





'''simple docstring'''






from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
A__       :     Optional[Any]          =20

# Time between time steps in seconds
A__       :     Optional[Any]          =INTERVAL / 10_00




class 				UpperCAmelCase  :




      def __init__(    self    :				str    ,    __snake_case    :				float    ,    __snake_case    :				float    ,    __snake_case    :				float    ,    __snake_case    :				float    ,    __snake_case    :				float = 1.0    ,    __snake_case    :				float = 1.0    ,    __snake_case    :				str = "blue"    ,    )       ->   None:
            _lowerCAmelCase							  =							position_x
            _lowerCAmelCase							  =							position_y
            _lowerCAmelCase							  =							velocity_x
            _lowerCAmelCase							  =							velocity_y
            _lowerCAmelCase							  =							mass
            _lowerCAmelCase							  =							size
            _lowerCAmelCase							  =							color




      @property
      def        lowercase__					(    self    :				Tuple						)       ->   tuple[float, float]:
            return self.position_x, self.position_y




      @property
      def        lowercase__					(    self    :				List[str]						)       ->   tuple[float, float]:
            return self.velocity_x, self.velocity_y




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				float    ,    __snake_case    :				float    ,    __snake_case    :				float						)       ->   None:
            self.velocity_x += force_x * delta_time
            self.velocity_y += force_y * delta_time




      def        lowercase__					(    self    :				Tuple    ,    __snake_case    :				float						)       ->   None:
            self.position_x += self.velocity_x * delta_time
            self.position_y += self.velocity_y * delta_time




class 				UpperCAmelCase  :




      def __init__(    self    :				int    ,    __snake_case    :				list[Body]    ,    __snake_case    :				float = 1.0    ,    __snake_case    :				float = 1.0    ,    __snake_case    :				float = 0.0    ,    )       ->   None:
            _lowerCAmelCase							  =							bodies
            _lowerCAmelCase							  =							gravitation_constant
            _lowerCAmelCase							  =							time_factor
            _lowerCAmelCase							  =							softening_factor




      def __len__(    self    :				List[Any]						)       ->   int:
            return len(self.bodies						)




      def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				float						)       ->   None:
            for bodya in self.bodies:
                  _lowerCAmelCase							  =							0.0
                  _lowerCAmelCase							  =							0.0
                  for bodya in self.bodies:
                        if bodya != bodya:
                              _lowerCAmelCase							  =							bodya.position_x - bodya.position_x
                              _lowerCAmelCase							  =							bodya.position_y - bodya.position_y

                              # Calculation of the distance using Pythagoras's theorem
                              # Extra factor due to the softening technique
                              _lowerCAmelCase							  =							(dif_x**2 + dif_y**2 + self.softening_factor) ** (
                                  1 / 2
                              )

                              # Newton's law of universal gravitation.
                              force_x += (
                                  self.gravitation_constant * bodya.mass * dif_x / distance**3
                              )
                              force_y += (
                                  self.gravitation_constant * bodya.mass * dif_y / distance**3
                              )

            # Update the body's velocity once all the force components have been added
                  bodya.update_velocity(__snake_case    ,    __snake_case    ,    delta_time * self.time_factor						)

            # Update the positions only after all the velocities have been updated
            for body in self.bodies:
                  body.update_position(delta_time * self.time_factor						)




def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      body_system.update_system(lowerCAmelCase						)

      # Update the positions of the patches
      for patch, body in zip(lowerCAmelCase							,			body_system.bodies						):
            _lowerCAmelCase							  =							(body.position_x, body.position_y)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = -1							,			lowerCAmelCase = 1							,			lowerCAmelCase = -1							,			lowerCAmelCase = 1							,			):





      """simple docstring"""


      _lowerCAmelCase							  =							plt.figure()
      fig.canvas.set_window_title(lowerCAmelCase						)
      _lowerCAmelCase							  =							plt.axes(
          xlim=(x_start, x_end)							,			ylim=(y_start, y_end)						)  # Set section to be plotted
      plt.gca().set_aspect("""equal"""						)  # Fix aspect ratio

      # Each body is drawn as a patch by the plt-function
      _lowerCAmelCase							  =							[
          plt.Circle((body.position_x, body.position_y)							,			body.size							,			fc=body.color						)
          for body in body_system.bodies
      ]

      for patch in patches:
            ax.add_patch(lowerCAmelCase						)

      # Function called at each step of the animation
      def update(lowerCAmelCase						) -> list[plt.Circle]:
            update_step(lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						)
            return patches

