




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      Optional[int]		 =     KandinskyInpaintPipeline
     a		:      Union[str, Any]		 =     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
     a		:      int		 =     [
         """prompt""",
         """negative_prompt""",
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
         """mask_image""",
     ]
     a		:      Optional[int]		 =     [
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     a		:      List[str]		 =     False







     @property
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          return 32







     @property
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          return 32







     @property
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          return self.time_input_dim







     @property
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.time_input_dim * 4







     @property
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          return 100







     @property
     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
          return tokenizer







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          torch.manual_seed(0			)
          a     =				MCLIPConfig(
              numDims=self.cross_attention_dim  ,		transformerDimensions=self.text_embedder_hidden_size  ,		hidden_size=self.text_embedder_hidden_size  ,		intermediate_size=37  ,		num_attention_heads=4  ,		num_hidden_layers=5  ,		vocab_size=1005  ,		)

          a     =				MultilingualCLIP(A			)
          a     =				text_encoder.eval()

          return text_encoder







     @property
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          torch.manual_seed(0			)

          a     =				{
              "in_channels": 9,
              # Out channels is double in channels because predicts mean and variance
              "out_channels": 8,
              "addition_embed_type": "text_image",
              "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
              "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
              "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "layers_per_block": 1,
              "encoder_hid_dim": self.text_embedder_hidden_size,
              "encoder_hid_dim_type": "text_image_proj",
              "cross_attention_dim": self.cross_attention_dim,
              "attention_head_dim": 4,
              "resnet_time_scale_shift": "scale_shift",
              "class_embed_type": None,
          }

          a     =				UNetaDConditionModel(**A			)
          return model







     @property
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }







     @property
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          torch.manual_seed(0			)
          a     =				VQModel(**self.dummy_movq_kwargs			)
          return model







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.dummy_text_encoder
          a     =				self.dummy_tokenizer
          a     =				self.dummy_unet
          a     =				self.dummy_movq

          a     =				DDIMScheduler(
              num_train_timesteps=1000  ,		beta_schedule="linear"  ,		beta_start=0.0_0_0_8_5  ,		beta_end=0.0_1_2  ,		clip_sample=A  ,		set_alpha_to_one=A  ,		steps_offset=1  ,		prediction_type="epsilon"  ,		thresholding=A  ,		)

          a     =				{
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }

          return components







     def       lowerCAmelCase_    (     self  ,		A  ,		A=0			)  ->   Dict:



          '''simple docstring'''



          a     =				floats_tensor((1, self.cross_attention_dim)  ,		rng=random.Random(A			)			).to(A			)
          a     =				floats_tensor((1, self.cross_attention_dim)  ,		rng=random.Random(seed + 1			)			).to(A			)
          # create init_image
          a     =				floats_tensor((1, 3, 64, 64)  ,		rng=random.Random(A			)			).to(A			)
          a     =				image.cpu().permute(0  ,		2  ,		3  ,		1			)[0]
          a     =				Image.fromarray(np.uinta(A			)			).convert("RGB"			).resize((256, 256)			)
          # create mask
          a     =				np.ones((64, 64)  ,		dtype=np.floataa			)
          a     =				0

          if str(A			).startswith("mps"			):
               a     =				torch.manual_seed(A			)
          else:
               a     =				torch.Generator(device=A			).manual_seed(A			)
          a     =				{
              "prompt": "horse",
              "image": init_image,
              "mask_image": mask,
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "generator": generator,
              "height": 64,
              "width": 64,
              "num_inference_steps": 2,
              "guidance_scale": 4.0,
              "output_type": "np",
          }
          return inputs







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				"cpu"

          a     =				self.get_dummy_components()

          a     =				self.pipeline_class(**A			)
          a     =				pipe.to(A			)

          pipe.set_progress_bar_config(disable=A			)

          a     =				pipe(**self.get_dummy_inputs(A			)			)
          a     =				output.images

          a     =				pipe(
              **self.get_dummy_inputs(A			)  ,		return_dict=A  ,		)[0]

          a     =				image[0, -3:, -3:, -1]
          a     =				image_from_tuple[0, -3:, -3:, -1]

          print(F'''image.shape {image.shape}'''			)

          assert image.shape == (1, 64, 64, 3)

          a     =				np.array(
              [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]			)

          assert (
              np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
          ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
          ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          super().test_inference_batch_single_identical(expected_max_diff=3e-3			)







