import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()







class        UpperCAmelCase_					(				unittest.TestCase     ):
    UpperCAmelCase_      :							Optional[Any]    			=				'hf-internal-testing/tiny-stable-diffusion-pipe'
    UpperCAmelCase_      :							Any    			=				1
    UpperCAmelCase_      :							Union[str, Any]    			=				77
    UpperCAmelCase_      :							Tuple    			=				32
    UpperCAmelCase_      :							int    			=				4
    UpperCAmelCase_      :							Union[str, Any]    			=				64
    UpperCAmelCase_      :							Optional[Any]    			=				torch.manual_seed(0     )




    def     __lowerCamelCase			(       self	: Dict   ,   lowerCAmelCase	: List[str]=0.9_999 )						->			Any:
      a__  		=  UNetaDConditionModel.from_pretrained(self.model_id   ,   subfolder="unet" )
      a__  		=  unet.to(lowerCAmelCase )
      a__  		=  EMAModel(unet.parameters()   ,   decay=lowerCAmelCase   ,   model_cls=lowerCAmelCase   ,   model_config=unet.config )
      return unet, ema_unet




    def     __lowerCamelCase			(       self	: List[str] )						->			str:
      a__  		=  torch.randn(
          self.batch_size   ,   self.num_in_channels   ,   self.latent_height   ,   self.latent_width   ,   generator=self.generator ).to(lowerCAmelCase )
      a__  		=  torch.randint(0   ,   1000   ,   size=(self.batch_size,)   ,   generator=self.generator ).to(lowerCAmelCase )
      a__  		=  torch.randn(
          self.batch_size   ,   self.prompt_length   ,   self.text_encoder_hidden_dim   ,   generator=self.generator ).to(lowerCAmelCase )
      return noisy_latents, timesteps, encoder_hidden_states




    def     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: Union[str, Any] )						->			int:
      a__  		=  {}
      for k, param in unet.state_dict().items():
        a__  		=  torch.randn_like(lowerCAmelCase ) + (param * torch.randn_like(lowerCAmelCase ))
        updated_state_dict.update({k: updated_param} )
      unet.load_state_dict(lowerCAmelCase )
      return unet




    def     __lowerCamelCase			(       self	: int )						->			Optional[Any]:
      a__		,a__  		=  self.get_models()
      # Take the first (hypothetical) EMA step.
      ema_unet.step(unet.parameters() )
      assert ema_unet.optimization_step == 1

      # Take two more.
      for _ in range(2 ):
        ema_unet.step(unet.parameters() )
      assert ema_unet.optimization_step == 3




    def     __lowerCamelCase			(       self	: int )						->			List[Any]:
      a__		,a__  		=  self.get_models()
      # Since the `unet` is not being updated (i.e., backprop'd)
      # there won't be any difference between the `params` of `unet`
      # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
      ema_unet.step(unet.parameters() )
      a__  		=  list(unet.parameters() )
      for s_param, param in zip(ema_unet.shadow_params   ,   lowerCAmelCase ):
        assert torch.allclose(lowerCAmelCase   ,   lowerCAmelCase )

      # The above holds true even if we call `ema.step()` multiple times since
      # `unet` params are still not being updated.
      for _ in range(4 ):
        ema_unet.step(unet.parameters() )
      for s_param, param in zip(ema_unet.shadow_params   ,   lowerCAmelCase ):
        assert torch.allclose(lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Optional[Any] )						->			List[Any]:
      a__		,a__  		=  self.get_models()
      # Here we simulate the parameter updates for `unet`. Since there might
      # be some parameters which are initialized to zero we take extra care to
      # initialize their values to something non-zero before the multiplication.
      a__  		=  self.simulate_backprop(lowerCAmelCase )

      # Take the EMA step.
      ema_unet.step(unet_pseudo_updated_step_one.parameters() )

      # Now the EMA'd parameters won't be equal to the original model parameters.
      a__  		=  list(unet_pseudo_updated_step_one.parameters() )
      for s_param, param in zip(ema_unet.shadow_params   ,   lowerCAmelCase ):
        assert ~torch.allclose(lowerCAmelCase   ,   lowerCAmelCase )

