





"""simple docstring"""







import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, 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 DinatBackbone, DinatForImageClassification, DinatModel
  from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor

class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=1_3					,				lowerCamelCase_=6_4					,				lowerCamelCase_=4					,				lowerCamelCase_=3					,				lowerCamelCase_=1_6					,				lowerCamelCase_=[1, 2, 1]					,				lowerCamelCase_=[2, 4, 8]					,				lowerCamelCase_=3					,				lowerCamelCase_=[[3], [1, 2], [1]]					,				lowerCamelCase_=2.0					,				lowerCamelCase_=True					,				lowerCamelCase_=0.0					,				lowerCamelCase_=0.0					,				lowerCamelCase_=0.1					,				lowerCamelCase_="gelu"					,				lowerCamelCase_=True					,				lowerCamelCase_=0.02					,				lowerCamelCase_=1e-5					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=1_0					,				lowerCamelCase_=["stage1", "stage2"]					,				lowerCamelCase_=[1, 2]					,				)							->      List[str]:
       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  image_size
       UpperCamelCase										=  patch_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  embed_dim
       UpperCamelCase										=  depths
       UpperCamelCase										=  num_heads
       UpperCamelCase										=  kernel_size
       UpperCamelCase										=  dilations
       UpperCamelCase										=  mlp_ratio
       UpperCamelCase										=  qkv_bias
       UpperCamelCase										=  hidden_dropout_prob
       UpperCamelCase										=  attention_probs_dropout_prob
       UpperCamelCase										=  drop_path_rate
       UpperCamelCase										=  hidden_act
       UpperCamelCase										=  patch_norm
       UpperCamelCase										=  layer_norm_eps
       UpperCamelCase										=  initializer_range
       UpperCamelCase										=  is_training
       UpperCamelCase										=  scope
       UpperCamelCase										=  use_labels
       UpperCamelCase										=  num_labels
       UpperCamelCase										=  out_features
       UpperCamelCase										=  out_indices







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       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.num_labels)

       UpperCamelCase										=  self.get_config()

       return config, pixel_values, labels







   def      UpperCAmelCase__ (		self)							->      Any:
       return DinatConfig(
           num_labels=self.num_labels					,				image_size=self.image_size					,				patch_size=self.patch_size					,				num_channels=self.num_channels					,				embed_dim=self.embed_dim					,				depths=self.depths					,				num_heads=self.num_heads					,				kernel_size=self.kernel_size					,				dilations=self.dilations					,				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					,				patch_norm=self.patch_norm					,				layer_norm_eps=self.layer_norm_eps					,				initializer_range=self.initializer_range					,				out_features=self.out_features					,				out_indices=self.out_indices					,				)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      str:
       UpperCamelCase										=  DinatModel(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_)

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

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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Any:
       UpperCamelCase										=  DinatForImageClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_					,				labels=lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels))

       # test greyscale images
       UpperCamelCase										=  1
       UpperCamelCase										=  DinatForImageClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()

       UpperCamelCase										=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
       UpperCamelCase										=  model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels))







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Tuple:
       UpperCamelCase										=  DinatBackbone(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_)

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps)					,				len(config.out_features))
       self.parent.assertListEqual(list(result.feature_maps[0].shape)					,				[self.batch_size, model.channels[0], 1_6, 1_6])

       # verify channels
       self.parent.assertEqual(len(model.channels)					,				len(config.out_features))

       # verify backbone works with out_features=None
       UpperCamelCase										=  None
       UpperCamelCase										=  DinatBackbone(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps)					,				1)
       self.parent.assertListEqual(list(result.feature_maps[0].shape)					,				[self.batch_size, model.channels[-1], 4, 4])

       # verify channels
       self.parent.assertEqual(len(model.channels)					,				1)







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase										=  self.prepare_config_and_inputs()
       UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  config_and_inputs
       UpperCamelCase										=  {'''pixel_values''': pixel_values}
       return config, inputs_dict

@require_natten
@require_torch
class 							snake_case_ (				lowerCamelCase_  ,   lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(
       (
           DinatModel,
           DinatForImageClassification,
           DinatBackbone,
       )
       if is_torch_available()
       else ()
   )
   A_										=					(
       {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
       if is_torch_available()
       else {}
   )
   A_										=					False

   A_										=					False
   A_										=					False
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       UpperCamelCase										=  DinatModelTester(self)
       UpperCamelCase										=  ConfigTester(self					,				config_class=lowerCamelCase_					,				embed_dim=3_7)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       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)							->      Dict:
       return







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_)







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







   def      UpperCAmelCase__ (		self)							->      Tuple:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*lowerCamelCase_)







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







   @unittest.skip(reason='''Dinat does not use feedforward chunking''')
   def      UpperCAmelCase__ (		self)							->      int:
       pass







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase										=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.''')







