



'''simple docstring'''
import torch
from torch import nn



class _A     (			nn.Module			):





		def __init__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase=1       ,				__UpperCAmelCase=False )				->   Any:

					'''simple docstring'''
					super().__init__()

					__UpperCAmelCase      :					Dict   =   n_token
					__UpperCAmelCase      :					Union[str, Any]   =   d_embed
					__UpperCAmelCase      :					Dict   =   d_proj

					__UpperCAmelCase      :					Any   =   cutoffs + [n_token]
					__UpperCAmelCase      :					Dict   =   [0] + self.cutoffs
					__UpperCAmelCase      :					str   =   div_val

					__UpperCAmelCase      :					Dict   =   self.cutoffs[0]
					__UpperCAmelCase      :					List[Any]   =   len(self.cutoffs ) - 1
					__UpperCAmelCase      :					Union[str, Any]   =   self.shortlist_size + self.n_clusters

					if self.n_clusters > 0:
								__UpperCAmelCase      :					Any   =   nn.Parameter(torch.zeros(self.n_clusters       ,				self.d_embed ) )
								__UpperCAmelCase      :					Union[str, Any]   =   nn.Parameter(torch.zeros(self.n_clusters ) )

					__UpperCAmelCase      :					Optional[int]   =   nn.ModuleList()
					__UpperCAmelCase      :					Optional[Any]   =   nn.ParameterList()

					if div_val == 1:
								for i in range(len(self.cutoffs ) ):
											if d_proj != d_embed:
														self.out_projs.append(nn.Parameter(torch.FloatTensor(_a       ,				_a ) ) )
											else:
														self.out_projs.append(_a )

								self.out_layers.append(nn.Linear(_a       ,				_a ) )
					else:
								for i in range(len(self.cutoffs ) ):
											__UpperCAmelCase      :					Tuple   =   self.cutoff_ends[i], self.cutoff_ends[i + 1]
											__UpperCAmelCase      :					Union[str, Any]   =   d_embed // (div_val**i)

											self.out_projs.append(nn.Parameter(torch.FloatTensor(_a       ,				_a ) ) )

											self.out_layers.append(nn.Linear(_a       ,				r_idx - l_idx ) )

					__UpperCAmelCase      :					str   =   keep_order





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase )				->   Any:

					'''simple docstring'''
					if proj is None:
								__UpperCAmelCase      :					Optional[Any]   =   nn.functional.linear(_a       ,				_a       ,				bias=_a )
					else:
								# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
								__UpperCAmelCase      :					int   =   nn.functional.linear(_a       ,				proj.t().contiguous() )
								__UpperCAmelCase      :					int   =   nn.functional.linear(_a       ,				_a       ,				bias=_a )
								# else:
								#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
								#     if bias is not None:
								#         logit = logit + bias

					return logit





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=None       ,				__UpperCAmelCase=False )				->   Tuple:

					'''simple docstring'''

					if labels is not None:
								# Shift so that tokens < n predict n
								__UpperCAmelCase      :					str   =   hidden[..., :-1, :].contiguous()
								__UpperCAmelCase      :					str   =   labels[..., 1:].contiguous()
								__UpperCAmelCase      :					List[Any]   =   hidden.view(-1       ,				hidden.size(-1 ) )
								__UpperCAmelCase      :					Optional[Any]   =   labels.view(-1 )
								if hidden.size(0 ) != labels.size(0 ):
											raise RuntimeError("""Input and labels should have the same size in the batch dimension.""" )
					else:
								__UpperCAmelCase      :					int   =   hidden.view(-1       ,				hidden.size(-1 ) )

