
import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         FocalNetBackbone,
         FocalNetForImageClassification,
         FocalNetForMaskedImageModeling,
         FocalNetModel,
     )
     from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor

class    _a				:





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__=13       ,							A__=32       ,							A__=2       ,							A__=3       ,							A__=16       ,							A__=[32, 64, 1_28]       ,							A__=[1, 2, 1]       ,							A__=[2, 2, 4]       ,							A__=2       ,							A__=2.0       ,							A__=True       ,							A__=0.0       ,							A__=0.0       ,							A__=0.1       ,							A__="gelu"       ,							A__=False       ,							A__=True       ,							A__=0.02       ,							A__=1E-5       ,							A__=True       ,							A__=None       ,							A__=True       ,							A__=10       ,							A__=8       ,							A__=["stage1", "stage2"]       ,							A__=[1, 2]       ,							)    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				parent
          _SCREAMING_SNAKE_CASE      =				batch_size
          _SCREAMING_SNAKE_CASE      =				image_size
          _SCREAMING_SNAKE_CASE      =				patch_size
          _SCREAMING_SNAKE_CASE      =				num_channels
          _SCREAMING_SNAKE_CASE      =				embed_dim
          _SCREAMING_SNAKE_CASE      =				hidden_sizes
          _SCREAMING_SNAKE_CASE      =				depths
          _SCREAMING_SNAKE_CASE      =				num_heads
          _SCREAMING_SNAKE_CASE      =				window_size
          _SCREAMING_SNAKE_CASE      =				mlp_ratio
          _SCREAMING_SNAKE_CASE      =				qkv_bias
          _SCREAMING_SNAKE_CASE      =				hidden_dropout_prob
          _SCREAMING_SNAKE_CASE      =				attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE      =				drop_path_rate
          _SCREAMING_SNAKE_CASE      =				hidden_act
          _SCREAMING_SNAKE_CASE      =				use_absolute_embeddings
          _SCREAMING_SNAKE_CASE      =				patch_norm
          _SCREAMING_SNAKE_CASE      =				layer_norm_eps
          _SCREAMING_SNAKE_CASE      =				initializer_range
          _SCREAMING_SNAKE_CASE      =				is_training
          _SCREAMING_SNAKE_CASE      =				scope
          _SCREAMING_SNAKE_CASE      =				use_labels
          _SCREAMING_SNAKE_CASE      =				type_sequence_label_size
          _SCREAMING_SNAKE_CASE      =				encoder_stride
          _SCREAMING_SNAKE_CASE      =				out_features
          _SCREAMING_SNAKE_CASE      =				out_indices






       def        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

          _SCREAMING_SNAKE_CASE      =				self.get_config()

          return config, pixel_values, labels






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          return FocalNetConfig(
              image_size=self.image_size       ,							patch_size=self.patch_size       ,							num_channels=self.num_channels       ,							embed_dim=self.embed_dim       ,							hidden_sizes=self.hidden_sizes       ,							depths=self.depths       ,							num_heads=self.num_heads       ,							window_size=self.window_size       ,							mlp_ratio=self.mlp_ratio       ,							qkv_bias=self.qkv_bias       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							drop_path_rate=self.drop_path_rate       ,							hidden_act=self.hidden_act       ,							use_absolute_embeddings=self.use_absolute_embeddings       ,							path_norm=self.patch_norm       ,							layer_norm_eps=self.layer_norm_eps       ,							initializer_range=self.initializer_range       ,							encoder_stride=self.encoder_stride       ,							out_features=self.out_features       ,							out_indices=self.out_indices       ,							)






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				FocalNetModel(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				model(A__      )

          _SCREAMING_SNAKE_CASE      =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
          _SCREAMING_SNAKE_CASE      =				int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

          self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, expected_seq_len, expected_dim)      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				FocalNetBackbone(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				model(A__      )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps      )       ,							len(config.out_features      )      )
          self.parent.assertListEqual(list(result.feature_maps[0].shape      )       ,							[self.batch_size, self.image_size, 8, 8]      )

          # verify channels
          self.parent.assertEqual(len(model.channels      )       ,							len(config.out_features      )      )
          self.parent.assertListEqual(model.channels       ,							config.hidden_sizes[:-1]      )

