


"""simple docstring"""





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 						snake_case_:






     def __init__(		self     :    List[Any]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Any=2    , UpperCamelCase_     :    Union[str, Any]=3_2    , UpperCamelCase_     :    int=1_6    , UpperCamelCase_     :    str=3    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    Optional[Any]=3_2    , UpperCamelCase_     :    Optional[int]=4    , UpperCamelCase_     :    Tuple=[0, 1, 2, 3]    , UpperCamelCase_     :    Dict=4    , UpperCamelCase_     :    Union[str, Any]=3_7    , UpperCamelCase_     :    Optional[Any]="gelu"    , UpperCamelCase_     :    Dict=0.1    , UpperCamelCase_     :    Tuple=0.1    , UpperCamelCase_     :    Optional[Any]=0.02    , UpperCamelCase_     :    List[str]=3    , UpperCamelCase_     :    Any=[1, 3_8_4, 2_4, 2_4]    , UpperCamelCase_     :    Union[str, Any]=True    , UpperCamelCase_     :    Tuple=None    , ):
           lowerCAmelCase    :		Optional[Any]				=      parent
           lowerCAmelCase    :		Dict				=      batch_size
           lowerCAmelCase    :		Any				=      image_size
           lowerCAmelCase    :		List[Any]				=      patch_size
           lowerCAmelCase    :		Dict				=      num_channels
           lowerCAmelCase    :		Optional[Any]				=      is_training
           lowerCAmelCase    :		List[Any]				=      use_labels
           lowerCAmelCase    :		List[Any]				=      hidden_size
           lowerCAmelCase    :		Any				=      num_hidden_layers
           lowerCAmelCase    :		Optional[Any]				=      backbone_out_indices
           lowerCAmelCase    :		Union[str, Any]				=      num_attention_heads
           lowerCAmelCase    :		Optional[int]				=      intermediate_size
           lowerCAmelCase    :		List[Any]				=      hidden_act
           lowerCAmelCase    :		List[Any]				=      hidden_dropout_prob
           lowerCAmelCase    :		Dict				=      attention_probs_dropout_prob
           lowerCAmelCase    :		List[str]				=      initializer_range
           lowerCAmelCase    :		Tuple				=      num_labels
           lowerCAmelCase    :		Any				=      backbone_featmap_shape
           lowerCAmelCase    :		List[str]				=      scope
           lowerCAmelCase    :		List[Any]				=      is_hybrid
           # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
           lowerCAmelCase    :		Any				=      (image_size // patch_size) ** 2
           lowerCAmelCase    :		List[str]				=      num_patches + 1






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		str				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

           lowerCAmelCase    :		Tuple				=      self.get_config()

           return config, pixel_values, labels






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		str				=      {
               '''global_padding''': '''same''',
               '''layer_type''': '''bottleneck''',
               '''depths''': [3, 4, 9],
               '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
               '''embedding_dynamic_padding''': True,
               '''hidden_sizes''': [9_6, 1_9_2, 3_8_4, 7_6_8],
               '''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=UpperCamelCase_    , initializer_range=self.initializer_range    , is_hybrid=self.is_hybrid    , backbone_config=UpperCamelCase_    , backbone_featmap_shape=self.backbone_featmap_shape    , )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    int    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Dict ):
           lowerCAmelCase    :		Optional[Any]				=      DPTModel(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           lowerCAmelCase    :		Dict				=      model(UpperCamelCase_ )
           self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size) )






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    List[str] ):
           lowerCAmelCase    :		Dict				=      self.num_labels
           lowerCAmelCase    :		List[Any]				=      DPTForDepthEstimation(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           lowerCAmelCase    :		int				=      model(UpperCamelCase_ )
           self.parent.assertEqual(result.predicted_depth.shape    , (self.batch_size, self.image_size, self.image_size) )






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Optional[Any] ):
           lowerCAmelCase    :		Optional[int]				=      self.num_labels
           lowerCAmelCase    :		Optional[int]				=      DPTForSemanticSegmentation(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           lowerCAmelCase    :		int				=      model(UpperCamelCase_    , labels=UpperCamelCase_ )
           self.parent.assertEqual(
               result.logits.shape    , (self.batch_size, self.num_labels, self.image_size, self.image_size) )






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		str				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		int				=      config_and_inputs
           lowerCAmelCase    :		Any				=      {'''pixel_values''': pixel_values}
           return config, inputs_dict