					if self.n_clusters == 0:
								__UpperCAmelCase      :					int   =   self._compute_logit(_a       ,				self.out_layers[0].weight       ,				self.out_layers[0].bias       ,				self.out_projs[0] )
								if labels is not None:
											__UpperCAmelCase      :					Any   =   labels != -100
											__UpperCAmelCase      :					Dict   =   torch.zeros_like(_a       ,				dtype=hidden.dtype       ,				device=hidden.device )
											__UpperCAmelCase      :					str   =   (
											    -nn.functional.log_softmax(_a       ,				dim=-1 )[mask].gather(1       ,				labels[mask].unsqueeze(1 ) ).squeeze(1 )
											)
								else:
											__UpperCAmelCase      :					Dict   =   nn.functional.log_softmax(_a       ,				dim=-1 )
					else:
								# construct weights and biases
								__UpperCAmelCase      :					int   =   [], []
								for i in range(len(self.cutoffs ) ):
											if self.div_val == 1:
														__UpperCAmelCase      :					List[Any]   =   self.cutoff_ends[i], self.cutoff_ends[i + 1]
														__UpperCAmelCase      :					Optional[Any]   =   self.out_layers[0].weight[l_idx:r_idx]
														__UpperCAmelCase      :					Tuple   =   self.out_layers[0].bias[l_idx:r_idx]
											else:
														__UpperCAmelCase      :					Dict   =   self.out_layers[i].weight
														__UpperCAmelCase      :					int   =   self.out_layers[i].bias

											if i == 0:
														__UpperCAmelCase      :					List[Any]   =   torch.cat([weight_i, self.cluster_weight]       ,				dim=0 )
														__UpperCAmelCase      :					Union[str, Any]   =   torch.cat([bias_i, self.cluster_bias]       ,				dim=0 )

											weights.append(_a )
											biases.append(_a )

								__UpperCAmelCase      :					Tuple   =   weights[0], biases[0], self.out_projs[0]

								__UpperCAmelCase      :					Optional[Any]   =   self._compute_logit(_a       ,				_a       ,				_a       ,				_a )
								__UpperCAmelCase      :					Optional[int]   =   nn.functional.log_softmax(_a       ,				dim=1 )

								if labels is None:
											__UpperCAmelCase      :					Tuple   =   hidden.new_empty((head_logit.size(0 ), self.n_token) )
								else:
											__UpperCAmelCase      :					List[str]   =   torch.zeros_like(_a       ,				dtype=hidden.dtype       ,				device=hidden.device )

								__UpperCAmelCase      :					str   =   0
								__UpperCAmelCase      :					Optional[Any]   =   [0] + self.cutoffs
								for i in range(len(_a ) - 1 ):
											__UpperCAmelCase      :					int   =   cutoff_values[i], cutoff_values[i + 1]

											if labels is not None:
														__UpperCAmelCase      :					List[Any]   =   (labels >= l_idx) & (labels < r_idx)
														__UpperCAmelCase      :					str   =   mask_i.nonzero().squeeze()

														if indices_i.numel() == 0:
																	continue

														__UpperCAmelCase      :					str   =   labels.index_select(0       ,				_a ) - l_idx
														__UpperCAmelCase      :					Tuple   =   head_logprob.index_select(0       ,				_a )
														__UpperCAmelCase      :					Dict   =   hidden.index_select(0       ,				_a )
											else:
														__UpperCAmelCase      :					Optional[int]   =   hidden

											if i == 0:
														if labels is not None:
																	__UpperCAmelCase      :					Dict   =   head_logprob_i.gather(1       ,				target_i[:, None] ).squeeze(1 )
														else:
																	__UpperCAmelCase      :					int   =   head_logprob[:, : self.cutoffs[0]]
											else:
														__UpperCAmelCase      :					Optional[int]   =   weights[i], biases[i], self.out_projs[i]

														__UpperCAmelCase      :					int   =   self._compute_logit(_a       ,				_a       ,				_a       ,				_a )
														__UpperCAmelCase      :					int   =   nn.functional.log_softmax(_a       ,				dim=1 )
														__UpperCAmelCase      :					Any   =   self.cutoffs[0] + i - 1  # No probability for the head cluster
														if labels is not None:
																	__UpperCAmelCase      :					List[Any]   =   head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																	    1       ,				target_i[:, None] ).squeeze(1 )
														else:
																	__UpperCAmelCase      :					Optional[Any]   =   head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																	__UpperCAmelCase      :					Optional[Any]   =   logprob_i

											if labels is not None:
														if (hasattr(self       ,				"""keep_order""" ) and self.keep_order) or keep_order:
																	out.index_copy_(0       ,				_a       ,				-logprob_i )
														else:
																	out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
														offset += logprob_i.size(0 )

					return out





		def 							__A				(   self       ,				__UpperCAmelCase )				->   Optional[int]:

