

"""simple docstring"""




# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


__UpperCAmelCase =				get_logger(__name__)







class      __lowercase			:
  snake_case_				       =       """dummy_data"""
  snake_case_				       =       """datasets"""
  snake_case_				       =       False

  def __init__(  self		:			Optional[Any]					,A		:			str					,A		:			str					,A		:			Union[Version, str]					,A		:			Optional[str] = None					,A		:			bool = False					,A		:			bool = True					,A		:			Optional[List[Callable]] = None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					0
   UpperCAmelCase__					:  int					   =					dataset_name
   UpperCAmelCase__					:  Dict					   =					cache_dir
   UpperCAmelCase__					:  int					   =					use_local_dummy_data
   UpperCAmelCase__					:  List[str]					   =					config
   # download_callbacks take a single url as input
   UpperCAmelCase__					:  List[Callable]					   =					download_callbacks or []
   # if False, it doesn't load existing files and it returns the paths of the dummy files relative
   # to the dummy_data zip file root
   UpperCAmelCase__					:  str					   =					load_existing_dummy_data

   # TODO(PVP, QL) might need to make this more general
   UpperCAmelCase__					:  str					   =					str(A    )
   # to be downloaded
   UpperCAmelCase__					:  Optional[Any]					   =					None
   UpperCAmelCase__					:  int					   =					None

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   if self._dummy_file is None:
    UpperCAmelCase__					:  Union[str, Any]					   =					self.download_dummy_data()
   return self._dummy_file

  @property
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   if self.config is not None:
    # structure is dummy / config_name / version_name
    return os.path.join("""dummy"""					,self.config.name					,self.version_name    )
   # structure is dummy / version_name
   return os.path.join("""dummy"""					,self.version_name    )

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   return os.path.join(self.dummy_data_folder					,"""dummy_data.zip"""    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					(
       self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
   )

   UpperCAmelCase__					:  Optional[Any]					   =					cached_path(
       A					,cache_dir=self.cache_dir					,extract_compressed_file=A					,force_extract=A    )

   return os.path.join(A					,self.dummy_file_name    )

  @property
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   return os.path.join(self.datasets_scripts_dir					,self.dataset_name					,self.dummy_zip_file    )

  @property
  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   if self._bucket_url is None:
    UpperCAmelCase__					:  Dict					   =					hf_github_url(self.dataset_name					,self.dummy_zip_file.replace(os.sep					,"""/"""    )    )
   return self._bucket_url

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   # return full path if its a dir
   if os.path.isdir(self.dummy_file    ):
    return self.dummy_file
   # else cut off path to file -> example `xsum`.
   return "/".join(self.dummy_file.replace(os.sep					,"""/"""    ).split("""/"""    )[:-1]    )

  def    __lowercase       (  self		:			List[str]					,A		:			Tuple					,*A		:			Tuple    ):






   '''simple docstring'''
   if self.load_existing_dummy_data:
    # dummy data is downloaded and tested
    UpperCAmelCase__					:  Optional[int]					   =					self.dummy_file
   else:
    # dummy data cannot be downloaded and only the path to dummy file is returned
    UpperCAmelCase__					:  Optional[Any]					   =					self.dummy_file_name

   # special case when data_url is a dict
   if isinstance(A					,A    ):
    return self.create_dummy_data_dict(A					,A    )
   elif isinstance(A					,(list, tuple)    ):
    return self.create_dummy_data_list(A					,A    )
   else:
    return self.create_dummy_data_single(A					,A    )

  def    __lowercase       (  self		:			List[Any]					,A		:			Tuple					,*A		:			Any    ):






   '''simple docstring'''
   return self.download_and_extract(A    )

  def    __lowercase       (  self		:			List[Any]					,A		:			List[Any]					,A		:			List[str]    ):






   '''simple docstring'''
   return self.download_and_extract(A    )

  def    __lowercase       (  self		:			Dict					,A		:			Tuple					,*A		:			Dict					,**A		:			int    ):






