
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)

def 		UpperCamelCase						(   lowercase_			,			lowercase_=False )		->						Dict:


  '''simple docstring'''






  try:
    lowercase__	: Any     		=   os.environ[key]
  except KeyError:
    # KEY isn't set, default to `default`.
    lowercase__	: str     		=   default
  else:
    # KEY is set, convert it to True or False.
    try:
      lowercase__	: Union[str, Any]     		=   strtobool(lowercase_ )
    except ValueError:
      # More values are supported, but let's keep the message simple.
      raise ValueError(F'If set, {key} must be yes or no.' )
  return _value


lowerCamelCase__     :		Union[str, Any]        = parse_flag_from_env("""RUN_SLOW""", default=False)

def 		UpperCamelCase						(   lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  return unittest.skip("""Test was skipped""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						List[str]:


  '''simple docstring'''






  return unittest.skipUnless(_run_slow_tests			,			"""test is slow""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Tuple:


  '''simple docstring'''






  return unittest.skipUnless(not torch.cuda.is_available()			,			"""test requires only a CPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  return unittest.skipUnless(torch.cuda.is_available()			,			"""test requires a GPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						List[str]:


  '''simple docstring'''






  return unittest.skipUnless(is_xpu_available()			,			"""test requires a XPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  return unittest.skipUnless(is_mps_available()			,			"""test requires a `mps` backend support in `torch`""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  return unittest.skipUnless(
      is_transformers_available() and is_datasets_available()			,			"""test requires the Hugging Face suite""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  return unittest.skipUnless(is_bnb_available()			,			"""test requires the bitsandbytes library""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return unittest.skipUnless(is_tpu_available()			,			"""test requires TPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  return unittest.skipUnless(torch.cuda.device_count() == 1			,			"""test requires a GPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return unittest.skipUnless(torch.xpu.device_count() == 1			,			"""test requires a XPU""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return unittest.skipUnless(torch.cuda.device_count() > 1			,			"""test requires multiple GPUs""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  return unittest.skipUnless(torch.xpu.device_count() > 1			,			"""test requires multiple XPUs""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  return unittest.skipUnless(is_safetensors_available()			,			"""test requires safetensors""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return unittest.skipUnless(is_deepspeed_available()			,			"""test requires DeepSpeed""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Tuple:


  '''simple docstring'''






  return unittest.skipUnless(is_torch_version(""">="""			,			"""1.12.0""" )			,			"""test requires torch version >= 1.12.0""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_=None			,			lowercase_=None )		->						List[str]:


  '''simple docstring'''






  if test_case is None:
    return partial(lowercase_			,			version=lowercase_ )
  return unittest.skipUnless(is_torch_version(""">="""			,			lowercase_ )			,			F'test requires torch version >= {version}' )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  return unittest.skipUnless(is_tensorboard_available()			,			"""test requires Tensorboard""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return unittest.skipUnless(is_wandb_available()			,			"""test requires wandb""" )(lowercase_ )

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  return unittest.skipUnless(is_comet_ml_available()			,			"""test requires comet_ml""" )(lowercase_ )


lowerCamelCase__     :		Any        = (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  return unittest.skipUnless(
      _atleast_one_tracker_available			,			"""test requires at least one tracker to be available and for `comet_ml` to not be installed"""			,			)(lowercase_ )



class    _snake_case (						unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      True


 @classmethod
 def   lowercase__  (       cls):


   '''simple docstring'''




   lowercase__	: Tuple     		=   tempfile.mkdtemp()


 @classmethod
 def   lowercase__  (       cls):


   '''simple docstring'''




   if os.path.exists(cls.tmpdir):
     shutil.rmtree(cls.tmpdir)



 def   lowercase__  (       self):


   '''simple docstring'''




   if self.clear_on_setup:
     for path in Path(self.tmpdir).glob("""**/*"""):
       if path.is_file():
         path.unlink()
       elif path.is_dir():
         shutil.rmtree(SCREAMING_SNAKE_CASE_)



