code: 
  '''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 			A							:
  
  
  
  			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__=False       ,     lowerCamelCase__=None       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       batch_size
  									lowercase__							 =       image_size
  									lowercase__							 =       patch_size
  									lowercase__							 =       num_channels
  									lowercase__							 =       is_training
  									lowercase__							 =       use_labels
  									lowercase__							 =       hidden_size
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       backbone_out_indices
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       intermediate_size
  									lowercase__							 =       hidden_act
  									lowercase__							 =       hidden_dropout_prob
  									lowercase__							 =       attention_probs_dropout_prob
  									lowercase__							 =       initializer_range
  									lowercase__							 =       num_labels
  									lowercase__							 =       scope
  									lowercase__							 =       is_hybrid
  									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
  									lowercase__							 =       (image_size // patch_size) ** 2
  									lowercase__							 =       num_patches + 1
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
  
  									lowercase__							 =       None
  									if self.use_labels:
  															lowercase__							 =       ids_tensor([self.batch_size, self.image_size, self.image_size]       ,     self.num_labels       )
  
  									lowercase__							 =       self.get_config()
  
  									return config, pixel_values, labels
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTModel(config=lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       DPTForDepthEstimation(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(result.predicted_depth.shape       ,     (self.batch_size, self.image_size, self.image_size)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       DPTForSemanticSegmentation(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       ,     labels=lowerCamelCase__       )
  									self.parent.assertEqual(
  									    result.logits.shape       ,     (self.batch_size, self.num_labels, self.image_size, self.image_size)       )
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  									lowercase__ ,						lowercase__ ,						lowercase__							 =       config_and_inputs
  									lowercase__							 =       {"""pixel_values""": pixel_values}
  									return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							Union[str, Any]       		=  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  			lowerCamelCase							:							List[Any]       		=  (
  			    {
  			        """depth-estimation""": DPTForDepthEstimation,
  			        """feature-extraction""": DPTModel,
  			        """image-segmentation""": DPTForSemanticSegmentation,
  			    }
  			    if is_torch_available()
  			    else {}
  			)
  
  			lowerCamelCase							:							Any       		=  False
  			lowerCamelCase							:							Any       		=  False
  			lowerCamelCase							:							Optional[int]       		=  False
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTModelTester(self       )
  									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     has_text_modality=lowerCamelCase__       ,     hidden_size=37       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.config_tester.run_common_tests()
  
  
  
  			@unittest.skip(reason="""DPT does not use inputs_embeds"""       )
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															self.assertIsInstance(model.get_input_embeddings()       ,     (nn.Module)       )
  															lowercase__							 =       model.get_output_embeddings()
  															self.assertTrue(x is None or isinstance(lowerCamelCase__       ,     nn.Linear       )       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															lowercase__							 =       inspect.signature(model.forward       )
  															# signature.parameters is an OrderedDict => so arg_names order is deterministic
  															lowercase__							 =       [*signature.parameters.keys()]
  
  															lowercase__							 =       ["""pixel_values"""]
  															self.assertListEqual(arg_names[:1]       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_model(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_class in self.all_model_classes:
  															if model_class.__name__ == "DPTForDepthEstimation":
  																					continue
  
  															lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  															lowercase__							 =       True
  
  															if model_class in get_values(lowerCamelCase__       ):
  																					continue
  
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															model.to(lowerCamelCase__       )
  															model.train()
  															lowercase__							 =       self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  															lowercase__							 =       model(**lowerCamelCase__       ).loss
  															loss.backward()
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_class in self.all_model_classes:
  															if model_class.__name__ == "DPTForDepthEstimation":
  																					continue
  
  															lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  															lowercase__							 =       False
  															lowercase__							 =       True
  
  															if model_class in get_values(lowerCamelCase__       ) or not model_class.supports_gradient_checkpointing:
  																					continue
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															model.to(lowerCamelCase__       )
  															model.gradient_checkpointing_enable()
  															model.train()
  															lowercase__							 =       self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  															lowercase__							 =       model(**lowerCamelCase__       ).loss
  															loss.backward()
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									lowercase__							 =       _config_zero_init(lowerCamelCase__       )
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(config=lowerCamelCase__       )
  															# Skip the check for the backbone
  															lowercase__							 =       []
  															for name, module in model.named_modules():
  																					if module.__class__.__name__ == "DPTViTHybridEmbeddings":
  																											lowercase__							 =       [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 A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			@slow
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  															lowercase__							 =       DPTModel.from_pretrained(lowerCamelCase__       )
  															self.assertIsNotNone(lowerCamelCase__       )
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  						return image
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class 			A							(       unittest.TestCase				):
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large"""       )
  									lowercase__							 =       DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""       ).to(lowerCamelCase__       )
  
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )
  
  									# forward pass
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  															lowercase__							 =       outputs.predicted_depth
  
  									# verify the predicted depth
  									lowercase__							 =       torch.Size((1, 384, 384)       )
  									self.assertEqual(predicted_depth.shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       torch.tensor(
  									    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]       ).to(lowerCamelCase__       )
  
  									self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]       ,     lowerCamelCase__       ,     atol=1e-4       )       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""       )
  									lowercase__							 =       DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""       ).to(lowerCamelCase__       )
  
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )
  
  									# forward pass
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  									# verify the logits
  									lowercase__							 =       torch.Size((1, 150, 480, 480)       )
  									self.assertEqual(outputs.logits.shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       torch.tensor(
  									    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]       ).to(lowerCamelCase__       )
  