   '''simple docstring'''
   return path

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   return {}

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[int]					,A		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					{}
   for key, single_urls in data_url.items():
    for download_callback in self.download_callbacks:
     if isinstance(A					,A    ):
      for single_url in single_urls:
       download_callback(A    )
     else:
      UpperCAmelCase__					:  str					   =					single_urls
      download_callback(A    )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
    if isinstance(A					,A    ):
     UpperCAmelCase__					:  str					   =					[os.path.join(A					,urllib.parse.quote_plus(Path(A    ).name    )    ) for x in single_urls]
    else:
     UpperCAmelCase__					:  Tuple					   =					single_urls
     UpperCAmelCase__					:  Optional[int]					   =					os.path.join(A					,urllib.parse.quote_plus(Path(A    ).name    )    )
    UpperCAmelCase__					:  List[str]					   =					value

   # make sure that values are unique
   if all(isinstance(A					,A    ) for i in dummy_data_dict.values()    ) and len(set(dummy_data_dict.values()    )    ) < len(
       dummy_data_dict.values()    ):
    # append key to value to make its name unique
    UpperCAmelCase__					:  Optional[Any]					   =					{key: value + key for key, value in dummy_data_dict.items()}

   return dummy_data_dict

  def    __lowercase       (  self		:			Any					,A		:			Optional[Any]					,A		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					[]
   # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
   UpperCAmelCase__					:  int					   =					all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}"""					,A    )    ) for url in data_url    )
   UpperCAmelCase__					:  List[Any]					   =					all(
       url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"""    ) for url in data_url    )
   if data_url and (is_tf_records or is_pubmed_records):
    UpperCAmelCase__					:  Optional[int]					   =					[data_url[0]] * len(A    )
   for single_url in data_url:
    for download_callback in self.download_callbacks:
     download_callback(A    )
    # we force the name of each key to be the last file / folder name of the url path
    # if the url has arguments, we need to encode them with urllib.parse.quote_plus
    UpperCAmelCase__					:  Optional[int]					   =					os.path.join(A					,urllib.parse.quote_plus(single_url.split("""/"""    )[-1]    )    )
    dummy_data_list.append(A    )
   return dummy_data_list

  def    __lowercase       (  self		:			Tuple					,A		:			str					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   for download_callback in self.download_callbacks:
    download_callback(A    )
   # we force the name of each key to be the last file / folder name of the url path
   # if the url has arguments, we need to encode them with urllib.parse.quote_plus
   UpperCAmelCase__					:  List[Any]					   =					os.path.join(A					,urllib.parse.quote_plus(data_url.split("""/"""    )[-1]    )    )
   if os.path.exists(A    ) or not self.load_existing_dummy_data:
    return value
   else:
    # Backward compatibility, maybe deprecate at one point.
    # For many datasets with single url calls to dl_manager.download_and_extract,
    # the dummy_data.zip file is actually the zipped downloaded file
    # while now we expected the dummy_data.zip file to be a directory containing
    # the downloaded file.
    return path_to_dummy_data

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			str					,A		:			int    ):






   '''simple docstring'''
   def _iter_archive_members(A		:			Optional[Any]    ):
    # this preserves the order of the members inside the ZIP archive
    UpperCAmelCase__					:  Tuple					   =					Path(self.dummy_file    ).parent
    UpperCAmelCase__					:  List[Any]					   =					path.relative_to(A    )
    with ZipFile(self.local_path_to_dummy_data    ) as zip_file:
     UpperCAmelCase__					:  List[str]					   =					zip_file.namelist()
    for member in members:
     if member.startswith(relative_path.as_posix()    ):
      yield dummy_parent_path.joinpath(A    )

   UpperCAmelCase__					:  List[Any]					   =					Path(A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					_iter_archive_members(A    ) if self.use_local_dummy_data else path.rglob("""*"""    )
   for file_path in file_paths:
    if file_path.is_file() and not file_path.name.startswith((""".""", """__""")    ):
     yield file_path.relative_to(A    ).as_posix(), file_path.open("""rb"""    )




  def    __lowercase       (  self		:			List[str]					,A		:			str    ):






