
"""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 			__magic_name__       :
       def __init__(     self ,				A_ ,				A_=13 ,				A_=32 ,				A_=2 ,				A_=3 ,				A_=16 ,				A_=[32, 64, 128] ,				A_=[1, 2, 1] ,				A_=[2, 2, 4] ,				A_=2 ,				A_=2.0 ,				A_=True ,				A_=0.0 ,				A_=0.0 ,				A_=0.1 ,				A_="gelu" ,				A_=False ,				A_=True ,				A_=0.02 ,				A_=1E-5 ,				A_=True ,				A_=None ,				A_=True ,				A_=10 ,				A_=8 ,				A_=["stage1", "stage2"] ,				A_=[1, 2] ,				)     ->	Any:





         """simple docstring"""




         _lowercase:			List[str]       =     parent
         _lowercase:			Dict       =     batch_size
         _lowercase:			Optional[Any]       =     image_size
         _lowercase:			int       =     patch_size
         _lowercase:			Union[str, Any]       =     num_channels
         _lowercase:			int       =     embed_dim
         _lowercase:			Optional[Any]       =     hidden_sizes
         _lowercase:			List[Any]       =     depths
         _lowercase:			List[Any]       =     num_heads
         _lowercase:			Dict       =     window_size
         _lowercase:			Dict       =     mlp_ratio
         _lowercase:			Union[str, Any]       =     qkv_bias
         _lowercase:			str       =     hidden_dropout_prob
         _lowercase:			Union[str, Any]       =     attention_probs_dropout_prob
         _lowercase:			List[Any]       =     drop_path_rate
         _lowercase:			Optional[Any]       =     hidden_act
         _lowercase:			Optional[Any]       =     use_absolute_embeddings
         _lowercase:			str       =     patch_norm
         _lowercase:			Union[str, Any]       =     layer_norm_eps
         _lowercase:			List[str]       =     initializer_range
         _lowercase:			str       =     is_training
         _lowercase:			List[Any]       =     scope
         _lowercase:			int       =     use_labels
         _lowercase:			Union[str, Any]       =     type_sequence_label_size
         _lowercase:			Optional[Any]       =     encoder_stride
         _lowercase:			Union[str, Any]       =     out_features
         _lowercase:			Tuple       =     out_indices
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Any       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         _lowercase:			List[Any]       =     None
         if self.use_labels:
           _lowercase:			Dict       =     ids_tensor([self.batch_size] ,				self.type_sequence_label_size       )

         _lowercase:			Any       =     self.get_config()

         return config, pixel_values, labels
       def 							lowercase_							(     self       )     ->	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 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			Optional[int]       =     FocalNetModel(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Tuple       =     model(A_       )

         _lowercase:			Optional[int]       =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
         _lowercase:			List[Any]       =     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 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Tuple       =     FocalNetBackbone(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Union[str, Any]       =     model(A_       )

         # 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
         _lowercase:			Dict       =     None
         _lowercase:			Union[str, Any]       =     FocalNetBackbone(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Tuple       =     model(A_       )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps       ) ,				1       )
         self.parent.assertListEqual(list(result.feature_maps[0].shape       ) ,				[self.batch_size, 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 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Dict       =     FocalNetForMaskedImageModeling(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			str       =     model(A_       )
         self.parent.assertEqual(
             result.reconstruction.shape ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

         # test greyscale images
         _lowercase:			List[Any]       =     1
         _lowercase:			Any       =     FocalNetForMaskedImageModeling(A_       )
         model.to(A_       )
         model.eval()

         _lowercase:			List[str]       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         _lowercase:			Dict       =     model(A_       )
         self.parent.assertEqual(result.reconstruction.shape ,				(self.batch_size, 1, self.image_size, self.image_size)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	str:





         """simple docstring"""




         _lowercase:			Dict       =     self.type_sequence_label_size
         _lowercase:			Tuple       =     FocalNetForImageClassification(A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Dict       =     model(A_ ,				labels=A_       )
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         _lowercase:			List[str]       =     1
         _lowercase:			Tuple       =     FocalNetForImageClassification(A_       )
         model.to(A_       )
         model.eval()