      _lowerCAmelCase							  =							animation.FuncAnimation(  # noqa: F841
          lowerCAmelCase							,			lowerCAmelCase							,			interval=lowerCAmelCase							,			blit=lowerCAmelCase						)

      plt.show()
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							0.9_700_436
      _lowerCAmelCase							  =							-0.24_308_753
      _lowerCAmelCase							  =							0.466_203_685
      _lowerCAmelCase							  =							0.43_236_573

      _lowerCAmelCase							  =							[
          Body(lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			size=0.2							,			color="""red"""						),
          Body(-position_x							,			-position_y							,			lowerCAmelCase							,			lowerCAmelCase							,			size=0.2							,			color="""green"""						),
          Body(0							,			0							,			-2 * velocity_x							,			-2 * velocity_y							,			size=0.2							,			color="""blue"""						),
      ]
      return BodySystem(lowerCAmelCase							,			time_factor=3						)
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							7.34_76e22
      _lowerCAmelCase							  =							5.9_72e24
      _lowerCAmelCase							  =							10_22
      _lowerCAmelCase							  =							3_84_39_90_00
      _lowerCAmelCase							  =							6.6_74e-11

      # Calculation of the respective velocities so that total impulse is zero,
      # i.e. the two bodies together don't move
      _lowerCAmelCase							  =							earth_mass * velocity_dif / (earth_mass + moon_mass)
      _lowerCAmelCase							  =							moon_velocity - velocity_dif

      _lowerCAmelCase							  =							Body(-earth_moon_distance							,			0							,			0							,			lowerCAmelCase							,			lowerCAmelCase							,			10_00_00_00							,			"""grey"""						)
      _lowerCAmelCase							  =							Body(0							,			0							,			0							,			lowerCAmelCase							,			lowerCAmelCase							,			50_00_00_00							,			"""blue"""						)
      return BodySystem([earth, moon]							,			lowerCAmelCase							,			time_factor=1_00_00_00						)
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							[]
      for _ in range(10						):
            _lowerCAmelCase							  =							random.uniform(-0.5							,			0.5						)
            _lowerCAmelCase							  =							random.uniform(-0.5							,			0.5						)

            # Bodies are created pairwise with opposite velocities so that the
            # total impulse remains zero
            bodies.append(
                Body(
                    random.uniform(-0.5							,			0.5						)							,			random.uniform(-0.5							,			0.5						)							,			lowerCAmelCase							,			lowerCAmelCase							,			size=0.05							,			)						)
            bodies.append(
                Body(
                    random.uniform(-0.5							,			0.5						)							,			random.uniform(-0.5							,			0.5						)							,			-velocity_x							,			-velocity_y							,			size=0.05							,			)						)
      return BodySystem(lowerCAmelCase							,			0.01							,			10							,			0.1						)


if __name__ == "__main__":
      plot('''Figure-8 solution to the 3-body-problem''', example_a(), -2, 2, -2, 2)
      plot(
          '''Moon\'s orbit around the earth''',
          example_a(),
          -4_30_00_00_00,
          4_30_00_00_00,
          -4_30_00_00_00,
          4_30_00_00_00,
      )
      plot('''Random system with many bodies''', example_a(), -1.5, 1.5, -1.5, 1.5)













'''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__       :     str          =logging.get_logger(__name__)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase = True						):





      """simple docstring"""


      _lowerCAmelCase							  =							{}
      for name in model.attn_processors.keys():
            _lowerCAmelCase							  =							name.endswith("""attn2.processor"""						) and not (
                name.startswith("""transformer_in"""						) or """temp_attentions""" in name.split("""."""						)
            )
            _lowerCAmelCase							  =							model.config.cross_attention_dim if has_cross_attention else None
            if name.startswith("""mid_block"""						):
                  _lowerCAmelCase							  =							model.config.block_out_channels[-1]
            elif name.startswith("""up_blocks"""						):
                  _lowerCAmelCase							  =							int(name[len("""up_blocks."""						)]						)
                  _lowerCAmelCase							  =							list(reversed(model.config.block_out_channels						)						)[block_id]
            elif name.startswith("""down_blocks"""						):
                  _lowerCAmelCase							  =							int(name[len("""down_blocks."""						)]						)
                  _lowerCAmelCase							  =							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
                  _lowerCAmelCase							  =							8 * model.config.attention_head_dim

