
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 			UpperCamelCase_				:




      '''simple docstring'''







      def __init__( self					,					_UpperCAmelCase					,					_UpperCAmelCase=13					,					_UpperCAmelCase=64					,					_UpperCAmelCase=4					,					_UpperCAmelCase=3					,					_UpperCAmelCase=16					,					_UpperCAmelCase=[1, 2, 1]					,					_UpperCAmelCase=[2, 4, 8]					,					_UpperCAmelCase=3					,					_UpperCAmelCase=[[3], [1, 2], [1]]					,					_UpperCAmelCase=2.0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=True					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=1E-5					,					_UpperCAmelCase=True					,					_UpperCAmelCase=None					,					_UpperCAmelCase=True					,					_UpperCAmelCase=10					,					_UpperCAmelCase=["stage1", "stage2"]					,					_UpperCAmelCase=[1, 2]					,					):
            lowerCAmelCase_						=      parent
            lowerCAmelCase_						=      batch_size
            lowerCAmelCase_						=      image_size
            lowerCAmelCase_						=      patch_size
            lowerCAmelCase_						=      num_channels
            lowerCAmelCase_						=      embed_dim
            lowerCAmelCase_						=      depths
            lowerCAmelCase_						=      num_heads
            lowerCAmelCase_						=      kernel_size
            lowerCAmelCase_						=      dilations
            lowerCAmelCase_						=      mlp_ratio
            lowerCAmelCase_						=      qkv_bias
            lowerCAmelCase_						=      hidden_dropout_prob
            lowerCAmelCase_						=      attention_probs_dropout_prob
            lowerCAmelCase_						=      drop_path_rate
            lowerCAmelCase_						=      hidden_act
            lowerCAmelCase_						=      patch_norm
            lowerCAmelCase_						=      layer_norm_eps
            lowerCAmelCase_						=      initializer_range
            lowerCAmelCase_						=      is_training
            lowerCAmelCase_						=      scope
            lowerCAmelCase_						=      use_labels
            lowerCAmelCase_						=      num_labels
            lowerCAmelCase_						=      out_features
            lowerCAmelCase_						=      out_indices







      def lowercase__		( self):
            lowerCAmelCase_						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

            lowerCAmelCase_						=      None
            if self.use_labels:
                  lowerCAmelCase_						=      ids_tensor([self.batch_size]					,					self.num_labels)

            lowerCAmelCase_						=      self.get_config()

            return config, pixel_values, labels







      def lowercase__		( self):
            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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      DinatModel(config=__SCREAMING_SNAKE_CASE)
            model.to(__SCREAMING_SNAKE_CASE)
            model.eval()
            lowerCAmelCase_						=      model(__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
            lowerCAmelCase_						=      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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      DinatForImageClassification(__SCREAMING_SNAKE_CASE)
            model.to(__SCREAMING_SNAKE_CASE)
            model.eval()
            lowerCAmelCase_						=      model(__SCREAMING_SNAKE_CASE					,					labels=__SCREAMING_SNAKE_CASE)
            self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels))

            # test greyscale images
            lowerCAmelCase_						=      1
            lowerCAmelCase_						=      DinatForImageClassification(__SCREAMING_SNAKE_CASE)
            model.to(__SCREAMING_SNAKE_CASE)
            model.eval()

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







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      DinatBackbone(config=__SCREAMING_SNAKE_CASE)
            model.to(__SCREAMING_SNAKE_CASE)
            model.eval()
            lowerCAmelCase_						=      model(__SCREAMING_SNAKE_CASE)

            # 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], 16, 16])

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

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

            # 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 lowercase__		( self):
            lowerCAmelCase_						=      self.prepare_config_and_inputs()
            lowerCAmelCase_						=      config_and_inputs
            lowerCAmelCase_						=      {'''pixel_values''': pixel_values}
            return config, inputs_dict


