import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import YolosForObjectDetection, YolosModel
    from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor



class 							a				:







 """simple docstring"""






 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_=13						,							lowerCAmelCase_=[30, 30]						,							lowerCAmelCase_=2						,							lowerCAmelCase_=3						,							lowerCAmelCase_=True						,							lowerCAmelCase_=True						,							lowerCAmelCase_=32						,							lowerCAmelCase_=5						,							lowerCAmelCase_=4						,							lowerCAmelCase_=37						,							lowerCAmelCase_="gelu"						,							lowerCAmelCase_=0.1						,							lowerCAmelCase_=0.1						,							lowerCAmelCase_=10						,							lowerCAmelCase_=0.02						,							lowerCAmelCase_=3						,							lowerCAmelCase_=None						,							lowerCAmelCase_=8						,							lowerCAmelCase_=10						,							)    ->   List[str]:
        _A	  =    parent
        _A	  =    batch_size
        _A	  =    image_size
        _A	  =    patch_size
        _A	  =    num_channels
        _A	  =    is_training
        _A	  =    use_labels
        _A	  =    hidden_size
        _A	  =    num_hidden_layers
        _A	  =    num_attention_heads
        _A	  =    intermediate_size
        _A	  =    hidden_act
        _A	  =    hidden_dropout_prob
        _A	  =    attention_probs_dropout_prob
        _A	  =    type_sequence_label_size
        _A	  =    initializer_range
        _A	  =    num_labels
        _A	  =    scope
        _A	  =    n_targets
        _A	  =    num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        _A	  =    (image_size[1] // patch_size) * (image_size[0] // patch_size)
        _A	  =    num_patches + 1 + self.num_detection_tokens



 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        _A	  =    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]				)

        _A	  =    None
        if self.use_labels:
               # labels is a list of Dict (each Dict being the labels for a given example in the batch)
               _A	  =    []
               for i in range(self.batch_size				):
                      _A	  =    {}
                      _A	  =    torch.randint(
                          high=self.num_labels						,							size=(self.n_targets,)						,							device=lowerCAmelCase_				)
                      _A	  =    torch.rand(self.n_targets						,							4						,							device=lowerCAmelCase_				)
                      labels.append(lowerCAmelCase_				)

        _A	  =    self.get_config()

        return config, pixel_values, labels



 def 	UpperCAmelCase				(   self				)    ->   Dict:
        return YolosConfig(
            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=lowerCAmelCase_						,							initializer_range=self.initializer_range						,							num_detection_tokens=self.num_detection_tokens						,							num_labels=self.num_labels						,							)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   Tuple:
        _A	  =    YolosModel(config=lowerCAmelCase_				)
        model.to(lowerCAmelCase_				)
        model.eval()
        _A	  =    model(lowerCAmelCase_				)
        self.parent.assertEqual(
            result.last_hidden_state.shape						,							(self.batch_size, self.expected_seq_len, self.hidden_size)				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   str:
        _A	  =    YolosForObjectDetection(lowerCAmelCase_				)
        model.to(lowerCAmelCase_				)
        model.eval()

        _A	  =    model(pixel_values=lowerCAmelCase_				)
        _A	  =    model(lowerCAmelCase_				)

        self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_detection_tokens, self.num_labels + 1)				)
        self.parent.assertEqual(result.pred_boxes.shape						,							(self.batch_size, self.num_detection_tokens, 4)				)

        _A	  =    model(pixel_values=lowerCAmelCase_						,							labels=lowerCAmelCase_				)

        self.parent.assertEqual(result.loss.shape						,							()				)
        self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_detection_tokens, self.num_labels + 1)				)
        self.parent.assertEqual(result.pred_boxes.shape						,							(self.batch_size, self.num_detection_tokens, 4)				)



 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        _A	  =    self.prepare_config_and_inputs()
        _A  ,    _A  ,    _A	  =    config_and_inputs
        _A	  =    {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class 							a				(     __lowerCAmelCase			,     __lowerCAmelCase			,     unittest.TestCase			):







 """simple docstring"""






 lowerCamelCase      :str            =						(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
 lowerCamelCase      :List[Any]            =						(
     {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
 )

 lowerCamelCase      :Tuple            =						False
 lowerCamelCase      :Dict            =						False
 lowerCamelCase      :List[Any]            =						False
 lowerCamelCase      :Any            =						False