            _lowerCAmelCase							  =							LoRAAttnProcessor(hidden_size=lowerCAmelCase							,			cross_attention_dim=lowerCAmelCase						)
            _lowerCAmelCase							  =							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 				UpperCAmelCase  (    snake_case_							,	snake_case_							,	unittest.TestCase     ):
      _lowercase:    int		  =     UNetaDConditionModel
      _lowercase:    Optional[int]		  =     '''sample'''




      @property
      def        lowercase__					(    self    :				Any						)       ->   int:
            _lowerCAmelCase							  =							4
            _lowerCAmelCase							  =							4
            _lowerCAmelCase							  =							4
            _lowerCAmelCase							  =							(32, 32)

            _lowerCAmelCase							  =							floats_tensor((batch_size, num_channels, num_frames) + sizes						).to(__snake_case						)
            _lowerCAmelCase							  =							torch.tensor([10]						).to(__snake_case						)
            _lowerCAmelCase							  =							floats_tensor((batch_size, 4, 32)						).to(__snake_case						)

            return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}




      @property
      def        lowercase__					(    self    :				Dict						)       ->   Any:
            return (4, 4, 32, 32)




      @property
      def        lowercase__					(    self    :				List[str]						)       ->   Optional[int]:
            return (4, 4, 32, 32)




      def        lowercase__					(    self    :				Optional[Any]						)       ->   Optional[Any]:
            _lowerCAmelCase							  =							{
                """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,
            }
            _lowerCAmelCase							  =							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        lowercase__					(    self    :				List[str]						)       ->   int:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()
            _lowerCAmelCase							  =							self.model_class(**__snake_case						)

            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        lowercase__					(    self    :				str						)       ->   int:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							32

            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)
            model.eval()

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						)

                  if isinstance(__snake_case    ,    __snake_case						):
                        _lowerCAmelCase							  =							output.sample

            self.assertIsNotNone(__snake_case						)
            _lowerCAmelCase							  =							inputs_dict["""sample"""].shape
            self.assertEqual(output.shape    ,    __snake_case    ,    """Input and output shapes do not match"""						)




      def        lowercase__					(    self    :				Any						)       ->   Any:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()
            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)
            model.eval()

            with torch.no_grad():
                  # Warmup pass when using mps (see #372)
                  if torch_device == "mps" and isinstance(__snake_case    ,    __snake_case						):
                        model(**self.dummy_input						)

                  _lowerCAmelCase							  =							model(**__snake_case						)
                  if isinstance(__snake_case    ,    __snake_case						):
                        _lowerCAmelCase							  =							first.sample

                  _lowerCAmelCase							  =							model(**__snake_case						)
                  if isinstance(__snake_case    ,    __snake_case						):
                        _lowerCAmelCase							  =							second.sample

            _lowerCAmelCase							  =							first.cpu().numpy()
            _lowerCAmelCase							  =							second.cpu().numpy()
            _lowerCAmelCase							  =							out_a[~np.isnan(__snake_case						)]
            _lowerCAmelCase							  =							out_a[~np.isnan(__snake_case						)]
            _lowerCAmelCase							  =							np.amax(np.abs(out_a - out_a						)						)
            self.assertLessEqual(__snake_case    ,    1E-5						)




      def        lowercase__					(    self    :				Any						)       ->   List[str]:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							8

            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)
            model.eval()

            model.set_attention_slice("""auto"""						)
            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						)
            assert output is not None

            model.set_attention_slice("""max"""						)
            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						)
            assert output is not None

            model.set_attention_slice(2						)
            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						)
            assert output is not None




      def        lowercase__					(    self    :				str						)       ->   Any:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							8

            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						).sample

            _lowerCAmelCase							  =							create_lora_layers(__snake_case						)

            # make sure we can set a list of attention processors
            model.set_attn_processor(__snake_case						)
            model.to(__snake_case						)

            # test that attn processors can be set to itself
            model.set_attn_processor(model.attn_processors						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case    ,    cross_attention_kwargs={"""scale""": 0.0}						).sample
                  _lowerCAmelCase							  =							model(**__snake_case    ,    cross_attention_kwargs={"""scale""": 0.5}						).sample
                  _lowerCAmelCase							  =							model(**__snake_case    ,    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        lowercase__					(    self    :				Tuple						)       ->   int:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							8

            torch.manual_seed(0						)
            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						).sample

