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 				a							:







 """simple docstring"""






 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, 3_84, 24, 24]		,				lowerCAmelCase_=True		,				lowerCAmelCase_=None		,				)  ->					Optional[int]:
        _A        =  parent
        _A        =  batch_size
        _A        =  image_size
        _A        =  patch_size
        _A        =  num_channels
        _A        =  is_training
        _A        =  use_labels
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  backbone_out_indices
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  initializer_range
        _A        =  num_labels
        _A        =  backbone_featmap_shape
        _A        =  scope
        _A        =  is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        _A        =  (image_size // patch_size) ** 2
        _A        =  num_patches + 1



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        _A        =  None
        if self.use_labels:
               _A        =  ids_tensor([self.batch_size, self.image_size, self.image_size]		,				self.num_labels							)

        _A        =  self.get_config()

        return config, pixel_values, labels



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  {
            """global_padding""": """same""",
            """layer_type""": """bottleneck""",
            """depths""": [3, 4, 9],
            """out_features""": ["""stage1""", """stage2""", """stage3"""],
            """embedding_dynamic_padding""": True,
            """hidden_sizes""": [96, 1_92, 3_84, 7_68],
            """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_							)  ->					int:
        _A        =  DPTModel(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  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[Any]:
        _A        =  self.num_labels
        _A        =  DPTForDepthEstimation(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  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:
        _A        =  self.num_labels
        _A        =  DPTForSemanticSegmentation(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  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							)  ->					int:
        _A        =  self.prepare_config_and_inputs()
        _A   ,  _A   ,  _A        =  config_and_inputs
        _A        =  {"""pixel_values""": pixel_values}
        return config, inputs_dict



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







 """simple docstring"""






 lowerCamelCase	:List[Any]    			=		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 lowerCamelCase	:Any    			=		(
     {
         '''depth-estimation''': DPTForDepthEstimation,
         '''feature-extraction''': DPTModel,
         '''image-segmentation''': DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowerCamelCase	:str    			=		False
 lowerCamelCase	:List[Any]    			=		False
 lowerCamelCase	:int    			=		False



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  DPTModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=lowerCAmelCase_		,				has_text_modality=lowerCAmelCase_		,				hidden_size=37							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        self.config_tester.run_common_tests()



 @unittest.skip(reason="""DPT does not use inputs_embeds"""							)
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        pass



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module)							)
               _A        =  model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase_		,				nn.Linear							)							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

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

               _A        =  ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        for model_class in self.all_model_classes:
               if model_class.__name__ == "DPTForDepthEstimation":
                      continue

               _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()
               _A        =  True

               if model_class in get_values(lowerCAmelCase_							):
                      continue

               _A        =  model_class(lowerCAmelCase_							)
               model.to(lowerCAmelCase_							)
               model.train()
               _A        =  self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_		,				return_labels=lowerCAmelCase_							)
               _A        =  model(**lowerCAmelCase_							).loss
               loss.backward()



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        for model_class in self.all_model_classes:
               if model_class.__name__ == "DPTForDepthEstimation":
                      continue

               _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()
               _A        =  False
               _A        =  True

               if model_class in get_values(lowerCAmelCase_							) or not model_class.supports_gradient_checkpointing:
                      continue
               _A        =  model_class(lowerCAmelCase_							)
               model.to(lowerCAmelCase_							)
               model.gradient_checkpointing_enable()
               model.train()
               _A        =  self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_		,				return_labels=lowerCAmelCase_							)
               _A        =  model(**lowerCAmelCase_							).loss
               loss.backward()



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        _A        =  _config_zero_init(lowerCAmelCase_							)
        for model_class in self.all_model_classes:
               _A        =  model_class(config=lowerCAmelCase_							)
               # Skip the check for the backbone
               _A        =  []
               for name, module in model.named_modules():
                      if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                             _A        =  [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							)  ->					Any:
        pass



 @slow
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
               _A        =  DPTModel.from_pretrained(lowerCAmelCase_							)
               self.assertIsNotNone(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()
        _A        =  """add"""
        with self.assertRaises(lowerCAmelCase_							):
               _A        =  DPTForDepthEstimation(lowerCAmelCase_							)






def  snake_case  (       )						->   Dict:
       _A        =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
       return image







@require_torch
@require_vision
@slow
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""							)
        _A        =  DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""							).to(lowerCAmelCase_							)

        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

        # forward pass
        with torch.no_grad():
               _A        =  model(**lowerCAmelCase_							)
               _A        =  outputs.predicted_depth

        # verify the predicted depth
        _A        =  torch.Size((1, 3_84, 3_84)							)
        self.assertEqual(predicted_depth.shape		,				lowerCAmelCase_							)

        _A        =  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] / 1_00		,				lowerCAmelCase_		,				atol=1E-4							)							)





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor



class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=7		,				lowerCAmelCase_=3		,				lowerCAmelCase_=18		,				lowerCAmelCase_=30		,				lowerCAmelCase_=4_00		,				lowerCAmelCase_=True		,				lowerCAmelCase_=None		,				lowerCAmelCase_=True		,				lowerCAmelCase_=False		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=[0.5, 0.5, 0.5]		,				lowerCAmelCase_=[0.5, 0.5, 0.5]		,				)  ->					Optional[Any]:
        _A        =  parent
        _A        =  batch_size
        _A        =  num_channels
        _A        =  image_size
        _A        =  min_resolution
        _A        =  max_resolution
        _A        =  do_resize
        _A        =  size if size is not None else {"""height""": 18, """width""": 20}
        _A        =  do_thumbnail
        _A        =  do_align_axis
        _A        =  do_pad
        _A        =  do_normalize
        _A        =  image_mean
        _A        =  image_std



 def      UpperCAmelCase							(					self							)  ->					str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }



@require_torch
@require_vision
class 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:int    			=		DonutImageProcessor if is_vision_available() else None



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  DonutImageProcessingTester(self							)



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_resize"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""size"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_thumbnail"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_align_long_axis"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_pad"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""do_normalize"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""image_mean"""							)							)
        self.assertTrue(hasattr(lowerCAmelCase_		,				"""image_std"""							)							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,				{"""height""": 18, """width""": 20}							)

        _A        =  self.image_processing_class.from_dict(self.image_processor_dict		,				size=42							)
        self.assertEqual(image_processor.size		,				{"""height""": 42, """width""": 42}							)

        # Previous config had dimensions in (width, height) order
        _A        =  self.image_processing_class.from_dict(self.image_processor_dict		,				size=(42, 84)							)
        self.assertEqual(image_processor.size		,				{"""height""": 84, """width""": 42}							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        pass



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				Image.Image							)

        # Test not batched input
        _A        =  image_processing(image_inputs[0]		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)

        # Test batched
        _A        =  image_processing(lowerCAmelCase_		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_		,				numpify=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				np.ndarray							)

        # Test not batched input
        _A        =  image_processing(image_inputs[0]		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)

        # Test batched
        _A        =  image_processing(lowerCAmelCase_		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)



 @is_flaky()
 def      UpperCAmelCase							(					self							)  ->					int:
        # Initialize image_processing
        _A        =  self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        _A        =  prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase_		,				torchify=lowerCAmelCase_							)
        for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase_		,				torch.Tensor							)

        # Test not batched input
        _A        =  image_processing(image_inputs[0]		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)

        # Test batched
        _A        =  image_processing(lowerCAmelCase_		,				return_tensors="""pt"""							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )		,				)