          # verify backbone works with out_features=None
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				FocalNetBackbone(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				model(A__      )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps      )       ,							1      )
          self.parent.assertListEqual(list(result.feature_maps[0].shape      )       ,							[self.batch_size, self.image_size * 2, 4, 4]      )

          # verify channels
          self.parent.assertEqual(len(model.channels      )       ,							1      )
          self.parent.assertListEqual(model.channels       ,							[config.hidden_sizes[-1]]      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				FocalNetForMaskedImageModeling(config=A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				model(A__      )
          self.parent.assertEqual(
              result.reconstruction.shape       ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

          # test greyscale images
          _SCREAMING_SNAKE_CASE      =				1
          _SCREAMING_SNAKE_CASE      =				FocalNetForMaskedImageModeling(A__      )
          model.to(A__      )
          model.eval()

          _SCREAMING_SNAKE_CASE      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
          _SCREAMING_SNAKE_CASE      =				model(A__      )
          self.parent.assertEqual(result.reconstruction.shape       ,							(self.batch_size, 1, self.image_size, self.image_size)      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.type_sequence_label_size
          _SCREAMING_SNAKE_CASE      =				FocalNetForImageClassification(A__      )
          model.to(A__      )
          model.eval()
          _SCREAMING_SNAKE_CASE      =				model(A__       ,							labels=A__      )
          self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)      )

          # test greyscale images
          _SCREAMING_SNAKE_CASE      =				1
          _SCREAMING_SNAKE_CASE      =				FocalNetForImageClassification(A__      )
          model.to(A__      )
          model.eval()

          _SCREAMING_SNAKE_CASE      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
          _SCREAMING_SNAKE_CASE      =				model(A__      )
          self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)      )






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.prepare_config_and_inputs()

          _SCREAMING_SNAKE_CASE      =				config_and_inputs
          _SCREAMING_SNAKE_CASE      =				{"pixel_values": pixel_values}
          return config, inputs_dict






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





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  (
           (
               FocalNetModel,
               FocalNetForImageClassification,
               FocalNetForMaskedImageModeling,
               FocalNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE      =  (
           {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE      =  False

       SCREAMING_SNAKE_CASE      =  False
       SCREAMING_SNAKE_CASE      =  False
       SCREAMING_SNAKE_CASE      =  False
       SCREAMING_SNAKE_CASE      =  False






       def        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				FocalNetModelTester(self      )
          _SCREAMING_SNAKE_CASE      =				ConfigTester(self       ,							config_class=A__       ,							embed_dim=37       ,							has_text_modality=A__      )






       def        UpperCamelCase				(							self      )    ->			Tuple:
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()






       def        UpperCamelCase				(							self      )    ->			Any:
          return






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__      )






       def        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*A__      )






       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*A__      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A__      )






       @unittest.skip(reason="""FocalNet does not use inputs_embeds"""      )
       def        UpperCamelCase				(							self      )    ->			Any:
          pass






       @unittest.skip(reason="""FocalNet does not use feedforward chunking"""      )
       def        UpperCamelCase				(							self      )    ->			str:
          pass






       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:-1]:
             _SCREAMING_SNAKE_CASE      =				model_class(A__      )
             self.assertIsInstance(model.get_input_embeddings()       ,							(nn.Module)      )
             _SCREAMING_SNAKE_CASE      =				model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A__       ,							nn.Linear      )      )






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes[:-1]:
             _SCREAMING_SNAKE_CASE      =				model_class(A__      )
             _SCREAMING_SNAKE_CASE      =				inspect.signature(model.forward      )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _SCREAMING_SNAKE_CASE      =				[*signature.parameters.keys()]

             _SCREAMING_SNAKE_CASE      =				["pixel_values"]
             self.assertListEqual(arg_names[:1]       ,							A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				model_class(A__      )
          model.to(A__      )
          model.eval()

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE      =				model(**self._prepare_for_class(A__       ,							A__      )      )

          _SCREAMING_SNAKE_CASE      =				outputs.hidden_states

          _SCREAMING_SNAKE_CASE      =				getattr(
              self.model_tester       ,							"""expected_num_hidden_layers"""       ,							len(self.model_tester.depths      ) + 1      )
          self.assertEqual(len(A__      )       ,							A__      )