      # Ensure this is the case when we take multiple EMA steps.
      for _ in range(4 ):
        ema_unet.step(unet.parameters() )
      for s_param, param in zip(ema_unet.shadow_params   ,   lowerCAmelCase ):
        assert ~torch.allclose(lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Dict )						->			Any:
      # If we call EMA step after a backpropagation consecutively for two times,
      # the shadow params from those two steps should be different.
      a__		,a__  		=  self.get_models()

      # First backprop + EMA
      a__  		=  self.simulate_backprop(lowerCAmelCase )
      ema_unet.step(unet_step_one.parameters() )
      a__  		=  ema_unet.shadow_params

      # Second backprop + EMA
      a__  		=  self.simulate_backprop(lowerCAmelCase )
      ema_unet.step(unet_step_two.parameters() )
      a__  		=  ema_unet.shadow_params

      for step_one, step_two in zip(lowerCAmelCase   ,   lowerCAmelCase ):
        assert ~torch.allclose(lowerCAmelCase   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: str )						->			str:
      # If there's no decay even if there are backprops, EMA steps
      # won't take any effect i.e., the shadow params would remain the
      # same.
      a__		,a__  		=  self.get_models(decay=0.0 )
      a__  		=  self.simulate_backprop(lowerCAmelCase )
      ema_unet.step(unet_step_one.parameters() )
      a__  		=  ema_unet.shadow_params

      a__  		=  self.simulate_backprop(lowerCAmelCase )
      ema_unet.step(unet_step_two.parameters() )
      a__  		=  ema_unet.shadow_params

      for step_one, step_two in zip(lowerCAmelCase   ,   lowerCAmelCase ):
        assert torch.allclose(lowerCAmelCase   ,   lowerCAmelCase )




    @skip_mps
    def     __lowerCamelCase			(       self	: Any )						->			int:
      a__		,a__  		=  self.get_models()
      a__		,a__		,a__  		=  self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdir:
        ema_unet.save_pretrained(lowerCAmelCase )
        a__  		=  UNetaDConditionModel.from_pretrained(lowerCAmelCase   ,   model_cls=lowerCAmelCase )
        a__  		=  loaded_unet.to(unet.device )

      # Since no EMA step has been performed the outputs should match.
      a__  		=  unet(lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase ).sample
      a__  		=  loaded_unet(lowerCAmelCase   ,   lowerCAmelCase   ,   lowerCAmelCase ).sample

      assert torch.allclose(lowerCAmelCase   ,   lowerCAmelCase   ,   atol=1e-4 )


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        UpperCAmelCase_					:




    def __init__(       self	: int   ,   lowerCAmelCase	: Tuple   ,   lowerCAmelCase	: Dict=2   ,   lowerCAmelCase	: Dict=32   ,   lowerCAmelCase	: Optional[int]=16   ,   lowerCAmelCase	: Tuple=3   ,   lowerCAmelCase	: Tuple=True   ,   lowerCAmelCase	: str=True   ,   lowerCAmelCase	: int=32   ,   lowerCAmelCase	: int=4   ,   lowerCAmelCase	: Tuple=[0, 1, 2, 3]   ,   lowerCAmelCase	: Dict=4   ,   lowerCAmelCase	: Optional[int]=37   ,   lowerCAmelCase	: Tuple="gelu"   ,   lowerCAmelCase	: Optional[int]=0.1   ,   lowerCAmelCase	: Dict=0.1   ,   lowerCAmelCase	: Tuple=0.02   ,   lowerCAmelCase	: List[str]=3   ,   lowerCAmelCase	: Dict=False   ,   lowerCAmelCase	: str=None   ,   )						->			Tuple:
      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__  		=  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     __lowerCamelCase			(       self	: List[Any] )						->			str:
      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     __lowerCamelCase			(       self	: List[Any] )						->			Union[str, Any]:
      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   ,   )




    def     __lowerCamelCase			(       self	: Optional[int]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Optional[Any] )						->			Optional[Any]:
      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     __lowerCamelCase			(       self	: Union[str, Any]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: str )						->			Optional[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     __lowerCamelCase			(       self	: str   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Optional[int] )						->			List[str]:
      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     __lowerCamelCase			(       self	: int )						->			Any:
      a__  		=  self.prepare_config_and_inputs()
      a__		,a__		,a__  		=  config_and_inputs
      a__  		=  {"pixel_values": pixel_values}
      return config, inputs_dict