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   super().tearDown()
   # Reset the state of the AcceleratorState singleton.
   AcceleratorState._reset_state()
   PartialState._reset_state()



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   mocks if isinstance(SCREAMING_SNAKE_CASE_       ,	(tuple, list)) else [mocks]
   for m in self.mocks:
     m.start()
     self.addCleanup(m.stop)

def 		UpperCamelCase						(   lowercase_ )		->						Dict:


  '''simple docstring'''






  lowercase__	: Tuple     		=   AcceleratorState()
  lowercase__	: Union[str, Any]     		=   tensor[None].clone().to(state.device )
  lowercase__	: List[str]     		=   gather(lowercase_ ).cpu()
  lowercase__	: List[Any]     		=   tensor[0].cpu()
  for i in range(tensors.shape[0] ):
    if not torch.equal(tensors[i]			,			lowercase_ ):
      return False
  return True



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   returncode
   lowercase__	: Dict     		=   stdout
   lowercase__	: str     		=   stderr

async def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  while True:
    lowercase__	: str     		=   await stream.readline()
    if line:
      callback(lowercase_ )
    else:
      break

async def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=False			,			lowercase_=False )		->						_RunOutput:


  '''simple docstring'''






  if echo:
    print("""\nRunning: """			,			""" """.join(lowercase_ ) )

  lowercase__	: int     		=   await asyncio.create_subprocess_exec(
      cmd[0]			,			*cmd[1:]			,			stdin=lowercase_			,			stdout=asyncio.subprocess.PIPE			,			stderr=asyncio.subprocess.PIPE			,			env=lowercase_			,			)

  # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
  # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
  #
  # If it starts hanging, will need to switch to the following code. The problem is that no data
  # will be seen until it's done and if it hangs for example there will be no debug info.
  # out, err = await p.communicate()
  # return _RunOutput(p.returncode, out, err)

  lowercase__	: Optional[Any]     		=   []
  lowercase__	: Optional[Any]     		=   []

  def tee(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_="" ):
    lowercase__	: Union[str, Any]     		=   line.decode("""utf-8""" ).rstrip()
    sink.append(lowercase_ )
    if not quiet:
      print(lowercase_			,			lowercase_			,			file=lowercase_ )

    # XXX: the timeout doesn't seem to make any difference here
  await asyncio.wait(
      [
          asyncio.create_task(_read_stream(p.stdout			,			lambda lowercase_ : tee(lowercase_			,			lowercase_			,			sys.stdout			,			label="""stdout:""" ) ) ),
          asyncio.create_task(_read_stream(p.stderr			,			lambda lowercase_ : tee(lowercase_			,			lowercase_			,			sys.stderr			,			label="""stderr:""" ) ) ),
      ]			,			timeout=lowercase_			,			)
  return _RunOutput(await p.wait()			,			lowercase_			,			lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=1_80			,			lowercase_=False			,			lowercase_=True )		->						_RunOutput:


  '''simple docstring'''






  lowercase__	: Dict     		=   asyncio.get_event_loop()
  lowercase__	: List[str]     		=   loop.run_until_complete(
      _stream_subprocess(lowercase_			,			env=lowercase_			,			stdin=lowercase_			,			timeout=lowercase_			,			quiet=lowercase_			,			echo=lowercase_ ) )

  lowercase__	: Dict     		=   """ """.join(lowercase_ )
  if result.returncode > 0:
    lowercase__	: Optional[Any]     		=   """\n""".join(result.stderr )
    raise RuntimeError(
        F'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
        F'The combined stderr from workers follows:\n{stderr}' )

  return result



class    _snake_case (						UpperCAmelCase_				):
 pass

def 		UpperCamelCase						(   lowercase_			,			lowercase_=False )		->						Union[str, Any]:


  '''simple docstring'''






  try:
    lowercase__	: Optional[Any]     		=   subprocess.check_output(lowercase_			,			stderr=subprocess.STDOUT )
    if return_stdout:
      if hasattr(lowercase_			,			"""decode""" ):
        lowercase__	: Tuple     		=   output.decode("""utf-8""" )
      return output
  except subprocess.CalledProcessError as e:
    raise SubprocessCallException(
        F'Command `{" ".join(lowercase_ )}` failed with the following error:\n\n{e.output.decode()}' ) from e








# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					StableDiffusionControlNetImgaImgPipeline
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowerCAmelCase__   =					IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}		)
	lowerCAmelCase__   =					IMAGE_TO_IMAGE_IMAGE_PARAMS



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					torch.manual_seed(0					)
					lowerCamelCase_         =     UNetaDConditionModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    sample_size=32		,    in_channels=4		,    out_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,    cross_attention_dim=32		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     ControlNetModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    in_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    cross_attention_dim=32		,    conditioning_embedding_out_channels=(16, 32)		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     DDIMScheduler(
					    beta_start=0.0_0_0_8_5		,    beta_end=0.0_1_2		,    beta_schedule="scaled_linear"		,    clip_sample=lowercase		,    set_alpha_to_one=lowercase		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     AutoencoderKL(
					    block_out_channels=[32, 64]		,    in_channels=3		,    out_channels=3		,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,    latent_channels=4		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     CLIPTextConfig(
					    bos_token_id=0		,    eos_token_id=2		,    hidden_size=32		,    intermediate_size=37		,    layer_norm_eps=1e-05		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=1000		,    )
					lowerCamelCase_         =     CLIPTextModel(lowercase					)
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

					lowerCamelCase_         =     {
					    "unet": unet,
					    "controlnet": controlnet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			int:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)

					lowerCamelCase_         =     2
					lowerCamelCase_         =     randn_tensor(
					    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,    generator=lowercase		,    device=torch.device(lowercase					)		,    )
					lowerCamelCase_         =     floats_tensor(control_image.shape		,    rng=random.Random(lowercase					)					).to(lowercase					)
					lowerCamelCase_         =     image.cpu().permute(0		,    2		,    3		,    1					)[0]
					lowerCamelCase_         =     Image.fromarray(np.uinta(lowercase					)					).convert("RGB"					).resize((64, 64)					)
					lowerCamelCase_         =     {
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "output_type": "numpy",
					    "image": image,
					    "control_image": control_image,
					}

					return inputs



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return self._test_attention_slicing_forward_pass(expected_max_diff=2e-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					)							->			List[str]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					self._test_inference_batch_single_identical(expected_max_diff=2e-3					)




class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					StableDiffusionControlNetImgaImgPipeline
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
	lowerCAmelCase__   =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowerCAmelCase__   =					frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					torch.manual_seed(0					)
					lowerCamelCase_         =     UNetaDConditionModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    sample_size=32		,    in_channels=4		,    out_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,    cross_attention_dim=32		,    )
					torch.manual_seed(0					)

					def init_weights(lowercase					):
									if isinstance(lowercase		,    torch.nn.Convad					):
													torch.nn.init.normal(m.weight					)
													m.bias.data.fill_(1.0					)

					lowerCamelCase_         =     ControlNetModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    in_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    cross_attention_dim=32		,    conditioning_embedding_out_channels=(16, 32)		,    )
					controlneta.controlnet_down_blocks.apply(lowercase					)

					torch.manual_seed(0					)
					lowerCamelCase_         =     ControlNetModel(
					    block_out_channels=(32, 64)		,    layers_per_block=2		,    in_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,    cross_attention_dim=32		,    conditioning_embedding_out_channels=(16, 32)		,    )
					controlneta.controlnet_down_blocks.apply(lowercase					)