          # FocalNet has a different seq_length
          _SCREAMING_SNAKE_CASE      =				(
              config.patch_size
              if isinstance(config.patch_size       ,							collections.abc.Iterable      )
              else (config.patch_size, config.patch_size)
          )

          _SCREAMING_SNAKE_CASE      =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]      )       ,							[num_patches, self.model_tester.embed_dim]       ,							)

          _SCREAMING_SNAKE_CASE      =				outputs.reshaped_hidden_states
          self.assertEqual(len(A__      )       ,							A__      )

          _SCREAMING_SNAKE_CASE      =				reshaped_hidden_states[0].shape
          _SCREAMING_SNAKE_CASE      =				(
              reshaped_hidden_states[0].view(A__       ,							A__       ,							height * width      ).permute(0       ,							2       ,							1      )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]      )       ,							[num_patches, self.model_tester.embed_dim]       ,							)






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE      =				(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size       ,							collections.abc.Iterable      )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes[:-1]:
             _SCREAMING_SNAKE_CASE      =				True
             self.check_hidden_states_output(A__       ,							A__       ,							A__       ,							A__      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             _SCREAMING_SNAKE_CASE      =				True

             self.check_hidden_states_output(A__       ,							A__       ,							A__       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE      =				3

          _SCREAMING_SNAKE_CASE      =				(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size       ,							collections.abc.Iterable      )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          _SCREAMING_SNAKE_CASE      =				(
              config.patch_size
              if isinstance(config.patch_size       ,							collections.abc.Iterable      )
              else (config.patch_size, config.patch_size)
          )

          _SCREAMING_SNAKE_CASE      =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          _SCREAMING_SNAKE_CASE      =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes[:-1]:
             _SCREAMING_SNAKE_CASE      =				True
             self.check_hidden_states_output(A__       ,							A__       ,							A__       ,							(padded_height, padded_width)      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             _SCREAMING_SNAKE_CASE      =				True
             self.check_hidden_states_output(A__       ,							A__       ,							A__       ,							(padded_height, padded_width)      )






       @slow
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _SCREAMING_SNAKE_CASE      =				FocalNetModel.from_pretrained(A__      )
             self.assertIsNotNone(A__      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.model_tester.prepare_config_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE      =				_config_zero_init(A__      )
          for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE      =				model_class(config=A__      )
             for name, param in model.named_parameters():
                if "embeddings" not in name and param.requires_grad:
                   self.assertIn(
                       ((param.data.mean() * 1E9).round() / 1E9).item()       ,							[0.0, 1.0]       ,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"       ,							)






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





       """simple docstring"""




       @cached_property
       def        UpperCamelCase				(							self      )    ->			List[str]:
          # TODO update organization
          return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""      ) if is_vision_available() else None






       @slow
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""      ).to(A__      )
          _SCREAMING_SNAKE_CASE      =				self.default_image_processor

          _SCREAMING_SNAKE_CASE      =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
          _SCREAMING_SNAKE_CASE      =				image_processor(images=A__       ,							return_tensors="""pt"""      ).to(A__      )

          # forward pass
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE      =				model(**A__      )

          # verify the logits
          _SCREAMING_SNAKE_CASE      =				torch.Size((1, 10_00)      )
          self.assertEqual(outputs.logits.shape       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor([0.2166, -0.4368, 0.2191]      ).to(A__      )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,							A__       ,							atol=1E-4      )      )
          self.assertTrue(outputs.logits.argmax(dim=-1      ).item()       ,							2_81      )






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





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  (FocalNetBackbone,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE      =  FocalNetConfig

       SCREAMING_SNAKE_CASE      =  False






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				FocalNetModelTester(self      )








'''simple docstring'''



from sklearn.metrics import matthews_corrcoef

import datasets


UpperCamelCase__      :		List[str]       							=  "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

UpperCamelCase__      :		List[Any]       							=  "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

UpperCamelCase__      :		Any       							=  "\\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    _a				(datasets.Metric):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
                  {
                      """predictions""": datasets.Value("""int32"""      ),
                      """references""": datasets.Value("""int32"""      ),
                  }      )       ,							reference_urls=[
                  """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
              ]       ,							)






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__=None      )    ->			List[str]:
          return {
              "matthews_correlation": float(matthews_corrcoef(A__       ,							A__       ,							sample_weight=A__      )      ),
          }