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




      '''simple docstring'''







      a		:Optional[int]						 =      (
          (
              DinatModel,
              DinatForImageClassification,
              DinatBackbone,
          )
          if is_torch_available()
          else ()
      )
      a		:Optional[int]						 =      (
          {'feature-extraction': DinatModel, 'image-classification': DinatForImageClassification}
          if is_torch_available()
          else {}
      )
      a		:Tuple						 =      False

      a		:Dict						 =      False
      a		:Union[str, Any]						 =      False
      a		:int						 =      False
      a		:str						 =      False







      def lowercase__		( self):
            lowerCAmelCase_						=      DinatModelTester(self)
            lowerCAmelCase_						=      ConfigTester(self					,					config_class=__SCREAMING_SNAKE_CASE					,					embed_dim=37)







      def lowercase__		( self):
            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 lowercase__		( self):
            return







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE)







      @unittest.skip(reason='''Dinat does not use inputs_embeds''')
      def lowercase__		( self):
            pass







      @unittest.skip(reason='''Dinat does not use feedforward chunking''')
      def lowercase__		( self):
            pass







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs_for_common()

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







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowerCAmelCase_						=      ['''pixel_values''']
                  self.assertListEqual(arg_names[:1]					,					__SCREAMING_SNAKE_CASE)







      def lowercase__		( self):
            self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.''')







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      model_class(__SCREAMING_SNAKE_CASE)
            model.to(__SCREAMING_SNAKE_CASE)
            model.eval()

            with torch.no_grad():
                  lowerCAmelCase_						=      model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE))

            lowerCAmelCase_						=      outputs.hidden_states

            lowerCAmelCase_						=      getattr(
                self.model_tester					,					'''expected_num_hidden_layers'''					,					len(self.model_tester.depths) + 1)
            self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,					__SCREAMING_SNAKE_CASE)

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

            lowerCAmelCase_						=      image_size[0] // patch_size[0]
            lowerCAmelCase_						=      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":
                  lowerCAmelCase_						=      outputs.reshaped_hidden_states
                  self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,					__SCREAMING_SNAKE_CASE)

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







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase_						=      (
                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:
                  lowerCAmelCase_						=      True
                  self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)

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

                  self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)







      @slow
      def lowercase__		( self):





            for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase_						=      DinatModel.from_pretrained(__SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(__SCREAMING_SNAKE_CASE)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase_						=      _config_zero_init(__SCREAMING_SNAKE_CASE)
            for model_class in self.all_model_classes:
                  lowerCAmelCase_						=      model_class(config=__SCREAMING_SNAKE_CASE)
                  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 			UpperCamelCase_				(  unittest.TestCase     ):




      '''simple docstring'''







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







      @slow
      def lowercase__		( self):
            lowerCAmelCase_						=      DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224''').to(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      self.default_image_processor

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

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase_						=      model(**__SCREAMING_SNAKE_CASE)

            # verify the logits
            lowerCAmelCase_						=      torch.Size((1, 1_000))
            self.assertEqual(outputs.logits.shape					,					__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      torch.tensor([-0.1545, -0.7667, 0.4642]).to(__SCREAMING_SNAKE_CASE)
            self.assertTrue(torch.allclose(outputs.logits[0, :3]					,					__SCREAMING_SNAKE_CASE					,					atol=1E-4))


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




      '''simple docstring'''







      a		:Union[str, Any]						 =      (DinatBackbone,) if is_torch_available() else ()
      a		:Any						 =      DinatConfig







      def lowercase__		( self):
            lowerCAmelCase_						=      DinatModelTester(self)





'''simple docstring'''







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	['''image_processor''', '''tokenizer''']
					lowerCAmelCase_	  =	'''EfficientNetImageProcessor'''
					lowerCAmelCase_	  =	('''BertTokenizer''', '''BertTokenizerFast''')






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											super().__init__(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					def __call__(  self							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE="max_length"							,				__SCREAMING_SNAKE_CASE=64							,				__SCREAMING_SNAKE_CASE=None							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''						)

											if text is not None:
																	lowercase_      :	str						=			self.tokenizer(
																	    __SCREAMING_SNAKE_CASE							,				padding=__SCREAMING_SNAKE_CASE							,				max_length=__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if images is not None:
																	lowercase_      :	int						=			self.image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if text is not None and images is not None:
																	lowercase_      :	Any						=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE						)							,				tensor_type=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Dict						=			self.tokenizer.model_input_names
											lowercase_      :	Optional[int]						=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)



