





'''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 __a :





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

      '''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[int]    ):

      '''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]    ):

      '''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[int]				,lowerCamelCase				:			int				,lowerCamelCase				:			Dict				,lowerCamelCase				:			Dict    ):

      '''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				:			List[str]				,lowerCamelCase				:			int				,lowerCamelCase				:			Optional[Any]				,lowerCamelCase				:			int    ):

      '''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]				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Dict				,lowerCamelCase				:			Optional[int]    ):

      '''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				:			Tuple				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Union[str, Any]				,lowerCamelCase				:			List[str]    ):

      '''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[Any]    ):

      '''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 __a (   snake_case__,   snake_case__,   unittest.TestCase			):
   __UpperCamelCase   : int	      =	(
       (
           FocalNetModel,
           FocalNetForImageClassification,
           FocalNetForMaskedImageModeling,
           FocalNetBackbone,
       )
       if is_torch_available()
       else ()
   )
   __UpperCamelCase   : Optional[Any]	      =	(
       {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
       if is_torch_available()
       else {}
   )
   __UpperCamelCase   : Optional[int]	      =	False

   __UpperCamelCase   : Dict	      =	False
   __UpperCamelCase   : str	      =	False
   __UpperCamelCase   : Optional[Any]	      =	False
   __UpperCamelCase   : str	      =	False





   def      UpperCAmelCase__     (					self				:			int    ):

      '''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				:			Optional[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      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      return





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''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				:			Dict    ):

      '''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				:			Optional[Any]    ):

      '''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				:			Any    ):

      '''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				:			Tuple    ):

      '''simple docstring'''

      pass





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

      '''simple docstring'''

      pass





   def      UpperCAmelCase__     (					self				:			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[int]    ):

      '''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				:			Optional[int]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Optional[Any]				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Optional[Any]    ):

      '''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				:			Union[str, Any]    ):

      '''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				:			Optional[Any]    ):

      '''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				:			str    ):

      '''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				:			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 __a (   unittest.TestCase			):





   @cached_property
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

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





   @slow
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''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, 1000)    )
      self.assertEqual(outputs.logits.shape				,_SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE      							=       torch.tensor([0.2_166, -0.4_368, 0.2_191]    ).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 __a (   snake_case__,   unittest.TestCase			):
   __UpperCamelCase   : List[str]	      =	(FocalNetBackbone,) if is_torch_available() else ()
   __UpperCamelCase   : Optional[Any]	      =	FocalNetConfig

   __UpperCamelCase   : List[Any]	      =	False





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       FocalNetModelTester(self    )












'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a        =						logging.get_logger(__name__)

a        =						{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class __a (   _snake_case			):
   __UpperCamelCase   : Dict	      =	'deta'
   __UpperCamelCase   : List[str]	      =	{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }





   def __init__(					self				:			Tuple				,lowerCamelCase				:			List[Any]=None				,lowerCamelCase				:			Any=900				,lowerCamelCase				:			int=2048				,lowerCamelCase				:			Any=6				,lowerCamelCase				:			Optional[Any]=2048				,lowerCamelCase				:			str=8				,lowerCamelCase				:			Union[str, Any]=6				,lowerCamelCase				:			List[str]=1024				,lowerCamelCase				:			int=8				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Optional[int]="relu"				,lowerCamelCase				:			int=256				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Optional[Any]=0.0				,lowerCamelCase				:			Tuple=0.0				,lowerCamelCase				:			List[str]=0.02				,lowerCamelCase				:			Any=1.0				,lowerCamelCase				:			Optional[int]=True				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Optional[Any]="sine"				,lowerCamelCase				:			Dict=5				,lowerCamelCase				:			List[Any]=4				,lowerCamelCase				:			Optional[Any]=4				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			int=300				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Tuple=True				,lowerCamelCase				:			int=1				,lowerCamelCase				:			Tuple=5				,lowerCamelCase				:			Union[str, Any]=2				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			int=1				,lowerCamelCase				:			str=5				,lowerCamelCase				:			Optional[Any]=2				,lowerCamelCase				:			List[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.25				,**lowerCamelCase				:			int				,):

      '''simple docstring'''

      if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
         __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]    )
      else:
         if isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       backbone_config.pop("""model_type"""    )
            __SCREAMING_SNAKE_CASE      							=       CONFIG_MAPPING[backbone_model_type]
            __SCREAMING_SNAKE_CASE      							=       config_class.from_dict(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       backbone_config
      __SCREAMING_SNAKE_CASE      							=       num_queries
      __SCREAMING_SNAKE_CASE      							=       max_position_embeddings
      __SCREAMING_SNAKE_CASE      							=       d_model
      __SCREAMING_SNAKE_CASE      							=       encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       encoder_layers
      __SCREAMING_SNAKE_CASE      							=       encoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       decoder_layers
      __SCREAMING_SNAKE_CASE      							=       decoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_function
      __SCREAMING_SNAKE_CASE      							=       init_std
      __SCREAMING_SNAKE_CASE      							=       init_xavier_std
      __SCREAMING_SNAKE_CASE      							=       encoder_layerdrop
      __SCREAMING_SNAKE_CASE      							=       auxiliary_loss
      __SCREAMING_SNAKE_CASE      							=       position_embedding_type
      # deformable attributes
      __SCREAMING_SNAKE_CASE      							=       num_feature_levels
      __SCREAMING_SNAKE_CASE      							=       encoder_n_points
      __SCREAMING_SNAKE_CASE      							=       decoder_n_points
      __SCREAMING_SNAKE_CASE      							=       two_stage
      __SCREAMING_SNAKE_CASE      							=       two_stage_num_proposals
      __SCREAMING_SNAKE_CASE      							=       with_box_refine
      __SCREAMING_SNAKE_CASE      							=       assign_first_stage
      if two_stage is True and with_box_refine is False:
         raise ValueError("""If two_stage is True, with_box_refine must be True."""    )
      # Hungarian matcher
      __SCREAMING_SNAKE_CASE      							=       class_cost
      __SCREAMING_SNAKE_CASE      							=       bbox_cost
      __SCREAMING_SNAKE_CASE      							=       giou_cost
      # Loss coefficients
      __SCREAMING_SNAKE_CASE      							=       mask_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       dice_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       bbox_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       giou_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       eos_coefficient
      __SCREAMING_SNAKE_CASE      							=       focal_alpha
      super().__init__(is_encoder_decoder=lowerCamelCase				,**lowerCamelCase    )





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return self.encoder_attention_heads





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return self.d_model





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       copy.deepcopy(self.__dict__    )
      __SCREAMING_SNAKE_CASE      							=       self.backbone_config.to_dict()
      __SCREAMING_SNAKE_CASE      							=       self.__class__.model_type
      return output