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase										=  model_class(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()

       with torch.no_grad():
           UpperCamelCase										=  model(**self._prepare_for_class(lowerCamelCase_					,				lowerCamelCase_))

       UpperCamelCase										=  outputs.hidden_states

       UpperCamelCase										=  getattr(
           self.model_tester					,				'''expected_num_hidden_layers'''					,				len(self.model_tester.depths) + 1)
       self.assertEqual(len(lowerCamelCase_)					,				lowerCamelCase_)

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

       UpperCamelCase										=  image_size[0] // patch_size[0]
       UpperCamelCase										=  image_size[1] // patch_size[1]

       self.assertListEqual(
           list(hidden_states[0].shape[-3:])					,				[height, width, self.model_tester.embed_dim]					,				)

       if model_class.__name__ != "DinatBackbone":
           UpperCamelCase										=  outputs.reshaped_hidden_states
           self.assertEqual(len(lowerCamelCase_)					,				lowerCamelCase_)

           UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  reshaped_hidden_states[0].shape
           UpperCamelCase										=  (
               reshaped_hidden_states[0].view(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_).permute(0					,				2					,				3					,				1)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-3:])					,				[height, width, self.model_tester.embed_dim]					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase										=  (
           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:
           UpperCamelCase										=  True
           self.check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)

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

           self.check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   @slow
   def      UpperCAmelCase__ (		self)							->      str:
       for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase										=  DinatModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)







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

       UpperCamelCase										=  _config_zero_init(lowerCamelCase_)
       for model_class in self.all_model_classes:
           UpperCamelCase										=  model_class(config=lowerCamelCase_)
           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_natten
@require_vision
@require_torch
class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   @cached_property
   def      UpperCAmelCase__ (		self)							->      Tuple:
       return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224''') if is_vision_available() else None







   @slow
   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase										=  DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224''').to(lowerCamelCase_)
       UpperCamelCase										=  self.default_image_processor

       UpperCamelCase										=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
       UpperCamelCase										=  image_processor(images=lowerCamelCase_					,				return_tensors='''pt''').to(lowerCamelCase_)

       # forward pass
       with torch.no_grad():
           UpperCamelCase										=  model(**lowerCamelCase_)

       # verify the logits
       UpperCamelCase										=  torch.Size((1, 1_0_0_0))
       self.assertEqual(outputs.logits.shape					,				lowerCamelCase_)
       UpperCamelCase										=  torch.tensor([-0.1545, -0.7667, 0.4642]).to(lowerCamelCase_)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]					,				lowerCamelCase_					,				atol=1e-4))

@require_torch
@require_natten
class 							snake_case_ (				unittest.TestCase  ,   lowerCamelCase_ ):







   """simple docstring"""




   A_										=					(DinatBackbone,) if is_torch_available() else ()
   A_										=					DinatConfig







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  DinatModelTester(self)





"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=7					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=1_8					,				lowerCamelCase_=3_0					,				lowerCamelCase_=4_0_0					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=None					,				)							->      Optional[Any]:
       UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 1_8}
       UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  num_frames
       UpperCamelCase										=  image_size
       UpperCamelCase										=  min_resolution
       UpperCamelCase										=  max_resolution
       UpperCamelCase										=  do_resize
       UpperCamelCase										=  size
       UpperCamelCase										=  do_normalize
       UpperCamelCase										=  image_mean
       UpperCamelCase										=  image_std
       UpperCamelCase										=  crop_size







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }

@require_torch
@require_vision
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					VideoMAEImageProcessor if is_vision_available() else None







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  VideoMAEImageProcessingTester(self)







   @property
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return self.image_processor_tester.prepare_image_processor_dict()







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_mean'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_std'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_normalize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_resize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_center_crop'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''size'''))







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 1_8})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 1_8, '''width''': 1_8})

       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict					,				size=4_2					,				crop_size=8_4)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 4_2})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 8_4, '''width''': 8_4})







   def      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PIL videos
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				Image.Image)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random numpy tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				numpify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				np.ndarray)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PyTorch tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				torchify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				torch.Tensor)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)