code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import collections
  import inspect
  import unittest
  
  from transformers import NatConfig
  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 NatBackbone, NatForImageClassification, NatModel
         from transformers.models.nat.modeling_nat import NAT_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=64				,					__A=4				,					__A=3				,					__A=16				,					__A=[1, 2, 1]				,					__A=[2, 4, 8]				,					__A=3				,					__A=2.0				,					__A=True				,					__A=0.0				,					__A=0.0				,					__A=0.1				,					__A="gelu"				,					__A=True				,					__A=0.02				,					__A=1E-5				,					__A=True				,					__A=None				,					__A=True				,					__A=10				,					__A=["stage1", "stage2"]				,					__A=[1, 2]				,					)      ->  Any:
       a					=parent
       a					=batch_size
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=embed_dim
       a					=depths
       a					=num_heads
       a					=kernel_size
       a					=mlp_ratio
       a					=qkv_bias
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=drop_path_rate
       a					=hidden_act
       a					=patch_norm
       a					=layer_norm_eps
       a					=initializer_range
       a					=is_training
       a					=scope
       a					=use_labels
       a					=num_labels
       a					=out_features
       a					=out_indices
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
       a					=None
       if self.use_labels:
           a					=ids_tensor([self.batch_size]				,					self.num_labels		)
  
       a					=self.get_config()
  
       return config, pixel_values, labels
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       return NatConfig(
           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				,					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    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  Union[str, Any]:
       a					=NatModel(config=__A		)
       model.to(__A		)
       model.eval()
       a					=model(__A		)
  
       a					=a					=(config.image_size // config.patch_size) // (2 ** (len(config.depths		) - 1))
       a					=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    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  Tuple:
       a					=NatForImageClassification(__A		)
       model.to(__A		)
       model.eval()
       a					=model(__A				,					labels=__A		)
       self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.num_labels)		)
  
       # test greyscale images
       a					=1
       a					=NatForImageClassification(__A		)
       model.to(__A		)
       model.eval()
  
       a					=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
       a					=model(__A		)
       self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.num_labels)		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  List[Any]:
       a					=NatBackbone(config=__A		)
       model.to(__A		)
       model.eval()
       a					=model(__A		)
  
       # 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
       a					=None
       a					=NatBackbone(config=__A		)
       model.to(__A		)
       model.eval()
       a					=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, model.channels[-1], 4, 4]		)
  
       # verify channels
       self.parent.assertEqual(len(model.channels		)				,					1		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       a					=self.prepare_config_and_inputs()
       a	,   a	,   a					=config_and_inputs
       a					={'''pixel_values''': pixel_values}
       return config, inputs_dict
  
  
  
  
  
  
  @require_natten
  @require_torch
  class    __A       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						(
       (
           NatModel,
           NatForImageClassification,
           NatBackbone,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase				      =						(
       {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase				      =						False
  
   __lowerCAmelCase				      =						False
   __lowerCAmelCase				      =						False
   __lowerCAmelCase				      =						False
   __lowerCAmelCase				      =						False
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       a					=NatModelTester(self		)
       a					=ConfigTester(self				,					config_class=__A				,					embed_dim=37		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       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    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       return
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       a					=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       a					=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__A		)
  
  
   @unittest.skip(reason='''Nat does not use inputs_embeds'''		)
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       pass
  
  
   @unittest.skip(reason='''Nat does not use feedforward chunking'''		)
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       pass
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       a	,   a					=self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
           a					=model_class(__A		)
           self.assertIsInstance(model.get_input_embeddings()				,					(nn.Module)		)
           a					=model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__A				,					nn.Linear		)		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       a	,   a					=self.model_tester.prepare_config_and_inputs_for_common()
  
       for model_class in self.all_model_classes:
           a					=model_class(__A		)
           a					=inspect.signature(model.forward		)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           a					=[*signature.parameters.keys()]
  
           a					=['''pixel_values''']
           self.assertListEqual(arg_names[:1]				,					__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  List[str]:
       a					=model_class(__A		)
       model.to(__A		)
       model.eval()
  
       with torch.no_grad():
           a					=model(**self._prepare_for_class(__A				,					__A		)		)
  
       a					=outputs.hidden_states
  
       a					=getattr(
           self.model_tester				,					'''expected_num_hidden_layers'''				,					len(self.model_tester.depths		) + 1		)
       self.assertEqual(len(__A		)				,					__A		)
  
