


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		,				_UpperCAmelCase		,				_UpperCAmelCase=13		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=16		,				_UpperCAmelCase=[32, 64, 1_28]		,				_UpperCAmelCase=[1, 2, 1]		,				_UpperCAmelCase=[2, 2, 4]		,				_UpperCAmelCase=2		,				_UpperCAmelCase=2.0		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-5		,				_UpperCAmelCase=True		,				_UpperCAmelCase=None		,				_UpperCAmelCase=True		,				_UpperCAmelCase=10		,				_UpperCAmelCase=8		,				_UpperCAmelCase=["stage1", "stage2"]		,				_UpperCAmelCase=[1, 2]		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				image_size
       snake_case_	=				patch_size
       snake_case_	=				num_channels
       snake_case_	=				embed_dim
       snake_case_	=				hidden_sizes
       snake_case_	=				depths
       snake_case_	=				num_heads
       snake_case_	=				window_size
       snake_case_	=				mlp_ratio
       snake_case_	=				qkv_bias
       snake_case_	=				hidden_dropout_prob
       snake_case_	=				attention_probs_dropout_prob
       snake_case_	=				drop_path_rate
       snake_case_	=				hidden_act
       snake_case_	=				use_absolute_embeddings
       snake_case_	=				patch_norm
       snake_case_	=				layer_norm_eps
       snake_case_	=				initializer_range
       snake_case_	=				is_training
       snake_case_	=				scope
       snake_case_	=				use_labels
       snake_case_	=				type_sequence_label_size
       snake_case_	=				encoder_stride
       snake_case_	=				out_features
       snake_case_	=				out_indices



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

       snake_case_	=				self.get_config()

       return config, pixel_values, labels



 def 						UpperCamelCase__					(     self ):
       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		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				FocalNetModel(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )

       snake_case_	=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
       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		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				FocalNetBackbone(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )

       # 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
       snake_case_	=				None
       snake_case_	=				FocalNetBackbone(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )

       # 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		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				FocalNetForMaskedImageModeling(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )
       self.parent.assertEqual(
           result.reconstruction.shape		,				(self.batch_size, self.num_channels, self.image_size, self.image_size) )

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

       snake_case_	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
       snake_case_	=				model(_UpperCAmelCase )
       self.parent.assertEqual(result.reconstruction.shape		,				(self.batch_size, 1, self.image_size, self.image_size) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.type_sequence_label_size
       snake_case_	=				FocalNetForImageClassification(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase		,				labels=_UpperCAmelCase )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size) )

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

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





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()

       snake_case_	,   snake_case_	,   snake_case_	=				config_and_inputs
       snake_case_	=				{'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

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

 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				FocalNetModelTester(self )
       snake_case_	=				ConfigTester(self		,				config_class=_UpperCAmelCase		,				embed_dim=37		,				has_text_modality=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()



 def 						UpperCamelCase__					(     self ):
       return



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )



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



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



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes[:-1]:
             snake_case_	=				model_class(_UpperCAmelCase )
             self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module) )
             snake_case_	=				model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCAmelCase		,				nn.Linear ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case_	=				['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				model_class(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()

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

       snake_case_	=				outputs.hidden_states

       snake_case_	=				getattr(
           self.model_tester		,				'''expected_num_hidden_layers'''		,				len(self.model_tester.depths ) + 1 )
       self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

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

       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]		,				)

       snake_case_	=				outputs.reshaped_hidden_states
       self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

       snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				reshaped_hidden_states[0].shape
       snake_case_	=				(
           reshaped_hidden_states[0].view(_UpperCAmelCase		,				_UpperCAmelCase		,				height * width ).permute(0		,				2		,				1 )
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:] )		,				[num_patches, self.model_tester.embed_dim]		,				)



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

       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]:
             snake_case_	=				True
             self.check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

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

             self.check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_	=				3

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

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

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

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             snake_case_	=				True
             self.check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				(padded_height, padded_width) )



 @slow
 def 						UpperCamelCase__					(     self ):
       for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_	=				FocalNetModel.from_pretrained(_UpperCAmelCase )
             self.assertIsNotNone(_UpperCAmelCase )





 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

       snake_case_	=				_config_zero_init(_UpperCAmelCase )
       for model_class in self.all_model_classes:
             snake_case_	=				model_class(config=_UpperCAmelCase )
             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 ):
       # TODO update organization
       return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny''' ) if is_vision_available() else None





 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''' ).to(_UpperCAmelCase )
       snake_case_	=				self.default_image_processor

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

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

       # verify the logits
       snake_case_	=				torch.Size((1, 10_00) )
       self.assertEqual(outputs.logits.shape		,				_UpperCAmelCase )
       snake_case_	=				torch.tensor([0.2_166, -0.4_368, 0.2_191] ).to(_UpperCAmelCase )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				_UpperCAmelCase		,				atol=1E-4 ) )
       self.assertTrue(outputs.logits.argmax(dim=-1 ).item()		,				2_81 )







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

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

 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				FocalNetModelTester(self )



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


UpperCAmelCase      =   logging.get_logger(__name__)

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







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "convnextv2"



 def __init__(     self		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=4		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-12		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=2_24		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase )

       snake_case_	=				num_channels
       snake_case_	=				patch_size
       snake_case_	=				num_stages
       snake_case_	=				[96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
       snake_case_	=				[3, 3, 9, 3] if depths is None else depths
       snake_case_	=				hidden_act
       snake_case_	=				initializer_range
       snake_case_	=				layer_norm_eps
       snake_case_	=				drop_path_rate
       snake_case_	=				image_size
       snake_case_	=				['''stem'''] + [F'''stage{idx}''' for idx in range(1		,				len(self.depths ) + 1 )]
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(
           out_features=_UpperCAmelCase		,				out_indices=_UpperCAmelCase		,				stage_names=self.stage_names )