@require_torch
class 						snake_case_(				a__							,    a__							,    unittest.TestCase	):
     __UpperCamelCase     =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     __UpperCamelCase     =    (
         {
             '''depth-estimation''': DPTForDepthEstimation,
             '''feature-extraction''': DPTModel,
             '''image-segmentation''': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __UpperCamelCase     =    False
     __UpperCamelCase     =    False
     __UpperCamelCase     =    False






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Any				=      DPTModelTester(self )
           lowerCAmelCase    :		str				=      ConfigTester(self    , config_class=UpperCamelCase_    , has_text_modality=UpperCamelCase_    , hidden_size=3_7 )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           self.config_tester.run_common_tests()






     @unittest.skip(reason='''DPT does not use inputs_embeds''' )
     def    lowerCamelCase__   (		self     :    int ):
           pass






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase,					lowerCAmelCase    :		str				=      self.model_tester.prepare_config_and_inputs_for_common()

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






     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase,					lowerCAmelCase    :		Any				=      self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCAmelCase    :		List[str]				=      ['''pixel_values''']
                 self.assertListEqual(arg_names[:1]    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Optional[int]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		List[str]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Dict				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_ )






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

                 lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      self.model_tester.prepare_config_and_inputs_for_common()
                 lowerCAmelCase    :		Optional[int]				=      True

                 if model_class in get_values(UpperCamelCase_ ):
                       continue

                 lowerCAmelCase    :		List[str]				=      model_class(UpperCamelCase_ )
                 model.to(UpperCamelCase_ )
                 model.train()
                 lowerCAmelCase    :		Dict				=      self._prepare_for_class(UpperCamelCase_    , UpperCamelCase_    , return_labels=UpperCamelCase_ )
                 lowerCAmelCase    :		int				=      model(**UpperCamelCase_ ).loss
                 loss.backward()






     def    lowerCamelCase__   (		self     :    int ):
           for model_class in self.all_model_classes:
                 if model_class.__name__ == "DPTForDepthEstimation":
                       continue

                 lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      self.model_tester.prepare_config_and_inputs_for_common()
                 lowerCAmelCase    :		Optional[int]				=      False
                 lowerCAmelCase    :		Optional[int]				=      True

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






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      self.model_tester.prepare_config_and_inputs_for_common()

           lowerCAmelCase    :		Dict				=      _config_zero_init(UpperCamelCase_ )
           for model_class in self.all_model_classes:
                 lowerCAmelCase    :		int				=      model_class(config=UpperCamelCase_ )
                 # Skip the check for the backbone
                 lowerCAmelCase    :		Tuple				=      []
                 for name, module in model.named_modules():
                       if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                             lowerCAmelCase    :		Any				=      [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] ):
           pass






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                 lowerCAmelCase    :		Any				=      DPTModel.from_pretrained(UpperCamelCase_ )
                 self.assertIsNotNone(UpperCamelCase_ )






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



def    _snake_case  (  ):
      lowerCAmelCase    :		List[Any]				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
      return image






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






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Tuple				=      DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''' )
           lowerCAmelCase    :		Optional[Any]				=      DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''' ).to(UpperCamelCase_ )

           lowerCAmelCase    :		str				=      prepare_img()
           lowerCAmelCase    :		int				=      image_processor(images=UpperCamelCase_    , return_tensors='''pt''' ).to(UpperCamelCase_ )

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase    :		List[Any]				=      model(**UpperCamelCase_ )
                 lowerCAmelCase    :		Tuple				=      outputs.predicted_depth

           # verify the predicted depth
           lowerCAmelCase    :		List[Any]				=      torch.Size((1, 3_8_4, 3_8_4) )
           self.assertEqual(predicted_depth.shape    , UpperCamelCase_ )

           lowerCAmelCase    :		Dict				=      torch.tensor(
               [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]] ).to(UpperCamelCase_ )

           self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0    , UpperCamelCase_    , atol=1E-4 ) )






"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           lowerCAmelCase    :		Any				=      '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

           lowerCAmelCase    :		Dict				=      (image / 2 + 0.5).clamp(0    , 1 )
           lowerCAmelCase    :		Dict				=      image.cpu().permute(0    , 2    , 3    , 1 ).numpy()
           if output_type == "pil":
                 lowerCAmelCase    :		List[str]				=      self.numpy_to_pil(UpperCamelCase_ )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