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_=False				)    ->   str:
        _A	  =    super()._prepare_for_class(lowerCAmelCase_						,							lowerCAmelCase_						,							return_labels=lowerCAmelCase_				)

        if return_labels:
               if model_class.__name__ == "YolosForObjectDetection":
                      _A	  =    []
                      for i in range(self.model_tester.batch_size				):
                             _A	  =    {}
                             _A	  =    torch.ones(
                                 size=(self.model_tester.n_targets,)						,							device=lowerCAmelCase_						,							dtype=torch.long				)
                             _A	  =    torch.ones(
                                 self.model_tester.n_targets						,							4						,							device=lowerCAmelCase_						,							dtype=torch.float				)
                             labels.append(lowerCAmelCase_				)
                      _A	  =    labels

        return inputs_dict



 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    YolosModelTester(self				)
        _A	  =    ConfigTester(self						,							config_class=lowerCAmelCase_						,							has_text_modality=lowerCAmelCase_						,							hidden_size=37				)



 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        self.config_tester.run_common_tests()



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



 def 	UpperCAmelCase				(   self				)    ->   str:
        _A  ,    _A	  =    self.model_tester.prepare_config_and_inputs_for_common()

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



 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        _A  ,    _A	  =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A	  =    model_class(lowerCAmelCase_				)
               _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]						,							lowerCAmelCase_				)



 def 	UpperCAmelCase				(   self				)    ->   Dict:
        _A	  =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_				)



 def 	UpperCAmelCase				(   self				)    ->   Any:
        _A  ,    _A	  =    self.model_tester.prepare_config_and_inputs_for_common()
        _A	  =    True

        # in YOLOS, the seq_len is different
        _A	  =    self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
               _A	  =    True
               _A	  =    False
               _A	  =    True
               _A	  =    model_class(lowerCAmelCase_				)
               model.to(lowerCAmelCase_				)
               model.eval()
               with torch.no_grad():
                      _A	  =    model(**self._prepare_for_class(lowerCAmelCase_						,							lowerCAmelCase_				)				)
               _A	  =    outputs.attentions
               self.assertEqual(len(lowerCAmelCase_				)						,							self.model_tester.num_hidden_layers				)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _A	  =    True
               _A	  =    model_class(lowerCAmelCase_				)
               model.to(lowerCAmelCase_				)
               model.eval()
               with torch.no_grad():
                      _A	  =    model(**self._prepare_for_class(lowerCAmelCase_						,							lowerCAmelCase_				)				)
               _A	  =    outputs.attentions
               self.assertEqual(len(lowerCAmelCase_				)						,							self.model_tester.num_hidden_layers				)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]				)						,							[self.model_tester.num_attention_heads, seq_len, seq_len]						,							)
               _A	  =    len(lowerCAmelCase_				)

               # Check attention is always last and order is fine
               _A	  =    True
               _A	  =    True
               _A	  =    model_class(lowerCAmelCase_				)
               model.to(lowerCAmelCase_				)
               model.eval()
               with torch.no_grad():
                      _A	  =    model(**self._prepare_for_class(lowerCAmelCase_						,							lowerCAmelCase_				)				)

               _A	  =    1
               self.assertEqual(out_len + added_hidden_states						,							len(lowerCAmelCase_				)				)

               _A	  =    outputs.attentions

               self.assertEqual(len(lowerCAmelCase_				)						,							self.model_tester.num_hidden_layers				)
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]				)						,							[self.model_tester.num_attention_heads, seq_len, seq_len]						,							)



 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        def check_hidden_states_output(lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				):
               _A	  =    model_class(lowerCAmelCase_				)
               model.to(lowerCAmelCase_				)
               model.eval()

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

               _A	  =    outputs.hidden_states

               _A	  =    getattr(
                   self.model_tester						,							"""expected_num_hidden_layers"""						,							self.model_tester.num_hidden_layers + 1				)
               self.assertEqual(len(lowerCAmelCase_				)						,							lowerCAmelCase_				)

               # YOLOS has a different seq_length
               _A	  =    self.model_tester.expected_seq_len

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

        _A  ,    _A	  =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A	  =    True
               check_hidden_states_output(lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				)

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

               check_hidden_states_output(lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				)