   '''simple docstring'''
   if not isinstance(A					,A    ):
    UpperCAmelCase__					:  Tuple					   =					[paths]
   for path in paths:
    if os.path.isfile(A    ):
     if os.path.basename(A    ).startswith((""".""", """__""")    ):
      return
     yield path
    else:
     for dirpath, dirnames, filenames in os.walk(A    ):
      if os.path.basename(A    ).startswith((""".""", """__""")    ):
       continue
      dirnames.sort()
      for filename in sorted(A    ):
       if filename.startswith((""".""", """__""")    ):
        continue
       yield os.path.join(A					,A    )








"""simple docstring"""




# 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      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       StableDiffusionControlNetImgaImgPipeline
  snake_case_				       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
  snake_case_				       =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  snake_case_				       =       IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}					)
  snake_case_				       =       IMAGE_TO_IMAGE_IMAGE_PARAMS

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   torch.manual_seed(0    )
   UpperCAmelCase__					:  Union[str, Any]					   =					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    )
   UpperCAmelCase__					:  Optional[int]					   =					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    )
   UpperCAmelCase__					:  Tuple					   =					DDIMScheduler(
       beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule="""scaled_linear"""					,clip_sample=A					,set_alpha_to_one=A					,)
   torch.manual_seed(0    )
   UpperCAmelCase__					:  Any					   =					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    )
   UpperCAmelCase__					:  int					   =					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=1_000					,)
   UpperCAmelCase__					:  List[str]					   =					CLIPTextModel(A    )
   UpperCAmelCase__					:  Dict					   =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

   UpperCAmelCase__					:  Optional[int]					   =					{
       """unet""": unet,
       """controlnet""": controlnet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }
   return components

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Dict					,A		:			Optional[Any]=0    ):






   '''simple docstring'''
   if str(A    ).startswith("""mps"""    ):
    UpperCAmelCase__					:  List[str]					   =					torch.manual_seed(A    )
   else:
    UpperCAmelCase__					:  List[Any]					   =					torch.Generator(device=A    ).manual_seed(A    )

   UpperCAmelCase__					:  Tuple					   =					2
   UpperCAmelCase__					:  Any					   =					randn_tensor(
       (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,generator=A					,device=torch.device(A    )					,)
   UpperCAmelCase__					:  str					   =					floats_tensor(control_image.shape					,rng=random.Random(A    )    ).to(A    )
   UpperCAmelCase__					:  Dict					   =					image.cpu().permute(0					,2					,3					,1    )[0]
   UpperCAmelCase__					:  List[str]					   =					Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    ).resize((64, 64)    )
   UpperCAmelCase__					:  Any					   =					{
       """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    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   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    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3    )




  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   self._test_inference_batch_single_identical(expected_max_diff=2e-3    )







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

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   torch.manual_seed(0    )
   UpperCAmelCase__					:  Tuple					   =					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(A		:			Union[str, Any]    ):
    if isinstance(A					,torch.nn.Convad    ):
     torch.nn.init.normal(m.weight    )
     m.bias.data.fill_(1.0    )

   UpperCAmelCase__					:  int					   =					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(A    )

   torch.manual_seed(0    )
   UpperCAmelCase__					:  Dict					   =					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(A    )

   torch.manual_seed(0    )
   UpperCAmelCase__					:  str					   =					DDIMScheduler(
       beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule="""scaled_linear"""					,clip_sample=A					,set_alpha_to_one=A					,)
   torch.manual_seed(0    )
   UpperCAmelCase__					:  Dict					   =					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    )
   UpperCAmelCase__					:  Tuple					   =					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=1_000					,)
   UpperCAmelCase__					:  Optional[Any]					   =					CLIPTextModel(A    )
   UpperCAmelCase__					:  List[str]					   =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

