




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
snake_case_ : int    		=       pytest.mark.integration


snake_case_ : str    		=       {"comet"}
snake_case_ : Optional[Any]    		=       importlib.util.find_spec("fairseq") is not None

snake_case_ : List[Any]    		=       {"code_eval"}
snake_case_ : str    		=       os.name == "nt"

snake_case_ : Any    		=       {"bertscore", "frugalscore", "perplexity"}
snake_case_ : str    		=       importlib.util.find_spec("transformers") is not None


def    A						(__A   :    Dict       )   ->      List[Any]:







     """simple docstring"""


     @wraps(__A       )
     def wrapper(self   :    Optional[int]   ,				__A   :    Tuple       ):
          if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
               self.skipTest('''"test requires Fairseq"'''       )
          else:
               test_case(self   ,				__A       )

     return wrapper


def    A						(__A   :    Tuple       )   ->      List[str]:







     """simple docstring"""


     @wraps(__A       )
     def wrapper(self   :    str   ,				__A   :    int       ):
          if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
               self.skipTest('''"test requires transformers"'''       )
          else:
               test_case(self   ,				__A       )

     return wrapper


def    A						(__A   :    int       )   ->      List[Any]:







     """simple docstring"""


     @wraps(__A       )
     def wrapper(self   :    List[Any]   ,				__A   :    str       ):
          if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
               self.skipTest('''"test not supported on Windows"'''       )
          else:
               test_case(self   ,				__A       )

     return wrapper


def    A						()   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							[metric_dir.split(os.sep       )[-2] for metric_dir in glob.glob('''./metrics/*/'''       )]
     return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished







@parameterized.named_parameters(get_local_metric_names()		)
@for_all_test_methods(
    a ,  a ,  a		)
@local
class __snake_case			(			parameterized.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  {}
   UpperCAmelCase__  :    List[str]          =  None







   @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''')
   @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning''')
   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''[...]'''
        UpperCAmelCase_          =							importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('''metrics'''					,						_snake_case)).module_path)
        UpperCAmelCase_          =							datasets.load.import_main_class(metric_module.__name__					,						dataset=_snake_case)
        # check parameters
        UpperCAmelCase_          =							inspect.signature(metric._compute).parameters
        self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()))  # no **kwargs
        # run doctest
        with self.patch_intensive_calls(_snake_case					,						metric_module.__name__):
             with self.use_local_metrics():
                  try:
                       UpperCAmelCase_          =							doctest.testmod(_snake_case					,						verbose=_snake_case					,						raise_on_error=_snake_case)
                  except doctest.UnexpectedException as e:
                       raise e.exc_info[1]  # raise the exception that doctest caught
        self.assertEqual(results.failed					,						0)
        self.assertGreater(results.attempted					,						1)







   @slow
   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''[...]'''
        UpperCAmelCase_          =							importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('''metrics'''					,						_snake_case)).module_path)
        # run doctest
        with self.use_local_metrics():
             UpperCAmelCase_          =							doctest.testmod(_snake_case					,						verbose=_snake_case					,						raise_on_error=_snake_case)
        self.assertEqual(results.failed					,						0)
        self.assertGreater(results.attempted					,						1)







   @contextmanager
   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        if metric_name in self.INTENSIVE_CALLS_PATCHER:
             with self.INTENSIVE_CALLS_PATCHER[metric_name](_snake_case):
                  yield
        else:
             yield







   @contextmanager
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        def load_local_metric(_snake_case		:						List[str]					,						*_snake_case		:						Union[str, Any]					,						**_snake_case		:						List[Any]):
             return load_metric(os.path.join('''metrics'''					,						_snake_case)					,						*_snake_case					,						**_snake_case)

        with patch('''datasets.load_metric''') as mock_load_metric:
             UpperCAmelCase_          =							load_local_metric
             yield







   @classmethod
   def lowerCamelCase						(      cls		:						List[str]					,						_snake_case		:						List[Any]):

        """simple docstring"""


        def wrapper(_snake_case		:						Optional[Any]):
             UpperCAmelCase_          =							contextmanager(_snake_case)
             UpperCAmelCase_          =							patcher
             return patcher

        return wrapper







@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''       )
def    A						(__A   :    Any       )   ->      List[str]:







     """simple docstring"""


     import tensorflow.compat.va as tf
     from bleurt.score import Predictor

     tf.flags.DEFINE_string('''sv'''   ,				''''''   ,				''''''       )  # handle pytest cli flags







     class __snake_case			(			a		):







        def lowerCamelCase						(      self		:						int					,						_snake_case		:						Optional[int]):

             """simple docstring"""


             assert len(input_dict['''input_ids''']) == 2
             return np.array([1.0_3, 1.0_4])

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
     with patch('''bleurt.score._create_predictor'''       ) as mock_create_predictor:
          UpperCAmelCase_          =							MockedPredictor()
          yield


@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''       )
def    A						(__A   :    int       )   ->      Tuple:







