import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
						from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor






class 		_lowerCamelCase		:
				def __init__(  self , lowerCAmelCase , lowerCAmelCase=2 , lowerCAmelCase=32 , lowerCAmelCase=16 , lowerCAmelCase=3 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=32 , lowerCAmelCase=4 , lowerCAmelCase=[0, 1, 2, 3] , lowerCAmelCase=4 , lowerCAmelCase=37 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=0.02 , lowerCAmelCase=3 , lowerCAmelCase=[1, 384, 24, 24] , lowerCAmelCase=True , lowerCAmelCase=None , )       ->					Any:
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      parent
					SCREAMING_SNAKE_CASE__:   Optional[int]=      batch_size
					SCREAMING_SNAKE_CASE__:   List[Any]=      image_size
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      patch_size
					SCREAMING_SNAKE_CASE__:   Dict=      num_channels
					SCREAMING_SNAKE_CASE__:   int=      is_training
					SCREAMING_SNAKE_CASE__:   List[Any]=      use_labels
					SCREAMING_SNAKE_CASE__:   Tuple=      hidden_size
					SCREAMING_SNAKE_CASE__:   List[Any]=      num_hidden_layers
					SCREAMING_SNAKE_CASE__:   List[str]=      backbone_out_indices
					SCREAMING_SNAKE_CASE__:   int=      num_attention_heads
					SCREAMING_SNAKE_CASE__:   List[Any]=      intermediate_size
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      hidden_act
					SCREAMING_SNAKE_CASE__:   Any=      hidden_dropout_prob
					SCREAMING_SNAKE_CASE__:   Tuple=      attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE__:   int=      initializer_range
					SCREAMING_SNAKE_CASE__:   Tuple=      num_labels
					SCREAMING_SNAKE_CASE__:   Optional[Any]=      backbone_featmap_shape
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      scope
					SCREAMING_SNAKE_CASE__:   List[Any]=      is_hybrid
					# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      (image_size // patch_size) ** 2
					SCREAMING_SNAKE_CASE__:   Any=      num_patches + 1
				def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
					SCREAMING_SNAKE_CASE__:   Optional[int]=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

					SCREAMING_SNAKE_CASE__:   List[str]=      None
					if self.use_labels:
						SCREAMING_SNAKE_CASE__:   Tuple=      ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels   )

					SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_config()

					return config, pixel_values, labels
				def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
					SCREAMING_SNAKE_CASE__:   Dict=      {
					    '''global_padding''': '''same''',
					    '''layer_type''': '''bottleneck''',
					    '''depths''': [3, 4, 9],
					    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
					    '''embedding_dynamic_padding''': True,
					    '''hidden_sizes''': [96, 192, 384, 768],
					    '''num_groups''': 2,
					}

					return DPTConfig(
					    image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCAmelCase , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=lowerCAmelCase , backbone_featmap_shape=self.backbone_featmap_shape , )
				def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Optional[int]:
					SCREAMING_SNAKE_CASE__:   Tuple=      DPTModel(config=lowerCAmelCase   )
					model.to(lowerCAmelCase   )
					model.eval()
					SCREAMING_SNAKE_CASE__:   List[Any]=      model(lowerCAmelCase   )
					self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)   )
				def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					List[str]:
					SCREAMING_SNAKE_CASE__:   Optional[int]=      self.num_labels
					SCREAMING_SNAKE_CASE__:   int=      DPTForDepthEstimation(lowerCAmelCase   )
					model.to(lowerCAmelCase   )
					model.eval()
					SCREAMING_SNAKE_CASE__:   str=      model(lowerCAmelCase   )
					self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size)   )
				def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
					SCREAMING_SNAKE_CASE__:   Any=      self.num_labels
					SCREAMING_SNAKE_CASE__:   Optional[Any]=      DPTForSemanticSegmentation(lowerCAmelCase   )
					model.to(lowerCAmelCase   )
					model.eval()
					SCREAMING_SNAKE_CASE__:   Dict=      model(lowerCAmelCase , labels=lowerCAmelCase   )
					self.parent.assertEqual(
					    result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size)   )



				def 						UpperCamelCase_				(  self   )       ->					str:
					SCREAMING_SNAKE_CASE__:   List[Any]=      self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE__:   Optional[int]=      config_and_inputs
					SCREAMING_SNAKE_CASE__:   int=      {'''pixel_values''': pixel_values}
					return config, inputs_dict






