code: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
style_context: 
  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()
  
  snake_case				:	int							   =						logging.get_logger(__name__)
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						bool = True     )       ->	Tuple:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[Any]			     =  {}
  	for name in model.attn_processors.keys():
  		__magic_name__	:	List[Any]			     =  name.endswith("attn2.processor"     ) and not (
  		    name.startswith("transformer_in"     ) or "temp_attentions" in name.split("."     )
  		)
  		__magic_name__	:	List[str]			     =  model.config.cross_attention_dim if has_cross_attention else None
  		if name.startswith("mid_block"     ):
  			__magic_name__	:	Tuple			     =  model.config.block_out_channels[-1]
  		elif name.startswith("up_blocks"     ):
  			__magic_name__	:	Tuple			     =  int(name[len("up_blocks."     )]     )
  			__magic_name__	:	Optional[Any]			     =  list(reversed(model.config.block_out_channels     )     )[block_id]
  		elif name.startswith("down_blocks"     ):
  			__magic_name__	:	Optional[int]			     =  int(name[len("down_blocks."     )]     )
  			__magic_name__	:	str			     =  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
  			__magic_name__	:	List[Any]			     =  8 * model.config.attention_head_dim
  
  		__magic_name__	:	Union[str, Any]			     =  LoRAAttnProcessor(hidden_size=_snake_case   ,     cross_attention_dim=_snake_case     )
  		__magic_name__	:	str			     =  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    _snake_case      (  snake_case       ,		snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					UNetaDConditionModel
  			UpperCamelCase__					   =					'sample'
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  4
  				__magic_name__	:	str			     =  4
  				__magic_name__	:	List[str]			     =  4
  				__magic_name__	:	Optional[int]			     =  (32, 32)
  
  				__magic_name__	:	Optional[Any]			     =  floats_tensor((batch_size, num_channels, num_frames) + sizes	).to(_a	)
  				__magic_name__	:	Union[str, Any]			     =  torch.tensor([10]	).to(_a	)
  				__magic_name__	:	List[str]			     =  floats_tensor((batch_size, 4, 32)	).to(_a	)
  
  				return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return (4, 4, 32, 32)
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return (4, 4, 32, 32)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  {
  				    "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,
  				}
  				__magic_name__	:	List[str]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Any			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  
  				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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Tuple			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Any			     =  32
  
  				__magic_name__	:	Dict			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	)
  
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	Any			     =  output.sample
  
  				self.assertIsNotNone(_a	)
  				__magic_name__	:	Union[str, Any]			     =  inputs_dict["sample"].shape
  				self.assertEqual(output.shape		,     _a		,     "Input and output shapes do not match"	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	int			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					# Warmup pass when using mps (see #372)
  					if torch_device == "mps" and isinstance(_a		,     _a	):
  						model(**self.dummy_input	)
  
  					__magic_name__	:	str			     =  model(**_a	)
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	str			     =  first.sample
  
  					__magic_name__	:	Tuple			     =  model(**_a	)
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	str			     =  second.sample
  
  				__magic_name__	:	Optional[int]			     =  first.cpu().numpy()
  				__magic_name__	:	Any			     =  second.cpu().numpy()
  				__magic_name__	:	List[Any]			     =  out_a[~np.isnan(_a	)]
  				__magic_name__	:	Any			     =  out_a[~np.isnan(_a	)]
  				__magic_name__	:	Tuple			     =  np.amax(np.abs(out_a - out_a	)	)
  				self.assertLessEqual(_a		,     1e-5	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Union[str, Any]			     =  8
  
  				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				model.set_attention_slice("auto"	)
  				with torch.no_grad():
  					__magic_name__	:	List[Any]			     =  model(**_a	)
  				assert output is not None
  
  				model.set_attention_slice("max"	)
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_a	)
  				assert output is not None
  
  				model.set_attention_slice(2	)
  				with torch.no_grad():
  					__magic_name__	:	Union[str, Any]			     =  model(**_a	)
  				assert output is not None
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[Any]			     =  8
  
  				__magic_name__	:	Dict			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Union[str, Any]			     =  model(**_a	).sample
  
  				__magic_name__	:	int			     =  create_lora_layers(_a	)
  
  				# make sure we can set a list of attention processors
  				model.set_attn_processor(_a	)
  				model.to(_a	)
  
  				# test that attn processors can be set to itself
  				model.set_attn_processor(model.attn_processors	)
  
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
  					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  					__magic_name__	:	Union[str, Any]			     =  model(**_a		,     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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_a	).sample
  
  				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	int			     =  new_model(**_a		,     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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Optional[int]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	str			     =  model(**_a	).sample
  
  				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a		,     safe_serialization=_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.safetensors"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	List[Any]			     =  new_model(**_a		,     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     SCREAMING_SNAKE_CASE					(       self	):
  				# enable deterministic behavior for gradient checkpointing
  				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[str]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				__magic_name__	:	Any			     =  create_lora_layers(_a		,     mock_weights=_a	)
  				model.set_attn_processor(_a	)
  				# Saving as torch, properly reloads with directly filename
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a		,     weight_name="pytorch_lora_weights.bin"	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[str]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				__magic_name__	:	str			     =  create_lora_layers(_a		,     mock_weights=_a	)
  				model.set_attn_processor(_a	)
  				# Saving as torch, properly reloads with directly filename
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					with self.assertRaises(_a	) as e:
  						new_model.load_attn_procs(_a		,     use_safetensors=_a	)
  					self.assertIn("Error no file named pytorch_lora_weights.safetensors"		,     str(e.exception	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Any			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	).sample
  
  				__magic_name__	:	int			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
  
  				model.set_attn_processor(AttnProcessor()	)
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a	).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     SCREAMING_SNAKE_CASE					(       self	):
  				# enable deterministic behavior for gradient checkpointing
  				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  4
  
  				torch.manual_seed(0	)
  				__magic_name__	:	str			     =  self.model_class(**_a	)
  				model.to(_a	)
  				__magic_name__	:	Any			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				# default
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a	).sample
  
  					model.enable_xformers_memory_efficient_attention()
  					__magic_name__	:	str			     =  model(**_a	).sample
  
  					model.disable_xformers_memory_efficient_attention()
  					__magic_name__	:	Optional[Any]			     =  model(**_a	).sample
  
  				assert (sample - on_sample).abs().max() < 1e-4
  				assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	str			     =  32
  
  				__magic_name__	:	int			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a	)[0]
  
  				model.enable_forward_chunking()
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a	)[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.
  