					'''simple docstring'''
					if self.n_clusters == 0:
								__UpperCAmelCase      :					str   =   self._compute_logit(_a       ,				self.out_layers[0].weight       ,				self.out_layers[0].bias       ,				self.out_projs[0] )
								return nn.functional.log_softmax(_a       ,				dim=-1 )
					else:
								# construct weights and biases
								__UpperCAmelCase      :					Dict   =   [], []
								for i in range(len(self.cutoffs ) ):
											if self.div_val == 1:
														__UpperCAmelCase      :					Optional[Any]   =   self.cutoff_ends[i], self.cutoff_ends[i + 1]
														__UpperCAmelCase      :					Optional[Any]   =   self.out_layers[0].weight[l_idx:r_idx]
														__UpperCAmelCase      :					List[str]   =   self.out_layers[0].bias[l_idx:r_idx]
											else:
														__UpperCAmelCase      :					Tuple   =   self.out_layers[i].weight
														__UpperCAmelCase      :					Any   =   self.out_layers[i].bias

											if i == 0:
														__UpperCAmelCase      :					Dict   =   torch.cat([weight_i, self.cluster_weight]       ,				dim=0 )
														__UpperCAmelCase      :					Tuple   =   torch.cat([bias_i, self.cluster_bias]       ,				dim=0 )

											weights.append(_a )
											biases.append(_a )

								__UpperCAmelCase      :					Optional[Any]   =   weights[0], biases[0], self.out_projs[0]
								__UpperCAmelCase      :					List[str]   =   self._compute_logit(_a       ,				_a       ,				_a       ,				_a )

								__UpperCAmelCase      :					Union[str, Any]   =   hidden.new_empty((head_logit.size(0 ), self.n_token) )
								__UpperCAmelCase      :					Union[str, Any]   =   nn.functional.log_softmax(_a       ,				dim=1 )

								__UpperCAmelCase      :					str   =   [0] + self.cutoffs
								for i in range(len(_a ) - 1 ):
											__UpperCAmelCase      :					Union[str, Any]   =   cutoff_values[i], cutoff_values[i + 1]

											if i == 0:
														__UpperCAmelCase      :					Union[str, Any]   =   head_logprob[:, : self.cutoffs[0]]
											else:
														__UpperCAmelCase      :					str   =   weights[i], biases[i], self.out_projs[i]

														__UpperCAmelCase      :					List[Any]   =   self._compute_logit(_a       ,				_a       ,				_a       ,				_a )
														__UpperCAmelCase      :					Optional[Any]   =   nn.functional.log_softmax(_a       ,				dim=1 )

														__UpperCAmelCase      :					Union[str, Any]   =   head_logprob[:, -i] + tail_logprob_i
														__UpperCAmelCase      :					List[Any]   =   logprob_i

								return out









'''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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=StableDiffusionControlNetImgaImgPipeline
  a_												=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
  a_												=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  a_												=IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}     )
  a_												=IMAGE_TO_IMAGE_IMAGE_PARAMS




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					List[str]:
    torch.manual_seed(0   )
    __lowerCamelCase       :       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   )
    __lowerCamelCase       :       Any           = 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       :       Any           = DDIMScheduler(
        beta_start=0.00085			,	beta_end=0.012			,	beta_schedule='scaled_linear'			,	clip_sample=_a			,	set_alpha_to_one=_a			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       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   )
    __lowerCamelCase       :       Dict           = 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       :       Union[str, Any]           = CLIPTextModel(_a   )
    __lowerCamelCase       :       Any           = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

    __lowerCamelCase       :       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      :	Any			,	_a      :	List[str]=0   )					->					List[str]:
    if str(_a   ).startswith('mps'   ):
      __lowerCamelCase       :       Any           = torch.manual_seed(_a   )
    else:
      __lowerCamelCase       :       Any           = torch.Generator(device=_a   ).manual_seed(_a   )