   UpperCAmelCase__					:  Dict					   =					MultiControlNetModel([controlneta, controlneta]    )

   UpperCAmelCase__					:  Union[str, Any]					   =					{
       """unet""": unet,
       """controlnet""": controlnet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }
   return components

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[Any]					,A		:			List[Any]=0    ):






   '''simple docstring'''
   if str(A    ).startswith("""mps"""    ):
    UpperCAmelCase__					:  List[str]					   =					torch.manual_seed(A    )
   else:
    UpperCAmelCase__					:  int					   =					torch.Generator(device=A    ).manual_seed(A    )

   UpperCAmelCase__					:  Optional[int]					   =					2

   UpperCAmelCase__					:  List[Any]					   =					[
       randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,generator=A					,device=torch.device(A    )					,),
       randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,generator=A					,device=torch.device(A    )					,),
   ]

   UpperCAmelCase__					:  List[Any]					   =					floats_tensor(control_image[0].shape					,rng=random.Random(A    )    ).to(A    )
   UpperCAmelCase__					:  str					   =					image.cpu().permute(0					,2					,3					,1    )[0]
   UpperCAmelCase__					:  Union[str, Any]					   =					Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    ).resize((64, 64)    )
   UpperCAmelCase__					:  Any					   =					{
       """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    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					self.get_dummy_components()
   UpperCAmelCase__					:  List[str]					   =					self.pipeline_class(**A    )
   pipe.to(A    )

   UpperCAmelCase__					:  Any					   =					1_0.0
   UpperCAmelCase__					:  Any					   =					4

   UpperCAmelCase__					:  Optional[int]					   =					self.get_dummy_inputs(A    )
   UpperCAmelCase__					:  Optional[Any]					   =					steps
   UpperCAmelCase__					:  List[str]					   =					scale
   UpperCAmelCase__					:  Optional[Any]					   =					pipe(**A    )[0]

   UpperCAmelCase__					:  Any					   =					self.get_dummy_inputs(A    )
   UpperCAmelCase__					:  Optional[int]					   =					steps
   UpperCAmelCase__					:  Tuple					   =					scale
   UpperCAmelCase__					:  List[Any]					   =					pipe(**A					,control_guidance_start=0.1					,control_guidance_end=0.2    )[0]

   UpperCAmelCase__					:  List[str]					   =					self.get_dummy_inputs(A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					steps
   UpperCAmelCase__					:  Any					   =					scale
   UpperCAmelCase__					:  str					   =					pipe(**A					,control_guidance_start=[0.1, 0.3]					,control_guidance_end=[0.2, 0.7]    )[0]

   UpperCAmelCase__					:  Any					   =					self.get_dummy_inputs(A    )
   UpperCAmelCase__					:  List[str]					   =					steps
   UpperCAmelCase__					:  Union[str, Any]					   =					scale
   UpperCAmelCase__					:  Union[str, Any]					   =					pipe(**A					,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    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   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    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3    )

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   self._test_inference_batch_single_identical(expected_max_diff=2e-3    )




  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					self.get_dummy_components()
   UpperCAmelCase__					:  str					   =					self.pipeline_class(**A    )
   pipe.to(A    )
   pipe.set_progress_bar_config(disable=A    )
   with tempfile.TemporaryDirectory() as tmpdir:
    try:
     # save_pretrained is not implemented for Multi-ControlNet
     pipe.save_pretrained(A    )
    except NotImplementedError:
     pass







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

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""    )

   UpperCAmelCase__					:  Tuple					   =					StableDiffusionControlNetImgaImgPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5"""					,safety_checker=A					,controlnet=A    )
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=A    )

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

   UpperCAmelCase__					:  List[str]					   =					pipe(
       A					,A					,control_image=A					,generator=A					,output_type="""np"""					,num_inference_steps=50					,strength=0.6					,)

   UpperCAmelCase__					:  Optional[Any]					   =					output.images[0]

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

   UpperCAmelCase__					:  Any					   =					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