         _lowercase:			List[str]       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         _lowercase:			List[str]       =     model(A_       )
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)       )



       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			Tuple       =     self.prepare_config_and_inputs()

         _lowercase:			Tuple       =     config_and_inputs
         _lowercase:			Optional[int]       =     {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		(
           (
               FocalNetModel,
               FocalNetForImageClassification,
               FocalNetForMaskedImageModeling,
               FocalNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase_   =		(
           {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
           if is_torch_available()
           else {}
       )
       UpperCamelCase_   =		False

       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			List[Any]       =     FocalNetModelTester(self       )
         _lowercase:			Tuple       =     ConfigTester(self ,				config_class=A_ ,				embed_dim=37 ,				has_text_modality=A_       )
       def 							lowercase_							(     self       )     ->	Union[str, 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 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         return
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			str       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*A_       )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Any       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*A_       )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_       )
       @unittest.skip(reason='''FocalNet does not use inputs_embeds'''       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         pass
       @unittest.skip(reason='''FocalNet does not use feedforward chunking'''       )
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




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





         """simple docstring"""




         _lowercase:			List[Any]       =     self.model_tester.prepare_config_and_inputs_for_common()

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





         """simple docstring"""




         _lowercase:			str       =     self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowercase:			Optional[int]       =     ['''pixel_values''']
           self.assertListEqual(arg_names[:1] ,				A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     model_class(A_       )
         model.to(A_       )
         model.eval()

         with torch.no_grad():
           _lowercase:			Optional[Any]       =     model(**self._prepare_for_class(A_ ,				A_       )       )

         _lowercase:			Optional[Any]       =     outputs.hidden_states

         _lowercase:			List[str]       =     getattr(
             self.model_tester ,				'''expected_num_hidden_layers''' ,				len(self.model_tester.depths       ) + 1       )
         self.assertEqual(len(A_       ) ,				A_       )

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

         _lowercase:			Union[str, Any]       =     (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] ,				)

         _lowercase:			Optional[Any]       =     outputs.reshaped_hidden_states
         self.assertEqual(len(A_       ) ,				A_       )

         _lowercase:			Optional[int]       =     reshaped_hidden_states[0].shape
         _lowercase:			Tuple       =     (
             reshaped_hidden_states[0].view(A_ ,				A_ ,				height * width       ).permute(0 ,				2 ,				1       )
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]       ) ,				[num_patches, self.model_tester.embed_dim] ,				)
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Dict       =     self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase:			List[Any]       =     (
             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]:
           _lowercase:			List[str]       =     True
           self.check_hidden_states_output(A_ ,				A_ ,				A_ ,				A_       )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase:			str       =     True

           self.check_hidden_states_output(A_ ,				A_ ,				A_ ,				A_       )
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			Any       =     self.model_tester.prepare_config_and_inputs_for_common()
         _lowercase:			Optional[Any]       =     3

         _lowercase:			Optional[int]       =     (
             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)
         )
         _lowercase:			Union[str, Any]       =     (
             config.patch_size
             if isinstance(config.patch_size ,				collections.abc.Iterable       )
             else (config.patch_size, config.patch_size)
         )

         _lowercase:			Union[str, Any]       =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         _lowercase:			Tuple       =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase:			List[str]       =     True
           self.check_hidden_states_output(A_ ,				A_ ,				A_ ,				(padded_height, padded_width)       )
       @slow
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:			Optional[Any]       =     FocalNetModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )



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





         """simple docstring"""




         _lowercase:			Tuple       =     self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase:			List[str]       =     _config_zero_init(A_       )
         for model_class in self.all_model_classes:
           _lowercase:			Any       =     model_class(config=A_       )
           for name, param in model.named_parameters():
             if "embeddings" not in name and param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1E9).round() / 1E9).item() ,				[0.0, 1.0] ,				msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,				)


@require_vision
@require_torch
class 			__magic_name__       (       unittest.TestCase ):
       @cached_property
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




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



       @slow
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''       ).to(A_       )
         _lowercase:			Optional[int]       =     self.default_image_processor

         _lowercase:			Optional[Any]       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         _lowercase:			List[str]       =     image_processor(images=A_ ,				return_tensors='''pt'''       ).to(A_       )

