"""simple docstring"""
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor




class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase=13   ,      lowercase=30   ,      lowercase=2   ,      lowercase=3   ,      lowercase=True   ,      lowercase=True   ,      lowercase=32   ,      lowercase=5   ,      lowercase=4   ,      lowercase=37   ,      lowercase="gelu"   ,      lowercase=0.1   ,      lowercase=0.1   ,      lowercase=10   ,      lowercase=0.02   ,      lowercase=3   ,      lowercase=0.6   ,      lowercase=None   ,      )     ->      str:





        '''simple docstring'''





        a__:					Any      							=     parent
        a__:					Any      							=     batch_size
        a__:					int      							=     image_size
        a__:					str      							=     patch_size
        a__:					Optional[Any]      							=     num_channels
        a__:					Tuple      							=     is_training
        a__:					Optional[int]      							=     use_labels
        a__:					Tuple      							=     hidden_size
        a__:					List[str]      							=     num_hidden_layers
        a__:					List[str]      							=     num_attention_heads
        a__:					Dict      							=     intermediate_size
        a__:					Any      							=     hidden_act
        a__:					str      							=     hidden_dropout_prob
        a__:					Optional[Any]      							=     attention_probs_dropout_prob
        a__:					Optional[int]      							=     type_sequence_label_size
        a__:					List[str]      							=     initializer_range
        a__:					Dict      							=     mask_ratio
        a__:					Union[str, Any]      							=     scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        a__:					str      							=     (image_size // patch_size) ** 2
        a__:					Tuple      							=     int(math.ceil((1 - mask_ratio) * (num_patches + 1)))



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					List[str]      							=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        a__:					Union[str, Any]      							=     None
        if self.use_labels:
          a__:					List[str]      							=     ids_tensor([self.batch_size]   ,      self.type_sequence_label_size)

        a__:					str      							=     self.get_config()

        return config, pixel_values, labels



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        return ViTMAEConfig(
            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=lowercase   ,      initializer_range=self.initializer_range   ,      mask_ratio=self.mask_ratio   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[int]      							=     ViTMAEModel(config=lowercase)
        model.to(lowercase)
        model.eval()
        a__:					int      							=     model(lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size))



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Tuple:





        '''simple docstring'''





        a__:					List[str]      							=     ViTMAEForPreTraining(lowercase)
        model.to(lowercase)
        model.eval()
        a__:					List[str]      							=     model(lowercase)
        a__:					int      							=     (self.image_size // self.patch_size) ** 2
        a__:					Tuple      							=     self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, num_patches, expected_num_channels))

        # test greyscale images
        a__:					Tuple      							=     1
        a__:					List[str]      							=     ViTMAEForPreTraining(lowercase)
        model.to(lowercase)
        model.eval()
        a__:					Optional[int]      							=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        a__:					Union[str, Any]      							=     model(lowercase)
        a__:					Tuple      							=     self.patch_size**2
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, num_patches, expected_num_channels))



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





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







@require_torch
class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      a__				=		{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

      a__				=		False
      a__				=		False
      a__				=		False
      a__				=		False



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





        '''simple docstring'''





        a__:					Dict      							=     ViTMAEModelTester(self)
        a__:					List[Any]      							=     ConfigTester(self   ,      config_class=lowercase   ,      has_text_modality=lowercase   ,      hidden_size=37)



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





        '''simple docstring'''





        self.config_tester.run_common_tests()



      @unittest.skip(reason='ViTMAE does not use inputs_embeds')
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        a__:					int      							=     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__:					Tuple      							=     model_class(lowercase)
          self.assertIsInstance(model.get_input_embeddings()   ,      (nn.Module))
          a__:					Tuple      							=     model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(lowercase   ,      nn.Linear))



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[Any]      							=     self.model_tester.prepare_config_and_inputs_for_common()

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

          a__:					Dict      							=     ['pixel_values']
          self.assertListEqual(arg_names[:1]   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					int      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					Any      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Dict:





        '''simple docstring'''





        np.random.seed(2)

        a__:					List[Any]      							=     int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
        a__:					List[str]      							=     np.random.uniform(size=(self.model_tester.batch_size, num_patches))
        a__:					Any      							=     torch.from_numpy(lowercase)

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        a__:					str      							=     pt_noise

        super().check_pt_tf_models(lowercase   ,      lowercase   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					int      							=     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__:					Dict      							=     model_class(lowercase)
          model.to(lowercase)
          model.eval()
          # make random mask reproducible
          torch.manual_seed(2)
          with torch.no_grad():
            a__:					Optional[Any]      							=     model(**self._prepare_for_class(lowercase   ,      lowercase))

          a__:					Any      							=     outputs[0].cpu().numpy()
          a__:					str      							=     0

          with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(lowercase)
            a__:					Optional[Any]      							=     model_class.from_pretrained(lowercase)
            model.to(lowercase)
            # make random mask reproducible
            torch.manual_seed(2)
            with torch.no_grad():
              a__:					Any      							=     model(**self._prepare_for_class(lowercase   ,      lowercase))

            # Make sure we don't have nans
            a__:					Optional[int]      							=     after_outputs[0].cpu().numpy()
            a__:					int      							=     0
            a__:					int      							=     np.amax(np.abs(out_a - out_a))
            self.assertLessEqual(lowercase   ,      1e-5)



      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        pass



      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        pass



      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        pass



      @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        pass



      @slow
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:					Any      							=     ViTMAEModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)







def 						__a       (     )     ->Dict:
  a__:					Dict      							=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image




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



      @cached_property
      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None



      @slow
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        np.random.seed(2)

        a__:					Optional[Any]      							=     ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(lowercase)

        a__:					int      							=     self.default_image_processor
        a__:					Optional[Any]      							=     prepare_img()
        a__:					int      							=     image_processor(images=lowercase   ,      return_tensors='pt').to(lowercase)

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        a__:					Any      							=     ViTMAEConfig()
        a__:					Tuple      							=     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
        a__:					Union[str, Any]      							=     np.random.uniform(size=(1, num_patches))

        # forward pass
        with torch.no_grad():
          a__:					Dict      							=     model(**lowercase   ,      noise=torch.from_numpy(lowercase).to(device=lowercase))

        # verify the logits
        a__:					Dict      							=     torch.Size((1, 1_96, 7_68))
        self.assertEqual(outputs.logits.shape   ,      lowercase)

        a__:					List[str]      							=     torch.tensor(
            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]])

        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,      expected_slice.to(lowercase)   ,      atol=1e-4))



"""simple docstring"""
import unittest

from knapsack import knapsack as k




class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					List[Any]      							=     0
        a__:					Dict      							=     [0]
        a__:					int      							=     [0]
        a__:					Optional[Any]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)

        a__:					str      							=     [60]
        a__:					Dict      							=     [10]
        a__:					List[str]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					int      							=     3
        a__:					str      							=     [1, 2, 3]
        a__:					Dict      							=     [3, 2, 1]
        a__:					Optional[int]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      5)



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





        '''simple docstring'''





        a__:					Any      							=     50
        a__:					Optional[int]      							=     [60, 1_00, 1_20]
        a__:					str      							=     [10, 20, 30]
        a__:					int      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      2_20)


if __name__ == "__main__":
 unittest.main()