     """simple docstring"""


     import torch

     def bert_cos_score_idf(__A   :    str   ,				__A   :    Optional[Any]   ,				*__A   :    List[Any]   ,				**__A   :    List[str]       ):
          return torch.tensor([[1.0, 1.0, 1.0]] * len(__A       )       )

     # mock get_model which is supposed to do download a bert model
     # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
     with patch('''bert_score.scorer.get_model'''       ), patch(
         '''bert_score.scorer.bert_cos_score_idf'''       ) as mock_bert_cos_score_idf:
          UpperCAmelCase_          =							bert_cos_score_idf
          yield


@LocalMetricTest.register_intensive_calls_patcher('''comet'''       )
def    A						(__A   :    List[str]       )   ->      Optional[Any]:







     """simple docstring"""


     def load_from_checkpoint(__A   :    str       ):







          class __snake_case			:







             def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Optional[int]					,						*_snake_case		:						str					,						**_snake_case		:						Optional[int]):

                  """simple docstring"""


                  assert len(_snake_case) == 2
                  UpperCAmelCase_          =							[0.1_9, 0.9_2]
                  return scores, sum(_snake_case) / len(_snake_case)







          return Model()

     # mock load_from_checkpoint which is supposed to do download a bert model
     # mock load_from_checkpoint which is supposed to do download a bert model
     with patch('''comet.download_model'''       ) as mock_download_model:
          UpperCAmelCase_          =							None
          with patch('''comet.load_from_checkpoint'''       ) as mock_load_from_checkpoint:
               UpperCAmelCase_          =							load_from_checkpoint
               yield


def    A						()   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							load_metric(os.path.join('''metrics'''   ,				'''seqeval'''       )       )
     UpperCAmelCase_          =							'''ERROR'''
     UpperCAmelCase_          =							F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
     with pytest.raises(__A   ,				match=re.escape(__A       )       ):
          metric.compute(predictions=[]   ,				references=[]   ,				scheme=__A       )












# 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 __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  StableDiffusionControlNetImgaImgPipeline
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCAmelCase__  :    Union[str, Any]          =  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}		)
   UpperCAmelCase__  :    Optional[Any]          =  IMAGE_TO_IMAGE_IMAGE_PARAMS







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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_          =							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_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							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_          =							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					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						Dict=0):

        """simple docstring"""


        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_          =							torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_          =							torch.Generator(device=_snake_case).manual_seed(_snake_case)

        UpperCAmelCase_          =							2
        UpperCAmelCase_          =							randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=_snake_case					,						device=torch.device(_snake_case)					,						)
        UpperCAmelCase_          =							floats_tensor(control_image.shape					,						rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_          =							{
            '''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 lowerCamelCase						(      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 lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self._test_inference_batch_single_identical(expected_max_diff=2e-3)







class __snake_case			(			a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    str          =  StableDiffusionControlNetImgaImgPipeline
   UpperCAmelCase__  :    List[str]          =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCAmelCase__  :    str          =  frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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(_snake_case		:						Optional[int]):
             if isinstance(_snake_case					,						torch.nn.Convad):
                  torch.nn.init.normal(m.weight)
                  m.bias.data.fill_(1.0)

        UpperCAmelCase_          =							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(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							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(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							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_          =							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					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        UpperCAmelCase_          =							MultiControlNetModel([controlneta, controlneta])

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







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str=0):

        """simple docstring"""


        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_          =							torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_          =							torch.Generator(device=_snake_case).manual_seed(_snake_case)

        UpperCAmelCase_          =							2

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

        UpperCAmelCase_          =							floats_tensor(control_image[0].shape					,						rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_          =							{
            '''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 lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_dummy_components()
        UpperCAmelCase_          =							self.pipeline_class(**_snake_case)
        pipe.to(_snake_case)

        UpperCAmelCase_          =							1_0.0
        UpperCAmelCase_          =							4

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case)[0]

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

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

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case					,						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 lowerCamelCase						(      self		:						Dict):

        """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 lowerCamelCase						(      self		:						int):

        """simple docstring"""


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        self._test_inference_batch_single_identical(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_dummy_components()
        UpperCAmelCase_          =							self.pipeline_class(**_snake_case)
        pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)
        with tempfile.TemporaryDirectory() as tmpdir:
             try:
                  # save_pretrained is not implemented for Multi-ControlNet
                  pipe.save_pretrained(_snake_case)
             except NotImplementedError:
                  pass







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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny''')

        UpperCAmelCase_          =							StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''					,						safety_checker=_snake_case					,						controlnet=_snake_case)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=_snake_case)

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

        UpperCAmelCase_          =							pipe(
            _snake_case					,						_snake_case					,						control_image=_snake_case					,						generator=_snake_case					,						output_type='''np'''					,						num_inference_steps=50					,						strength=0.6					,						)

        UpperCAmelCase_          =							output.images[0]

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

        UpperCAmelCase_          =							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