            _lowerCAmelCase							  =							create_lora_layers(__snake_case						)
            model.set_attn_processor(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case    ,    cross_attention_kwargs={"""scale""": 0.5}						).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(__snake_case						)
                  self.assertTrue(os.path.isfile(os.path.join(__snake_case    ,    """pytorch_lora_weights.bin"""						)						)						)
                  torch.manual_seed(0						)
                  _lowerCAmelCase							  =							self.model_class(**__snake_case						)
                  new_model.to(__snake_case						)
                  new_model.load_attn_procs(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							new_model(**__snake_case    ,    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        lowercase__					(    self    :				Optional[int]						)       ->   Optional[int]:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							8

            torch.manual_seed(0						)
            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						).sample

            _lowerCAmelCase							  =							create_lora_layers(__snake_case						)
            model.set_attn_processor(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case    ,    cross_attention_kwargs={"""scale""": 0.5}						).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(__snake_case    ,    safe_serialization=__snake_case						)
                  self.assertTrue(os.path.isfile(os.path.join(__snake_case    ,    """pytorch_lora_weights.safetensors"""						)						)						)
                  torch.manual_seed(0						)
                  _lowerCAmelCase							  =							self.model_class(**__snake_case						)
                  new_model.to(__snake_case						)
                  new_model.load_attn_procs(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							new_model(**__snake_case    ,    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        lowercase__					(    self    :				Dict						)       ->   List[str]:
            # enable deterministic behavior for gradient checkpointing
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							8

            torch.manual_seed(0						)
            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)

            _lowerCAmelCase							  =							create_lora_layers(__snake_case    ,    mock_weights=__snake_case						)
            model.set_attn_processor(__snake_case						)
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(__snake_case						)
                  self.assertTrue(os.path.isfile(os.path.join(__snake_case    ,    """pytorch_lora_weights.bin"""						)						)						)
                  torch.manual_seed(0						)
                  _lowerCAmelCase							  =							self.model_class(**__snake_case						)
                  new_model.to(__snake_case						)
                  new_model.load_attn_procs(__snake_case    ,    weight_name="""pytorch_lora_weights.bin"""						)




      def        lowercase__					(    self    :				Optional[int]						)       ->   List[Any]:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							8

            torch.manual_seed(0						)
            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)

            _lowerCAmelCase							  =							create_lora_layers(__snake_case    ,    mock_weights=__snake_case						)
            model.set_attn_processor(__snake_case						)
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(__snake_case						)
                  self.assertTrue(os.path.isfile(os.path.join(__snake_case    ,    """pytorch_lora_weights.bin"""						)						)						)
                  torch.manual_seed(0						)
                  _lowerCAmelCase							  =							self.model_class(**__snake_case						)
                  new_model.to(__snake_case						)
                  with self.assertRaises(__snake_case						) as e:
                        new_model.load_attn_procs(__snake_case    ,    use_safetensors=__snake_case						)
                  self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""    ,    str(e.exception						)						)




      def        lowercase__					(    self    :				Any						)       ->   Any:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							8

            torch.manual_seed(0						)
            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						).sample

            _lowerCAmelCase							  =							create_lora_layers(__snake_case						)
            model.set_attn_processor(__snake_case						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case    ,    cross_attention_kwargs={"""scale""": 0.0}						).sample

            model.set_attn_processor(AttnProcessor()						)

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						).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        lowercase__					(    self    :				Union[str, Any]						)       ->   Tuple:
            # enable deterministic behavior for gradient checkpointing
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()

            _lowerCAmelCase							  =							4

            torch.manual_seed(0						)
            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)
            _lowerCAmelCase							  =							create_lora_layers(__snake_case						)
            model.set_attn_processor(__snake_case						)

            # default
            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						).sample

                  model.enable_xformers_memory_efficient_attention()
                  _lowerCAmelCase							  =							model(**__snake_case						).sample

                  model.disable_xformers_memory_efficient_attention()
                  _lowerCAmelCase							  =							model(**__snake_case						).sample

            assert (sample - on_sample).abs().max() < 1E-4
            assert (sample - off_sample).abs().max() < 1E-4




      def        lowercase__					(    self    :				Dict						)       ->   Optional[int]:
            _lowerCAmelCase  ,							_lowerCAmelCase							  =							self.prepare_init_args_and_inputs_for_common()
            _lowerCAmelCase							  =							32

            _lowerCAmelCase							  =							self.model_class(**__snake_case						)
            model.to(__snake_case						)
            model.eval()

            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						)[0]

            model.enable_forward_chunking()
            with torch.no_grad():
                  _lowerCAmelCase							  =							model(**__snake_case						)[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.