@require_torch
class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
				__a  =      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				__a  =      (
				    {
				        "depth-estimation": DPTForDepthEstimation,
				        "feature-extraction": DPTModel,
				        "image-segmentation": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__a  =      False
				__a  =      False
				__a  =      False
				def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
					SCREAMING_SNAKE_CASE__:   Any=      DPTModelTester(self   )
					SCREAMING_SNAKE_CASE__:   Tuple=      ConfigTester(self , config_class=lowerCAmelCase , has_text_modality=lowerCAmelCase , hidden_size=37   )
				def 						UpperCamelCase_				(  self   )       ->					Tuple:
					self.config_tester.run_common_tests()
				@unittest.skip(reason='''DPT does not use inputs_embeds'''   )
				def 						UpperCamelCase_				(  self   )       ->					int:
					pass
				def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
					SCREAMING_SNAKE_CASE__:   int=      self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE__:   List[str]=      model_class(lowerCAmelCase   )
						self.assertIsInstance(model.get_input_embeddings() , (nn.Module)   )
						SCREAMING_SNAKE_CASE__:   Tuple=      model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(lowerCAmelCase , nn.Linear   )   )
				def 						UpperCamelCase_				(  self   )       ->					Any:
					SCREAMING_SNAKE_CASE__:   Optional[int]=      self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE__:   List[Any]=      model_class(lowerCAmelCase   )
						SCREAMING_SNAKE_CASE__:   Optional[int]=      inspect.signature(model.forward   )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						SCREAMING_SNAKE_CASE__:   Any=      [*signature.parameters.keys()]

						SCREAMING_SNAKE_CASE__:   List[Any]=      ['''pixel_values''']
						self.assertListEqual(arg_names[:1] , lowerCAmelCase   )
				def 						UpperCamelCase_				(  self   )       ->					Dict:
					SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase   )
				def 						UpperCamelCase_				(  self   )       ->					Any:
					SCREAMING_SNAKE_CASE__:   Dict=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase   )
				def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase   )
				def 						UpperCamelCase_				(  self   )       ->					Dict:
					for model_class in self.all_model_classes:
						if model_class.__name__ == "DPTForDepthEstimation":
							continue

						SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__:   Tuple=      True

						if model_class in get_values(lowerCAmelCase   ):
							continue

						SCREAMING_SNAKE_CASE__:   Optional[int]=      model_class(lowerCAmelCase   )
						model.to(lowerCAmelCase   )
						model.train()
						SCREAMING_SNAKE_CASE__:   Dict=      self._prepare_for_class(lowerCAmelCase , lowerCAmelCase , return_labels=lowerCAmelCase   )
						SCREAMING_SNAKE_CASE__:   List[Any]=      model(**lowerCAmelCase   ).loss
						loss.backward()
				def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
					for model_class in self.all_model_classes:
						if model_class.__name__ == "DPTForDepthEstimation":
							continue

						SCREAMING_SNAKE_CASE__:   str=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__:   Dict=      False
						SCREAMING_SNAKE_CASE__:   Tuple=      True

						if model_class in get_values(lowerCAmelCase   ) or not model_class.supports_gradient_checkpointing:
							continue
						SCREAMING_SNAKE_CASE__:   Optional[int]=      model_class(lowerCAmelCase   )
						model.to(lowerCAmelCase   )
						model.gradient_checkpointing_enable()
						model.train()
						SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self._prepare_for_class(lowerCAmelCase , lowerCAmelCase , return_labels=lowerCAmelCase   )
						SCREAMING_SNAKE_CASE__:   Optional[int]=      model(**lowerCAmelCase   ).loss
						loss.backward()
				def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
					SCREAMING_SNAKE_CASE__:   str=      self.model_tester.prepare_config_and_inputs_for_common()