  									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]       ,     lowerCamelCase__       ,     atol=1e-4       )       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""       )
  									lowercase__							 =       DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""       ).to(lowerCamelCase__       )
  
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )
  
  									# forward pass
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  									lowercase__							 =       outputs.logits.detach().cpu()
  
  									lowercase__							 =       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__       ,     target_sizes=[(500, 300)]       )
  									lowercase__							 =       torch.Size((500, 300)       )
  									self.assertEqual(segmentation[0].shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__       )
  									lowercase__							 =       torch.Size((480, 480)       )
  									self.assertEqual(segmentation[0].shape       ,     lowerCamelCase__       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import os
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers.models import ModelMixin, UNetaDConditionModel
  from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
  from diffusers.utils import (
      floats_tensor,
      logging,
      skip_mps,
      torch_device,
  )
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism
  
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
  
  
  enable_full_determinism()
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__ = True	):
  						lowercase__							 =       {}
  						for name in model.attn_processors.keys():
  												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
  												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
  												)
  												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
  												if name.startswith("""mid_block"""	):
  																		lowercase__							 =       model.config.block_out_channels[-1]
  												elif name.startswith("""up_blocks"""	):
  																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
  																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
  												elif name.startswith("""down_blocks"""	):
  																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
  																		lowercase__							 =       model.config.block_out_channels[block_id]
  												elif name.startswith("""transformer_in"""	):
  																		# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
  																		lowercase__							 =       8 * model.config.attention_head_dim
  
  												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
  												lowercase__							 =       lora_attn_procs[name].to(model.device	)
  
  												if mock_weights:
  																		# add 1 to weights to mock trained weights
  																		with torch.no_grad():
  																								lora_attn_procs[name].to_q_lora.up.weight += 1
  																								lora_attn_procs[name].to_k_lora.up.weight += 1
  																								lora_attn_procs[name].to_v_lora.up.weight += 1
  																								lora_attn_procs[name].to_out_lora.up.weight += 1
  
  						return lora_attn_procs
  
  
  
  
  
  
  
  @skip_mps
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
  			lowerCamelCase							:							int       		=  """sample"""
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       (32, 32)
  
  									lowercase__							 =       floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
  									lowercase__							 =       torch.tensor([10]       ).to(lowerCamelCase__       )
  									lowercase__							 =       floats_tensor((batch_size, 4, 32)       ).to(lowerCamelCase__       )
  
  									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  			@property
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """block_out_channels""": (32, 64),
  									    """down_block_types""": (
  									        """CrossAttnDownBlock3D""",
  									        """DownBlock3D""",
  									    ),
  									    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
  									    """cross_attention_dim""": 32,
  									    """attention_head_dim""": 8,
  									    """out_channels""": 4,
  									    """in_channels""": 4,
  									    """layers_per_block""": 1,
  									    """sample_size""": 32,
  									}
  									lowercase__							 =       self.dummy_input
  									return init_dict, inputs_dict
  
  
  
  			@unittest.skipIf(
  			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  
  									model.enable_xformers_memory_efficient_attention()
  
  									assert (
  									    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
  									    == "XFormersAttnProcessor"
  									), "xformers is not enabled"
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       output.sample
  
  									self.assertIsNotNone(lowerCamelCase__       )
  									lowercase__							 =       inputs_dict["""sample"""].shape
  									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															# Warmup pass when using mps (see #372)
  															if torch_device == "mps" and isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					model(**self.dummy_input       )
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       first.sample
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       second.sample
  
  									lowercase__							 =       first.cpu().numpy()
  									lowercase__							 =       second.cpu().numpy()
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
  									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									model.set_attention_slice("""auto"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice("""max"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice(2       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  
  									# make sure we can set a list of attention processors
  									model.set_attn_processor(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									# test that attn processors can be set to itself
  									model.set_attn_processor(model.attn_processors       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (samplea - samplea).abs().max() < 3e-3
  									assert (samplea - samplea).abs().max() < 3e-3
  
  									# sample 2 and sample 3 should be different
  									assert (samplea - samplea).abs().max() > 3e-3
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (sample - new_sample).abs().max() < 1e-3
  
  									# LoRA and no LoRA should NOT be the same
  									assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       ,     safe_serialization=lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.safetensors"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (sample - new_sample).abs().max() < 3e-3
  
  									# LoRA and no LoRA should NOT be the same
  									assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  									# Saving as torch, properly reloads with directly filename
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  									# Saving as torch, properly reloads with directly filename
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															with self.assertRaises(lowerCamelCase__       ) as e:
  																					new_model.load_attn_procs(lowerCamelCase__       ,     use_safetensors=lowerCamelCase__       )
  															self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""       ,     str(e.exception       )       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  
  									model.set_attn_processor(AttnProcessor()       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									assert (sample - new_sample).abs().max() < 1e-4
  									assert (sample - old_sample).abs().max() < 3e-3
  
  
  
  			@unittest.skipIf(
  			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       4
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									# default
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.enable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.disable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									assert (sample - on_sample).abs().max() < 1e-4
  									assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[0]
  
  									model.enable_forward_chunking()
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[0]
  
  									self.assertEqual(output.shape       ,     output_a.shape       ,     """Shape doesn't match"""       )
  									assert np.abs(output.cpu() - output_a.cpu()       ).max() < 1e-2
  
  
  # (todo: sayakpaul) implement SLOW tests.
  
  
  
  
  
  
  