
'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ViltImageProcessor





class 			A							(       unittest.TestCase				):



			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=7       ,     lowerCamelCase__=3       ,     lowerCamelCase__=18       ,     lowerCamelCase__=30       ,     lowerCamelCase__=400       ,     lowerCamelCase__=True       ,     lowerCamelCase__=None       ,     lowerCamelCase__=2       ,     lowerCamelCase__=True       ,     lowerCamelCase__=[0.5, 0.5, 0.5]       ,     lowerCamelCase__=[0.5, 0.5, 0.5]       ,     )			->		List[Any]:




									'''simple docstring'''



									lowercase__							 =       size if size is not None else {'shortest_edge': 30}
									lowercase__							 =       parent
									lowercase__							 =       batch_size
									lowercase__							 =       num_channels
									lowercase__							 =       image_size
									lowercase__							 =       min_resolution
									lowercase__							 =       max_resolution
									lowercase__							 =       do_resize
									lowercase__							 =       size
									lowercase__							 =       size_divisor
									lowercase__							 =       do_normalize
									lowercase__							 =       image_mean
									lowercase__							 =       image_std



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "size_divisor": self.size_divisor,
									}



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=False       )			->		Union[str, Any]:




									'''simple docstring'''



									if not batched:
															lowercase__							 =       self.size['shortest_edge']
															lowercase__							 =       image_inputs[0]
															if isinstance(SCREAMING_SNAKE_CASE_       ,     Image.Image       ):
																					lowercase__							 =       image.size
															else:
																					lowercase__							 =       image.shape[1], image.shape[2]
															lowercase__							 =       size / min(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       )
															if h < w:
																					lowercase__							 =       size, scale * w
															else:
																					lowercase__							 =       scale * h, size

															lowercase__							 =       int((1_333 / 800) * size       )
															if max(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ) > max_size:
																					lowercase__							 =       max_size / max(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       )
																					lowercase__							 =       newh * scale
																					lowercase__							 =       neww * scale

															lowercase__							 =       int(newh + 0.5       ), int(neww + 0.5       )
															lowercase__							 =       (
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

									else:
															lowercase__							 =       []
															for image in image_inputs:
																					lowercase__							 =       self.get_expected_values([image]       )
																					expected_values.append((expected_height, expected_width)       )
															lowercase__							 =       max(SCREAMING_SNAKE_CASE_       ,     key=lambda lowerCamelCase__       : item[0]       )[0]
															lowercase__							 =       max(SCREAMING_SNAKE_CASE_       ,     key=lambda lowerCamelCase__       : item[1]       )[1]

									return expected_height, expected_width





@require_torch
@require_vision
class 			A							(       a__		,						unittest.TestCase				):
			lowerCamelCase							:							List[Any]       		=  ViltImageProcessor if is_vision_available() else None



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




									'''simple docstring'''



									lowercase__							 =       ViltImageProcessingTester(self       )



			@property
			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									return self.image_processor_tester.prepare_image_processor_dict()



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




									'''simple docstring'''



									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,     """image_mean"""       )       )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,     """image_std"""       )       )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,     """do_normalize"""       )       )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,     """do_resize"""       )       )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,     """size"""       )       )
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,     """size_divisor"""       )       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__							 =       self.image_processing_class.from_dict(self.image_processor_dict       )
									self.assertEqual(image_processor.size       ,     {"""shortest_edge""": 30}       )

									lowercase__							 =       self.image_processing_class.from_dict(self.image_processor_dict       ,     size=42       )
									self.assertEqual(image_processor.size       ,     {"""shortest_edge""": 42}       )



			def A__ (					self       )			->		Dict:




									'''simple docstring'''



									pass



			def A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									lowercase__							 =       prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=SCREAMING_SNAKE_CASE_       )
									for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,     Image.Image       )

									# Test not batched input
									lowercase__							 =       image_processing(image_inputs[0]       ,     return_tensors="""pt"""       ).pixel_values

									lowercase__							 =       self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )
									self.assertEqual(
									    encoded_images.shape       ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,     )

									# Test batched
									lowercase__							 =       image_processing(SCREAMING_SNAKE_CASE_       ,     return_tensors="""pt"""       ).pixel_values

									lowercase__							 =       self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,     batched=SCREAMING_SNAKE_CASE_       )
									self.assertEqual(
									    encoded_images.shape       ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,     )



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




									'''simple docstring'''



									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									lowercase__							 =       prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=SCREAMING_SNAKE_CASE_       ,     numpify=SCREAMING_SNAKE_CASE_       )
									for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,     np.ndarray       )

									# Test not batched input
									lowercase__							 =       image_processing(image_inputs[0]       ,     return_tensors="""pt"""       ).pixel_values

									lowercase__							 =       self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )
									self.assertEqual(
									    encoded_images.shape       ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,     )

									# Test batched
									lowercase__							 =       image_processing(SCREAMING_SNAKE_CASE_       ,     return_tensors="""pt"""       ).pixel_values

									lowercase__							 =       self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,     batched=SCREAMING_SNAKE_CASE_       )
									self.assertEqual(
									    encoded_images.shape       ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,     )



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




									'''simple docstring'''



									lowercase__							 =       self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									lowercase__							 =       prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=SCREAMING_SNAKE_CASE_       ,     torchify=SCREAMING_SNAKE_CASE_       )
									for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor       )

									# Test not batched input
									lowercase__							 =       image_processing(image_inputs[0]       ,     return_tensors="""pt"""       ).pixel_values

									lowercase__							 =       self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       )
									self.assertEqual(
									    encoded_images.shape       ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,     )

									# Test batched
									lowercase__							 =       image_processing(SCREAMING_SNAKE_CASE_       ,     return_tensors="""pt"""       ).pixel_values

									lowercase__							 =       self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_       ,     batched=SCREAMING_SNAKE_CASE_       )
									self.assertEqual(
									    encoded_images.shape       ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )       ,     )








'''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.






