



'''simple docstring'''





import tensorflow as tf

from ...tf_utils import shape_list






class 					lowerCamelCase_    (				tf.keras.layers.Layer     ):
  """simple docstring"""




  def __init__( self      :	str			,	_a      :	int			,	_a      :	Optional[int]			,	_a      :	Union[str, Any]			,	_a      :	Tuple			,	_a      :	List[Any]=1			,	_a      :	int=False			,	**_a      :	List[str]   )					->					str:
    super().__init__(**_a   )

    __lowerCamelCase       :       Optional[Any]           = vocab_size
    __lowerCamelCase       :       List[str]           = d_embed
    __lowerCamelCase       :       str           = d_proj

    __lowerCamelCase       :       Dict           = cutoffs + [vocab_size]
    __lowerCamelCase       :       Tuple           = [0] + self.cutoffs
    __lowerCamelCase       :       str           = div_val

    __lowerCamelCase       :       List[Any]           = self.cutoffs[0]
    __lowerCamelCase       :       Union[str, Any]           = len(self.cutoffs   ) - 1
    __lowerCamelCase       :       List[Any]           = self.shortlist_size + self.n_clusters
    __lowerCamelCase       :       Optional[int]           = keep_order

    __lowerCamelCase       :       Optional[Any]           = []
    __lowerCamelCase       :       int           = []




  def 						_lowercase     ( self      :	Tuple			,	_a      :	Optional[int]   )					->					Any:
    if self.n_clusters > 0:
      __lowerCamelCase       :       Any           = self.add_weight(
          shape=(self.n_clusters, self.d_embed)			,	initializer='zeros'			,	trainable=_a			,	name='cluster_weight'   )
      __lowerCamelCase       :       str           = self.add_weight(
          shape=(self.n_clusters,)			,	initializer='zeros'			,	trainable=_a			,	name='cluster_bias'   )

    if self.div_val == 1:
      for i in range(len(self.cutoffs   )   ):
        if self.d_proj != self.d_embed:
          __lowerCamelCase       :       List[Any]           = self.add_weight(
              shape=(self.d_embed, self.d_proj)			,	initializer='zeros'			,	trainable=_a			,	name=f'out_projs_._{i}'			,	)
          self.out_projs.append(_a   )
        else:
          self.out_projs.append(_a   )
        __lowerCamelCase       :       Dict           = self.add_weight(
            shape=(self.vocab_size, self.d_embed)			,	initializer='zeros'			,	trainable=_a			,	name=f'out_layers_._{i}_._weight'			,	)
        __lowerCamelCase       :       str           = self.add_weight(
            shape=(self.vocab_size,)			,	initializer='zeros'			,	trainable=_a			,	name=f'out_layers_._{i}_._bias'			,	)
        self.out_layers.append((weight, bias)   )
    else:
      for i in range(len(self.cutoffs   )   ):
        __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = self.cutoff_ends[i], self.cutoff_ends[i + 1]
        __lowerCamelCase       :       Optional[int]           = self.d_embed // (self.div_val**i)

        __lowerCamelCase       :       Optional[int]           = self.add_weight(
            shape=(d_emb_i, self.d_proj)			,	initializer='zeros'			,	trainable=_a			,	name=f'out_projs_._{i}'   )
        self.out_projs.append(_a   )
        __lowerCamelCase       :       str           = self.add_weight(
            shape=(r_idx - l_idx, d_emb_i)			,	initializer='zeros'			,	trainable=_a			,	name=f'out_layers_._{i}_._weight'			,	)
        __lowerCamelCase       :       int           = self.add_weight(
            shape=(r_idx - l_idx,)			,	initializer='zeros'			,	trainable=_a			,	name=f'out_layers_._{i}_._bias'			,	)
        self.out_layers.append((weight, bias)   )
    super().build(_a   )




  @staticmethod
  def 						_lowercase     ( _a      :	Dict			,	_a      :	str			,	_a      :	List[Any]			,	_a      :	Any=None   )					->					str:
    __lowerCamelCase       :       Optional[int]           = x
    if proj is not None:
      __lowerCamelCase       :       Optional[int]           = tf.einsum('ibd,ed->ibe'			,	_a			,	_a   )
    return tf.einsum('ibd,nd->ibn'			,	_a			,	_a   ) + b