 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        _A	  =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*lowerCAmelCase_				)



 @slow
 def 	UpperCAmelCase				(   self				)    ->   Dict:
        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A	  =    YolosModel.from_pretrained(lowerCAmelCase_				)
               self.assertIsNotNone(lowerCAmelCase_				)






def 					snake_case    (			)		->  Union[str, Any]:
       _A	  =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
       return image







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







 """simple docstring"""






 @cached_property
 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""				) if is_vision_available() else None



 @slow
 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        _A	  =    YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""				).to(lowerCAmelCase_				)

        _A	  =    self.default_image_processor
        _A	  =    prepare_img()
        _A	  =    image_processor(images=lowerCAmelCase_						,							return_tensors="""pt"""				).to(lowerCAmelCase_				)

        # forward pass
        with torch.no_grad():
               _A	  =    model(inputs.pixel_values				)

        # verify outputs
        _A	  =    torch.Size((1, 1_00, 92)				)
        self.assertEqual(outputs.logits.shape						,							lowerCAmelCase_				)

        _A	  =    torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]						,							device=lowerCAmelCase_						,							)
        _A	  =    torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]						,							device=lowerCAmelCase_				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]						,							lowerCAmelCase_						,							atol=1E-4				)				)
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]						,							lowerCAmelCase_						,							atol=1E-4				)				)

        # verify postprocessing
        _A	  =    image_processor.post_process_object_detection(
            lowerCAmelCase_						,							threshold=0.3						,							target_sizes=[image.size[::-1]]				)[0]
        _A	  =    torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]				).to(lowerCAmelCase_				)
        _A	  =    [75, 75, 17, 63, 17]
        _A	  =    torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]				).to(lowerCAmelCase_				)

        self.assertEqual(len(results["""scores"""]				)						,							5				)
        self.assertTrue(torch.allclose(results["""scores"""]						,							lowerCAmelCase_						,							atol=1E-4				)				)
        self.assertSequenceEqual(results["""labels"""].tolist()						,							lowerCAmelCase_				)
        self.assertTrue(torch.allclose(results["""boxes"""][0, :]						,							lowerCAmelCase_				)				)












import inspect
import unittest

from transformers import ConvNextConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
  from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]=13       ,				lowerCamelCase		:			Dict=32       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			List[Any]=4       ,				lowerCamelCase		:			Tuple=[10, 20, 30, 40]       ,				lowerCamelCase		:			str=[2, 2, 3, 2]       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			Union[str, Any]=37       ,				lowerCamelCase		:			Any="gelu"       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			Optional[Any]=0.02       ,				lowerCamelCase		:			Union[str, Any]=["stage2", "stage3", "stage4"]       ,				lowerCamelCase		:			Any=[2, 3, 4]       ,				lowerCamelCase		:			Tuple=None       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    num_stages
         _UpperCAmelCase							=    hidden_sizes
         _UpperCAmelCase							=    depths
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    out_features
         _UpperCAmelCase							=    out_indices
         _UpperCAmelCase							=    scope


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


         """simple docstring"""
         _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  lowerCamelCase       (      self		:			Optional[Any]							)						->	Any:


         """simple docstring"""
         return ConvNextConfig(
             num_channels=self.num_channels       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				num_stages=self.num_stages       ,				hidden_act=self.hidden_act       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,				)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextBackbone(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, self.hidden_sizes[1], 4, 4]							)

         # 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
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    ConvNextBackbone(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, self.hidden_sizes[-1], 1, 1]							)

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





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


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         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  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         return


  @unittest.skip(reason="""ConvNext does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Any:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not support input and output embeddings"""							)
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not use feedforward chunking"""							)
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _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  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         def check_hidden_states_output(lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                _UpperCAmelCase							=    self.model_tester.num_stages
                self.assertEqual(len(lowerCamelCase							)       ,				expected_num_stages + 1							)

                # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]							)       ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,				)

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

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

                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    ConvNextModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Tuple:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image
@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""							) if is_vision_available() else None





  @slow
  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _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, 1000)							)
         self.assertEqual(outputs.logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([-0.0260, -0.4739, 0.1911]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase					,      UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (ConvNextBackbone,) if is_torch_available() else ()
  _lowerCamelCase    =  ConvNextConfig

  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)