       # Nat has a different seq_length
       a					=(
           config.patch_size
           if isinstance(config.patch_size				,					collections.abc.Iterable		)
           else (config.patch_size, config.patch_size)
       )
  
       a					=image_size[0] // patch_size[0]
       a					=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__ != "NatBackbone":
           a					=outputs.reshaped_hidden_states
           self.assertEqual(len(__A		)				,					__A		)
  
           a	,   a	,   a	,   a					=reshaped_hidden_states[0].shape
           a					=(
               reshaped_hidden_states[0].view(__A				,					__A				,					__A				,					__A		).permute(0				,					2				,					3				,					1		)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-3:]		)				,					[height, width, self.model_tester.embed_dim]				,					)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       a	,   a					=self.model_tester.prepare_config_and_inputs_for_common()
  
       a					=(
           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:
           a					=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"]
           a					=True
  
           self.check_hidden_states_output(__A				,					__A				,					__A				,					__A		)
  
  
   @slow
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           a					=NatModel.from_pretrained(__A		)
           self.assertIsNotNone(__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       a	,   a					=self.model_tester.prepare_config_and_inputs_for_common()
  
       a					=_config_zero_init(__A		)
       for model_class in self.all_model_classes:
           a					=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_natten
  @require_vision
  @require_torch
  class    __A       ( unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   @cached_property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''		) if is_vision_available() else None
  
  
  
  
  
   @slow
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       a					=NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''		).to(__A		)
       a					=self.default_image_processor
  
       a					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       a					=image_processor(images=__A				,					return_tensors='''pt'''		).to(__A		)
  
       # forward pass
       with torch.no_grad():
           a					=model(**__A		)
  
       # verify the logits
       a					=torch.Size((1, 1000)		)
       self.assertEqual(outputs.logits.shape				,					__A		)
       a					=torch.tensor([0.3_805, -0.8_676, -0.3_912]		).to(__A		)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,					__A				,					atol=1E-4		)		)
  
  
  
  
  
  
  @require_torch
  @require_natten
  class    __A       ( unittest.TestCase,					_SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						(NatBackbone,) if is_torch_available() else ()
   __lowerCAmelCase				      =						NatConfig
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       a					=NatModelTester(self		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"hybrid-clip"
   __lowerCAmelCase				      =						True
  
  
   def __init__(	self				,					__A=512				,					**__A		)      ->  Any:
       super().__init__(**__A		)
  
       if "text_config" not in kwargs:
           raise ValueError('''`text_config` can not be `None`.'''		)
  
       if "vision_config" not in kwargs:
           raise ValueError('''`vision_config` can not be `None`.'''		)
  
       a					=kwargs.pop('''text_config'''		)
       a					=kwargs.pop('''vision_config'''		)
  
       a					=text_config.pop('''model_type'''		)
       a					=vision_config.pop('''model_type'''		)
  
       from transformers import AutoConfig
  
       a					=AutoConfig.for_model(__A				,					**__A		)
  
       if vision_model_type == "clip":
           a					=AutoConfig.for_model(__A				,					**__A		).vision_config
       elif vision_model_type == "clip_vision_model":
           from transformers import CLIPVisionConfig
  
           a					=CLIPVisionConfig(**__A		)
       else:
           a					=AutoConfig.for_model(__A				,					**__A		)
  
       a					=projection_dim
       a					=1.0
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					**__A		)      ->  Tuple:
       return cls(text_config=text_config.to_dict()				,					vision_config=vision_config.to_dict()				,					**__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=copy.deepcopy(self.__dict__		)
       a					=self.text_config.to_dict()
       a					=self.vision_config.to_dict()
       a					=self.__class__.model_type
       return output