					SCREAMING_SNAKE_CASE__:   int=      _config_zero_init(lowerCAmelCase   )
					for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE__:   Optional[int]=      model_class(config=lowerCAmelCase   )
						# Skip the check for the backbone
						SCREAMING_SNAKE_CASE__:   Dict=      []
						for name, module in model.named_modules():
							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
								SCREAMING_SNAKE_CASE__:   Optional[int]=      [f'{name}.{key}' for key in module.state_dict().keys()]
								break

						for name, param in model.named_parameters():
							if param.requires_grad:
								if name in backbone_params:
									continue
								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' , )
				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
				def 						UpperCamelCase_				(  self   )       ->					int:
					pass
				@slow
				def 						UpperCamelCase_				(  self   )       ->					Any:
					for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
						SCREAMING_SNAKE_CASE__:   Optional[int]=      DPTModel.from_pretrained(lowerCAmelCase   )
						self.assertIsNotNone(lowerCAmelCase   )



				def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
					# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
					SCREAMING_SNAKE_CASE__:   Tuple=      self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE__:   List[str]=      '''add'''
					with self.assertRaises(lowerCAmelCase   ):
						SCREAMING_SNAKE_CASE__:   int=      DPTForDepthEstimation(lowerCAmelCase   )







def 			A__      (	):
	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
	return image






@require_torch
@require_vision
@slow
class 		_lowerCamelCase		(   unittest.TestCase    ):
				def 						UpperCamelCase_				(  self   )       ->					str:
					SCREAMING_SNAKE_CASE__:   Dict=      DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''   )
					SCREAMING_SNAKE_CASE__:   List[Any]=      DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''   ).to(lowerCAmelCase   )

					SCREAMING_SNAKE_CASE__:   Dict=      prepare_img()
					SCREAMING_SNAKE_CASE__:   Optional[int]=      image_processor(images=lowerCAmelCase , return_tensors='''pt'''   ).to(lowerCAmelCase   )

					# forward pass
					with torch.no_grad():
						SCREAMING_SNAKE_CASE__:   Tuple=      model(**lowerCAmelCase   )
						SCREAMING_SNAKE_CASE__:   Tuple=      outputs.predicted_depth

					# verify the predicted depth
					SCREAMING_SNAKE_CASE__:   Any=      torch.Size((1, 384, 384)   )
					self.assertEqual(predicted_depth.shape , lowerCAmelCase   )

					SCREAMING_SNAKE_CASE__:   Tuple=      torch.tensor(
					    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(lowerCAmelCase   )

					self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 , lowerCAmelCase , atol=1e-4   )   )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase_     :   List[str] 				=				{
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   Optional[Any] 				=				['MaskFormerFeatureExtractor']
      lowercase_     :   Dict 				=				['MaskFormerImageProcessor']


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MaskFormerForInstanceSegmentation',
          'MaskFormerModel',
          'MaskFormerPreTrainedModel',
      ]
      lowercase_     :   Optional[Any] 				=				[
          'MaskFormerSwinBackbone',
          'MaskFormerSwinModel',
          'MaskFormerSwinPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
      from .configuration_maskformer_swin import MaskFormerSwinConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_maskformer import MaskFormerFeatureExtractor
            from .image_processing_maskformer import MaskFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_maskformer import (
                MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaskFormerForInstanceSegmentation,
                MaskFormerModel,
                MaskFormerPreTrainedModel,
            )
            from .modeling_maskformer_swin import (
                MaskFormerSwinBackbone,
                MaskFormerSwinModel,
                MaskFormerSwinPreTrainedModel,
            )


else:
      import sys

      lowercase_     :   Any 				=				_LazyModule(__name__, globals()['__file__'], _import_structure)