  @staticmethod
  def 						_lowercase     ( _a      :	str			,	_a      :	str   )					->					int:
    __lowerCamelCase       :       int           = shape_list(_a   )
    __lowerCamelCase       :       Dict           = tf.range(lp_size[0]			,	dtype=target.dtype   )
    __lowerCamelCase       :       Optional[Any]           = tf.stack([r, target]			,	1   )
    return tf.gather_nd(_a			,	_a   )




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	Dict			,	_a      :	Any			,	_a      :	Union[str, Any]=True			,	_a      :	List[Any]=False   )					->					Tuple:
    __lowerCamelCase       :       Any           = 0
    if self.n_clusters == 0:
      __lowerCamelCase       :       int           = self._logit(_a			,	self.out_layers[0][0]			,	self.out_layers[0][1]			,	self.out_projs[0]   )
      if target is not None:
        __lowerCamelCase       :       int           = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=_a			,	logits=_a   )
      __lowerCamelCase       :       Optional[int]           = tf.nn.log_softmax(_a			,	axis=-1   )
    else:
      __lowerCamelCase       :       List[str]           = shape_list(_a   )
      __lowerCamelCase       :       int           = []
      __lowerCamelCase       :       Union[str, Any]           = tf.zeros(hidden_sizes[:2]   )
      for i in range(len(self.cutoffs   )   ):
        __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = self.cutoff_ends[i], self.cutoff_ends[i + 1]
        if target is not None:
          __lowerCamelCase       :       Optional[int]           = (target >= l_idx) & (target < r_idx)
          __lowerCamelCase       :       Union[str, Any]           = tf.where(_a   )
          __lowerCamelCase       :       Optional[Any]           = tf.boolean_mask(_a			,	_a   ) - l_idx

        if self.div_val == 1:
          __lowerCamelCase       :       Union[str, Any]           = self.out_layers[0][0][l_idx:r_idx]
          __lowerCamelCase       :       Union[str, Any]           = self.out_layers[0][1][l_idx:r_idx]
        else:
          __lowerCamelCase       :       Dict           = self.out_layers[i][0]
          __lowerCamelCase       :       List[Any]           = self.out_layers[i][1]

        if i == 0:
          __lowerCamelCase       :       Union[str, Any]           = tf.concat([cur_W, self.cluster_weight]			,	0   )
          __lowerCamelCase       :       Union[str, Any]           = tf.concat([cur_b, self.cluster_bias]			,	0   )

          __lowerCamelCase       :       List[Any]           = self._logit(_a			,	_a			,	_a			,	self.out_projs[0]   )
          __lowerCamelCase       :       str           = tf.nn.log_softmax(_a   )
          out.append(head_logprob[..., : self.cutoffs[0]]   )
          if target is not None:
            __lowerCamelCase       :       Tuple           = tf.boolean_mask(_a			,	_a   )
            __lowerCamelCase       :       List[Any]           = self._gather_logprob(_a			,	_a   )
        else:
          __lowerCamelCase       :       int           = self._logit(_a			,	_a			,	_a			,	self.out_projs[i]   )
          __lowerCamelCase       :       Optional[int]           = tf.nn.log_softmax(_a   )
          __lowerCamelCase       :       Tuple           = self.cutoffs[0] + i - 1  # No probability for the head cluster
          __lowerCamelCase       :       Optional[int]           = head_logprob[..., cluster_prob_idx, None] + tail_logprob
          out.append(_a   )
          if target is not None:
            __lowerCamelCase       :       int           = tf.boolean_mask(_a			,	_a   )
            __lowerCamelCase       :       Optional[int]           = tf.boolean_mask(_a			,	_a   )
            __lowerCamelCase       :       Tuple           = self._gather_logprob(_a			,	_a   )
            cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
        if target is not None:
          loss += tf.scatter_nd(_a			,	-cur_logprob			,	shape_list(_a   )   )
      __lowerCamelCase       :       str           = tf.concat(_a			,	axis=-1   )

    if target is not None:
      if return_mean:
        __lowerCamelCase       :       Union[str, Any]           = tf.reduce_mean(_a   )
      # Add the training-time loss value to the layer using `self.add_loss()`.
      self.add_loss(_a   )

      # Log the loss as a metric (we could log arbitrary metrics,
      # including different metrics for training and inference.
      self.add_metric(_a			,	name=self.name			,	aggregation='mean' if return_mean else ''   )

    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