					torch.manual_seed(0					)
					lowerCamelCase_         =     DDIMScheduler(
					    beta_start=0.0_0_0_8_5		,    beta_end=0.0_1_2		,    beta_schedule="scaled_linear"		,    clip_sample=lowercase		,    set_alpha_to_one=lowercase		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     AutoencoderKL(
					    block_out_channels=[32, 64]		,    in_channels=3		,    out_channels=3		,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,    latent_channels=4		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     CLIPTextConfig(
					    bos_token_id=0		,    eos_token_id=2		,    hidden_size=32		,    intermediate_size=37		,    layer_norm_eps=1e-05		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=1000		,    )
					lowerCamelCase_         =     CLIPTextModel(lowercase					)
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

					lowerCamelCase_         =     MultiControlNetModel([controlneta, controlneta]					)

					lowerCamelCase_         =     {
					    "unet": unet,
					    "controlnet": controlnet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			List[Any]:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)

					lowerCamelCase_         =     2

					lowerCamelCase_         =     [
					    randn_tensor(
					        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,    generator=lowercase		,    device=torch.device(lowercase					)		,    ),
					    randn_tensor(
					        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,    generator=lowercase		,    device=torch.device(lowercase					)		,    ),
					]

					lowerCamelCase_         =     floats_tensor(control_image[0].shape		,    rng=random.Random(lowercase					)					).to(lowercase					)
					lowerCamelCase_         =     image.cpu().permute(0		,    2		,    3		,    1					)[0]
					lowerCamelCase_         =     Image.fromarray(np.uinta(lowercase					)					).convert("RGB"					).resize((64, 64)					)
					lowerCamelCase_         =     {
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 6.0,
					    "output_type": "numpy",
					    "image": image,
					    "control_image": control_image,
					}

					return inputs



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     self.pipeline_class(**lowercase					)
					pipe.to(lowercase					)

					lowerCamelCase_         =     1_0.0
					lowerCamelCase_         =     4

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase		,    control_guidance_start=0.1		,    control_guidance_end=0.2					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase		,    control_guidance_start=[0.1, 0.3]		,    control_guidance_end=[0.2, 0.7]					)[0]

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     pipe(**lowercase		,    control_guidance_start=0.4		,    control_guidance_end=[0.5, 0.8]					)[0]

					# make sure that all outputs are different
					assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
					assert np.sum(np.abs(output_a - output_a					)					) > 1e-3
					assert np.sum(np.abs(output_a - output_a					)					) > 1e-3



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					return self._test_attention_slicing_forward_pass(expected_max_diff=2e-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					)							->			Optional[int]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					self._test_inference_batch_single_identical(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     self.pipeline_class(**lowercase					)
					pipe.to(lowercase					)
					pipe.set_progress_bar_config(disable=lowercase					)
					with tempfile.TemporaryDirectory() as tmpdir:
									try:
													# save_pretrained is not implemented for Multi-ControlNet
													pipe.save_pretrained(lowercase					)
									except NotImplementedError:
													pass




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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_         =     ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"					)

					lowerCamelCase_         =     StableDiffusionControlNetImgaImgPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"		,    safety_checker=lowercase		,    controlnet=lowercase					)
					pipe.enable_model_cpu_offload()
					pipe.set_progress_bar_config(disable=lowercase					)

					lowerCamelCase_         =     torch.Generator(device="cpu"					).manual_seed(0					)
					lowerCamelCase_         =     "evil space-punk bird"
					lowerCamelCase_         =     load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"					).resize((512, 512)					)
					lowerCamelCase_         =     load_image(
					    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"					).resize((512, 512)					)

					lowerCamelCase_         =     pipe(
					    lowercase		,    lowercase		,    control_image=lowercase		,    generator=lowercase		,    output_type="np"		,    num_inference_steps=50		,    strength=0.6		,    )

					lowerCamelCase_         =     output.images[0]

					assert image.shape == (512, 512, 3)

					lowerCamelCase_         =     load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"					)

					assert np.abs(expected_image - image					).max() < 9e-2