         # forward pass
         with torch.no_grad():
           _lowercase:			str       =     model(**A_       )

         # verify the logits
         _lowercase:			Any       =     torch.Size((1, 1000)       )
         self.assertEqual(outputs.logits.shape ,				A_       )
         _lowercase:			str       =     torch.tensor([0.21_66, -0.43_68, 0.21_91]       ).to(A_       )
         self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				A_ ,				atol=1E-4       )       )
         self.assertTrue(outputs.logits.argmax(dim=-1       ).item() ,				281       )


@require_torch
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		(FocalNetBackbone,) if is_torch_available() else ()
       UpperCamelCase_   =		FocalNetConfig

       UpperCamelCase_   =		False
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			int       =     FocalNetModelTester(self       )




"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


A__   :					Dict							=   logging.get_logger(__name__)

A__   :					int							=   {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''dpt'''
       def __init__(     self ,				A_=768 ,				A_=12 ,				A_=12 ,				A_=3072 ,				A_="gelu" ,				A_=0.0 ,				A_=0.0 ,				A_=0.02 ,				A_=1E-12 ,				A_=384 ,				A_=16 ,				A_=3 ,				A_=False ,				A_=True ,				A_=[2, 5, 8, 11] ,				A_="project" ,				A_=[4, 2, 1, 0.5] ,				A_=[96, 192, 384, 768] ,				A_=256 ,				A_=-1 ,				A_=False ,				A_=True ,				A_=0.4 ,				A_=255 ,				A_=0.1 ,				A_=[1, 1024, 24, 24] ,				A_=[0, 1] ,				A_=None ,				**A_ ,				)     ->	int:





         """simple docstring"""




         super().__init__(**A_       )

         _lowercase:			Union[str, Any]       =     hidden_size
         _lowercase:			str       =     is_hybrid

         if self.is_hybrid:
           if backbone_config is None:
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }
             _lowercase:			List[str]       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             _lowercase:			str       =     backbone_config
           else:
             raise ValueError(
                 f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

           _lowercase:			Dict       =     backbone_featmap_shape
           _lowercase:			Optional[int]       =     neck_ignore_stages

           if readout_type != "project":
             raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''       )
         else:
           _lowercase:			Any       =     None
           _lowercase:			str       =     None
           _lowercase:			Optional[Any]       =     []

         _lowercase:			Tuple       =     num_hidden_layers
         _lowercase:			Optional[int]       =     num_attention_heads
         _lowercase:			Any       =     intermediate_size
         _lowercase:			List[Any]       =     hidden_act
         _lowercase:			Union[str, Any]       =     hidden_dropout_prob
         _lowercase:			Tuple       =     attention_probs_dropout_prob
         _lowercase:			Dict       =     initializer_range
         _lowercase:			Dict       =     layer_norm_eps
         _lowercase:			Any       =     image_size
         _lowercase:			Optional[int]       =     patch_size
         _lowercase:			Optional[int]       =     num_channels
         _lowercase:			int       =     qkv_bias
         _lowercase:			List[str]       =     backbone_out_indices
         if readout_type not in ["ignore", "add", "project"]:
           raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''       )
         _lowercase:			Union[str, Any]       =     readout_type
         _lowercase:			Optional[Any]       =     reassemble_factors
         _lowercase:			List[str]       =     neck_hidden_sizes
         _lowercase:			Tuple       =     fusion_hidden_size
         _lowercase:			int       =     head_in_index
         _lowercase:			Optional[int]       =     use_batch_norm_in_fusion_residual
         # auxiliary head attributes (semantic segmentation)
         _lowercase:			int       =     use_auxiliary_head
         _lowercase:			Dict       =     auxiliary_loss_weight
         _lowercase:			List[Any]       =     semantic_loss_ignore_index
         _lowercase:			Dict       =     semantic_classifier_dropout



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[int]       =     copy.deepcopy(self.__dict__       )

         if output["backbone_config"] is not None:
           _lowercase:			List[str]       =     self.backbone_config.to_dict()

         _lowercase:			List[Any]       =     self.__class__.model_type
         return output
