

'''simple docstring'''



import collections
import inspect
import unittest

from transformers import FocalNetConfig
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, _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 (
      FocalNetBackbone,
      FocalNetForImageClassification,
      FocalNetForMaskedImageModeling,
      FocalNetModel,
  )
  from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str=13					,							__SCREAMING_SNAKE_CASE					:       str=32					,							__SCREAMING_SNAKE_CASE					:       Tuple=2					,							__SCREAMING_SNAKE_CASE					:       List[str]=3					,							__SCREAMING_SNAKE_CASE					:       List[str]=16					,							__SCREAMING_SNAKE_CASE					:       Any=[32, 64, 128]					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=[1, 2, 1]					,							__SCREAMING_SNAKE_CASE					:       Any=[2, 2, 4]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=2					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=2.0					,							__SCREAMING_SNAKE_CASE					:       str=True					,							__SCREAMING_SNAKE_CASE					:       Dict=0.0					,							__SCREAMING_SNAKE_CASE					:       str=0.0					,							__SCREAMING_SNAKE_CASE					:       int=0.1					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]="gelu"					,							__SCREAMING_SNAKE_CASE					:       Any=False					,							__SCREAMING_SNAKE_CASE					:       str=True					,							__SCREAMING_SNAKE_CASE					:       List[str]=0.02					,							__SCREAMING_SNAKE_CASE					:       List[str]=1E-5					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=True					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=True					,							__SCREAMING_SNAKE_CASE					:       str=10					,							__SCREAMING_SNAKE_CASE					:       int=8					,							__SCREAMING_SNAKE_CASE					:       List[str]=["stage1", "stage2"]					,							__SCREAMING_SNAKE_CASE					:       Tuple=[1, 2]					,							)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      embed_dim
      __SCREAMING_SNAKE_CASE     =      hidden_sizes
      __SCREAMING_SNAKE_CASE     =      depths
      __SCREAMING_SNAKE_CASE     =      num_heads
      __SCREAMING_SNAKE_CASE     =      window_size
      __SCREAMING_SNAKE_CASE     =      mlp_ratio
      __SCREAMING_SNAKE_CASE     =      qkv_bias
      __SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
      __SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      use_absolute_embeddings
      __SCREAMING_SNAKE_CASE     =      patch_norm
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      initializer_range
      __SCREAMING_SNAKE_CASE     =      is_training
      __SCREAMING_SNAKE_CASE     =      scope
      __SCREAMING_SNAKE_CASE     =      use_labels
      __SCREAMING_SNAKE_CASE     =      type_sequence_label_size
      __SCREAMING_SNAKE_CASE     =      encoder_stride
      __SCREAMING_SNAKE_CASE     =      out_features
      __SCREAMING_SNAKE_CASE     =      out_indices
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      __SCREAMING_SNAKE_CASE     =      None
      if self.use_labels:
           __SCREAMING_SNAKE_CASE     =      ids_tensor([self.batch_size]					,							self.type_sequence_label_size			)

      __SCREAMING_SNAKE_CASE     =      self.get_config()

      return config, pixel_values, labels
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




      return FocalNetConfig(
          image_size=self.image_size					,							patch_size=self.patch_size					,							num_channels=self.num_channels					,							embed_dim=self.embed_dim					,							hidden_sizes=self.hidden_sizes					,							depths=self.depths					,							num_heads=self.num_heads					,							window_size=self.window_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					,							use_absolute_embeddings=self.use_absolute_embeddings					,							path_norm=self.patch_norm					,							layer_norm_eps=self.layer_norm_eps					,							initializer_range=self.initializer_range					,							encoder_stride=self.encoder_stride					,							out_features=self.out_features					,							out_indices=self.out_indices					,							)
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      FocalNetModel(config=__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
      __SCREAMING_SNAKE_CASE     =      int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

      self.parent.assertEqual(result.last_hidden_state.shape					,							(self.batch_size, expected_seq_len, expected_dim)			)
 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      FocalNetBackbone(config=__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps			)					,							len(config.out_features			)			)
      self.parent.assertListEqual(list(result.feature_maps[0].shape			)					,							[self.batch_size, self.image_size, 8, 8]			)

      # 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
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      FocalNetBackbone(config=__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      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, self.image_size * 2, 4, 4]			)

      # verify channels
      self.parent.assertEqual(len(model.channels			)					,							1			)
      self.parent.assertListEqual(model.channels					,							[config.hidden_sizes[-1]]			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       int			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      FocalNetForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)
      self.parent.assertEqual(
          result.reconstruction.shape					,							(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

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

      __SCREAMING_SNAKE_CASE     =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE			)
      self.parent.assertEqual(result.reconstruction.shape					,							(self.batch_size, 1, self.image_size, self.image_size)			)
 def       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       List[str]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.type_sequence_label_size
      __SCREAMING_SNAKE_CASE     =      FocalNetForImageClassification(__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()
      __SCREAMING_SNAKE_CASE     =      model(__SCREAMING_SNAKE_CASE					,							labels=__SCREAMING_SNAKE_CASE			)
      self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.type_sequence_label_size)			)

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

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


 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.prepare_config_and_inputs()

      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      config_and_inputs
      __SCREAMING_SNAKE_CASE     =      {"""pixel_values""": pixel_values}
      return config, inputs_dict