@slow
@require_torch_gpu
class       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"			)

          a     =				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"			)
          a     =				np.ones((768, 768)  ,		dtype=np.floataa			)
          a     =				0

          a     =				"a hat"

          a     =				KandinskyPriorPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-1-prior"  ,		torch_dtype=torch.floataa			)
          pipe_prior.to(A			)

          a     =				KandinskyInpaintPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-1-inpaint"  ,		torch_dtype=torch.floataa			)
          a     =				pipeline.to(A			)
          pipeline.set_progress_bar_config(disable=A			)

          a     =				torch.Generator(device="cpu"			).manual_seed(0			)
          a  ,				a     =				pipe_prior(
              A  ,		generator=A  ,		num_inference_steps=5  ,		negative_prompt=""  ,		).to_tuple()

          a     =				pipeline(
              A  ,		image=A  ,		mask_image=A  ,		image_embeds=A  ,		negative_image_embeds=A  ,		generator=A  ,		num_inference_steps=100  ,		height=768  ,		width=768  ,		output_type="np"  ,		)

          a     =				output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(A  ,		A			)







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__	:







     def __init__(     self  ,		A  ,		A=2  ,		A=32  ,		A=16  ,		A=3  ,		A=True  ,		A=True  ,		A=32  ,		A=4  ,		A=[0, 1, 2, 3]  ,		A=4  ,		A=37  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=0.0_2  ,		A=3  ,		A=[1, 384, 24, 24]  ,		A=True  ,		A=None  ,		)  ->   Any:



          '''simple docstring'''



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



          '''simple docstring'''



          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]:



          '''simple docstring'''



          a     =				{
              "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=A  ,		initializer_range=self.initializer_range  ,		is_hybrid=self.is_hybrid  ,		backbone_config=A  ,		backbone_featmap_shape=self.backbone_featmap_shape  ,		)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				DPTModel(config=A			)
          model.to(A			)
          model.eval()
          a     =				model(A			)
          self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.num_labels
          a     =				DPTForDepthEstimation(A			)
          model.to(A			)
          model.eval()
          a     =				model(A			)
          self.parent.assertEqual(result.predicted_depth.shape  ,		(self.batch_size, self.image_size, self.image_size)			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Dict:



          '''simple docstring'''



          a     =				self.num_labels
          a     =				DPTForSemanticSegmentation(A			)
          model.to(A			)
          model.eval()
          a     =				model(A  ,		labels=A			)
          self.parent.assertEqual(
              result.logits.shape  ,		(self.batch_size, self.num_labels, self.image_size, self.image_size)			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          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__	( UpperCamelCase__       , UpperCamelCase__       , unittest.TestCase				):
     a		:      Union[str, Any]		 =     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     a		:      Union[str, Any]		 =     (
         {
             """depth-estimation""": DPTForDepthEstimation,
             """feature-extraction""": DPTModel,
             """image-segmentation""": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     a		:      Optional[int]		 =     False
     a		:      List[Any]		 =     False
     a		:      int		 =     False







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				DPTModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=A  ,		has_text_modality=A  ,		hidden_size=37			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     @unittest.skip(reason="DPT does not use inputs_embeds"			)
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a     =				model_class(A			)
               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]  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*A			)







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



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*A			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          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(A			):
                    continue

               a     =				model_class(A			)
               model.to(A			)
               model.train()
               a     =				self._prepare_for_class(A  ,		A  ,		return_labels=A			)
               a     =				model(**A			).loss
               loss.backward()







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          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(A			) or not model_class.supports_gradient_checkpointing:
                    continue
               a     =				model_class(A			)
               model.to(A			)
               model.gradient_checkpointing_enable()
               model.train()
               a     =				self._prepare_for_class(A  ,		A  ,		return_labels=A			)
               a     =				model(**A			).loss
               loss.backward()







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

          a     =				_config_zero_init(A			)
          for model_class in self.all_model_classes:
               a     =				model_class(config=A			)
               # 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[Any]:



          '''simple docstring'''



          pass







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
               a     =				DPTModel.from_pretrained(A			)
               self.assertIsNotNone(A			)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()
          a     =				"add"
          with self.assertRaises(A			):
               a     =				DPTForDepthEstimation(A			)







def  SCREAMING_SNAKE_CASE							(       )      ->					str:
     a     =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     return image





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







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"			)
          a     =				DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"			).to(A			)

          a     =				prepare_img()
          a     =				image_processor(images=A  ,		return_tensors="pt"			).to(A			)

          # forward pass
          with torch.no_grad():
               a     =				model(**A			)
               a     =				outputs.predicted_depth

          # verify the predicted depth
          a     =				torch.Size((1, 384, 384)			)
          self.assertEqual(predicted_depth.shape  ,		A			)

          a     =				torch.tensor(
              [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]			).to(A			)

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