@require_torch
class        UpperCAmelCase_					(				_a  ,  _a  ,  unittest.TestCase     ):
    UpperCAmelCase_      :							int    			=				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    UpperCAmelCase_      :							str    			=				(
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase_      :							List[str]    			=				False
    UpperCAmelCase_      :							int    			=				False
    UpperCAmelCase_      :							Optional[Any]    			=				False




    def     __lowerCamelCase			(       self	: List[Any] )						->			Any:
      a__  		=  DPTModelTester(self )
      a__  		=  ConfigTester(self   ,   config_class=lowerCAmelCase   ,   has_text_modality=lowerCAmelCase   ,   hidden_size=37 )




    def     __lowerCamelCase			(       self	: int )						->			List[Any]:
      self.config_tester.run_common_tests()




    @unittest.skip(reason="DPT does not use inputs_embeds" )
    def     __lowerCamelCase			(       self	: str )						->			Dict:
      pass




    def     __lowerCamelCase			(       self	: List[str] )						->			int:
      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     __lowerCamelCase			(       self	: Union[str, Any] )						->			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     __lowerCamelCase			(       self	: Optional[int] )						->			Optional[Any]:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Tuple )						->			List[str]:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: List[Any] )						->			Any:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Tuple )						->			Tuple:
      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     __lowerCamelCase			(       self	: Any )						->			Dict:
      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     __lowerCamelCase			(       self	: List[str] )						->			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     __lowerCamelCase			(       self	: List[str] )						->			Union[str, Any]:
      pass




    @slow
    def     __lowerCamelCase			(       self	: List[str] )						->			Dict:
      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a__  		=  DPTModel.from_pretrained(lowerCAmelCase )
        self.assertIsNotNone(lowerCAmelCase )

def 					__lowercase						(    )					->int:

  '''simple docstring'''




  a__  		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
  return image







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




    def     __lowerCamelCase			(       self	: Any )						->			Union[str, Any]:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large" )
      a__  		=  DPTForDepthEstimation.from_pretrained("Intel/dpt-large" ).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, 384, 384) )
      self.assertEqual(predicted_depth.shape   ,   lowerCAmelCase )

      a__  		=  torch.tensor(
          [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] ).to(lowerCAmelCase )

      self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,   lowerCAmelCase   ,   atol=1e-4 ) )




    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			str:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
      a__  		=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(lowerCAmelCase )

      a__  		=  prepare_img()
      a__  		=  image_processor(images=lowerCAmelCase   ,   return_tensors="pt" ).to(lowerCAmelCase )

      # forward pass
      with torch.no_grad():
        a__  		=  model(**lowerCAmelCase )

      # verify the logits
      a__  		=  torch.Size((1, 150, 480, 480) )
      self.assertEqual(outputs.logits.shape   ,   lowerCAmelCase )

      a__  		=  torch.tensor(
          [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] ).to(lowerCAmelCase )

      self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,   lowerCAmelCase   ,   atol=1e-4 ) )




    def     __lowerCamelCase			(       self	: str )						->			Optional[int]:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
      a__  		=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).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.logits.detach().cpu()

      a__  		=  image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase   ,   target_sizes=[(500, 300)] )
      a__  		=  torch.Size((500, 300) )
      self.assertEqual(segmentation[0].shape   ,   lowerCAmelCase )

      a__  		=  image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase )
      a__  		=  torch.Size((480, 480) )
      self.assertEqual(segmentation[0].shape   ,   lowerCAmelCase )

