt'  ).to(A  )

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

             SCREAMING_SNAKE_CASE   :      int    					=						outputs.logits.detach().cpu()

             SCREAMING_SNAKE_CASE   :      Dict    					=						image_processor.post_process_semantic_segmentation(outputs=A,							target_sizes=[(50, 60)]  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						torch.Size((50, 60)  )
             self.assertEqual(segmentation[0].shape,							A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						image_processor.post_process_semantic_segmentation(outputs=A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						torch.Size((32, 32)  )
             self.assertEqual(segmentation[0].shape,							A  )