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



 """simple docstring"""



 lowerCAmelCase__  					=      (
     (
         FocalNetModel,
         FocalNetForImageClassification,
         FocalNetForMaskedImageModeling,
         FocalNetBackbone,
     )
     if is_torch_available()
     else ()
 )
 lowerCAmelCase__  					=      (
     {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
     if is_torch_available()
     else {}
 )
 lowerCAmelCase__  					=      False

 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      False
 lowerCAmelCase__  					=      False
 def       UpperCAmelCase__		(		self					:       str			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      FocalNetModelTester(self			)
      __SCREAMING_SNAKE_CASE     =      ConfigTester(self					,							config_class=__SCREAMING_SNAKE_CASE					,							embed_dim=37					,							has_text_modality=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       str			)  -> Optional[Any]:


      """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       UpperCAmelCase__		(		self					:       str			)  -> Any:


      """simple docstring"""




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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       int			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE			)
 @unittest.skip(reason="""FocalNet does not use inputs_embeds"""			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




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


      """simple docstring"""




      pass
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:-1]:
           __SCREAMING_SNAKE_CASE     =      model_class(__SCREAMING_SNAKE_CASE			)
           self.assertIsInstance(model.get_input_embeddings()					,							(nn.Module)			)
           __SCREAMING_SNAKE_CASE     =      model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE					,							nn.Linear			)			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:-1]:
           __SCREAMING_SNAKE_CASE     =      model_class(__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      inspect.signature(model.forward			)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           __SCREAMING_SNAKE_CASE     =      [*signature.parameters.keys()]

           __SCREAMING_SNAKE_CASE     =      ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      model_class(__SCREAMING_SNAKE_CASE			)
      model.to(__SCREAMING_SNAKE_CASE			)
      model.eval()

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

      __SCREAMING_SNAKE_CASE     =      outputs.hidden_states

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

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

      __SCREAMING_SNAKE_CASE     =      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

      __SCREAMING_SNAKE_CASE     =      outputs.reshaped_hidden_states
      self.assertEqual(len(__SCREAMING_SNAKE_CASE			)					,							__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      reshaped_hidden_states[0].shape
      __SCREAMING_SNAKE_CASE     =      (
          reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							height * width			).permute(0					,							2					,							1			)
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]			)					,							[num_patches, self.model_tester.embed_dim]					,							)
 def       UpperCAmelCase__		(		self					:       int			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_common()

      __SCREAMING_SNAKE_CASE     =      (
          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[:-1]:
           __SCREAMING_SNAKE_CASE     =      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"]
           __SCREAMING_SNAKE_CASE     =      True

           self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE     =      3

      __SCREAMING_SNAKE_CASE     =      (
          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)
      )
      __SCREAMING_SNAKE_CASE     =      (
          config.patch_size
          if isinstance(config.patch_size					,							collections.abc.Iterable			)
          else (config.patch_size, config.patch_size)
      )

      __SCREAMING_SNAKE_CASE     =      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      __SCREAMING_SNAKE_CASE     =      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes[:-1]:
           __SCREAMING_SNAKE_CASE     =      True
           self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							(padded_height, padded_width)			)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __SCREAMING_SNAKE_CASE     =      True
           self.check_hidden_states_output(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							(padded_height, padded_width)			)
 @slow
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Dict:


      """simple docstring"""




      for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE     =      FocalNetModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs_for_common()

      __SCREAMING_SNAKE_CASE     =      _config_zero_init(__SCREAMING_SNAKE_CASE			)
      for model_class in self.all_model_classes:
           __SCREAMING_SNAKE_CASE     =      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_vision
@require_torch
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 @cached_property
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[int]:


      """simple docstring"""




      return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""			) if is_vision_available() else None


 @slow
 def       UpperCAmelCase__		(		self					:       int			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""			).to(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      self.default_image_processor

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

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

      # verify the logits
      __SCREAMING_SNAKE_CASE     =      torch.Size((1, 1_000)			)
      self.assertEqual(outputs.logits.shape					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      torch.tensor([0.2166, -0.4368, 0.2191]			).to(__SCREAMING_SNAKE_CASE			)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]					,							__SCREAMING_SNAKE_CASE					,							atol=1E-4			)			)
      self.assertTrue(outputs.logits.argmax(dim=-1			).item()					,							281			)







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



 """simple docstring"""



 lowerCAmelCase__  					=      (FocalNetBackbone,) if is_torch_available() else ()
 lowerCAmelCase__  					=      FocalNetConfig

 lowerCAmelCase__  					=      False
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      FocalNetModelTester(self			)









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase :       Optional[int]			   =     logging.get_logger(__name__)

UpperCAmelCase :       str			   =     {
    'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "convnextv2"
 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       int=3					,							__SCREAMING_SNAKE_CASE					:       Dict=4					,							__SCREAMING_SNAKE_CASE					:       List[Any]=4					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Optional[int]="gelu"					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=0.02					,							__SCREAMING_SNAKE_CASE					:       Dict=1E-12					,							__SCREAMING_SNAKE_CASE					:       List[str]=0.0					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=224					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							__SCREAMING_SNAKE_CASE					:       List[str]=None					,							**__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							)  -> Union[str, Any]:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_stages
      __SCREAMING_SNAKE_CASE     =      [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      __SCREAMING_SNAKE_CASE     =      [3, 3, 9, 3] if depths is None else depths
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      initializer_range
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(self.depths			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