    __lowerCamelCase       :       Union[str, Any]           = 2
    __lowerCamelCase       :       Any           = randn_tensor(
        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,	generator=_a			,	device=torch.device(_a   )			,	)
    __lowerCamelCase       :       Optional[Any]           = floats_tensor(control_image.shape			,	rng=random.Random(_a   )   ).to(_a   )
    __lowerCamelCase       :       List[Any]           = image.cpu().permute(0			,	2			,	3			,	1   )[0]
    __lowerCamelCase       :       int           = Image.fromarray(np.uinta(_a   )   ).convert('RGB'   ).resize((64, 64)   )
    __lowerCamelCase       :       int           = {
        '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      :	List[str]   )					->					Optional[Any]:
    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      :	str   )					->					List[str]:
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	Any   )					->					List[str]:
    self._test_inference_batch_single_identical(expected_max_diff=2e-3   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=StableDiffusionControlNetImgaImgPipeline
  a_												=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
  a_												=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  a_												=frozenset([]     )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




  def 						_lowercase     ( self      :	List[Any]   )					->					List[str]:
    torch.manual_seed(0   )
    __lowerCamelCase       :       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   )

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

    __lowerCamelCase       :       Any           = 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   )
    __lowerCamelCase       :       Any           = 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   )
    __lowerCamelCase       :       List[Any]           = DDIMScheduler(
        beta_start=0.00085			,	beta_end=0.012			,	beta_schedule='scaled_linear'			,	clip_sample=_a			,	set_alpha_to_one=_a			,	)
    torch.manual_seed(0   )
    __lowerCamelCase       :       str           = 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       :       Optional[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=1000			,	)
    __lowerCamelCase       :       Dict           = CLIPTextModel(_a   )
    __lowerCamelCase       :       Optional[Any]           = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

    __lowerCamelCase       :       List[str]           = MultiControlNetModel([controlneta, controlneta]   )

    __lowerCamelCase       :       str           = {
        '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      :	Dict			,	_a      :	int			,	_a      :	Union[str, Any]=0   )					->					Union[str, Any]:
    if str(_a   ).startswith('mps'   ):
      __lowerCamelCase       :       Optional[Any]           = torch.manual_seed(_a   )
    else:
      __lowerCamelCase       :       Union[str, Any]           = torch.Generator(device=_a   ).manual_seed(_a   )

    __lowerCamelCase       :       Any           = 2

    __lowerCamelCase       :       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   )			,	),
    ]

    __lowerCamelCase       :       List[Any]           = floats_tensor(control_image[0].shape			,	rng=random.Random(_a   )   ).to(_a   )
    __lowerCamelCase       :       Union[str, Any]           = image.cpu().permute(0			,	2			,	3			,	1   )[0]
    __lowerCamelCase       :       Dict           = Image.fromarray(np.uinta(_a   )   ).convert('RGB'   ).resize((64, 64)   )
    __lowerCamelCase       :       Dict           = {
        '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      :	Tuple   )					->					Union[str, Any]:
    __lowerCamelCase       :       Any           = self.get_dummy_components()
    __lowerCamelCase       :       Any           = self.pipeline_class(**_a   )
    pipe.to(_a   )

    __lowerCamelCase       :       Optional[int]           = 10.0
    __lowerCamelCase       :       Tuple           = 4

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

    __lowerCamelCase       :       Dict           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       List[str]           = steps
    __lowerCamelCase       :       List[Any]           = scale
    __lowerCamelCase       :       Dict           = pipe(**_a			,	control_guidance_start=0.1			,	control_guidance_end=0.2   )[0]

    __lowerCamelCase       :       Any           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       Union[str, Any]           = steps
    __lowerCamelCase       :       Any           = scale
    __lowerCamelCase       :       Optional[int]           = pipe(**_a			,	control_guidance_start=[0.1, 0.3]			,	control_guidance_end=[0.2, 0.7]   )[0]

    __lowerCamelCase       :       Any           = self.get_dummy_inputs(_a   )
    __lowerCamelCase       :       Tuple           = steps
    __lowerCamelCase       :       List[Any]           = scale
    __lowerCamelCase       :       List[str]           = 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      :	Optional[Any]   )					->					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 						_lowercase     ( self      :	Dict   )					->					Tuple:
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	str   )					->					int:
    self._test_inference_batch_single_identical(expected_max_diff=2e-3   )




  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    __lowerCamelCase       :       int           = self.get_dummy_components()
    __lowerCamelCase       :       List[Any]           = 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 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




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




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[Any]           = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'   )

    __lowerCamelCase       :       List[str]           = 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   )

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

    __lowerCamelCase       :       Dict           = pipe(
        _a			,	_a			,	control_image=_a			,	generator=_a			,	output_type='np'			,	num_inference_steps=50			,	strength=0.6			,	)

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

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

    __lowerCamelCase       :       List[str]           = 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






