"""simple docstring"""


import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


UpperCAmelCase__  		=						re.compile(r"""\s+""")






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return {"hash": hashlib.mda(re.sub(lowercase   ,""""""   ,example["""content"""]		).encode("""utf-8"""		)		).hexdigest()}






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      [len(lowercase		) for line in example["""content"""].splitlines()]
  return {"line_mean": np.mean(lowercase		), "line_max": max(lowercase		)}






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      np.mean([c.isalnum() for c in example["""content"""]]		)
  return {"alpha_frac": alpha_frac}






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  if example["hash"] in uniques:
    uniques.remove(example["""hash"""]		)
    return True
  else:
    return False






def     __UpperCAmelCase					(			lowercase   ,lowercase=5		):







  """simple docstring"""
  _UpperCAmelCase						 =      ["""auto-generated""", """autogenerated""", """automatically generated"""]
  _UpperCAmelCase						 =      example["""content"""].splitlines()
  for _, line in zip(range(lowercase		)   ,lowercase		):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}






def     __UpperCAmelCase					(			lowercase   ,lowercase=5   ,lowercase=0.05		):







  """simple docstring"""
  _UpperCAmelCase						 =      ["""unit tests""", """test file""", """configuration file"""]
  _UpperCAmelCase						 =      example["""content"""].splitlines()
  _UpperCAmelCase						 =      0
  _UpperCAmelCase						 =      0
  # first test
  for _, line in zip(range(lowercase		)   ,lowercase		):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  _UpperCAmelCase						 =      example["""content"""].count("""\n"""		)
  _UpperCAmelCase						 =      int(coeff * nlines		)
  for line in lines:
    count_config += line.lower().count("""config"""		)
    count_test += line.lower().count("""test"""		)
    if count_config > threshold or count_test > threshold:
      return {"config_or_test": True}
  return {"config_or_test": False}






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      ["""def """, """class """, """for """, """while """]
  _UpperCAmelCase						 =      example["""content"""].splitlines()
  for line in lines:
    for keyword in keywords:
      if keyword in line.lower():
        return {"has_no_keywords": False}
  return {"has_no_keywords": True}






def     __UpperCAmelCase					(			lowercase   ,lowercase=4		):







  """simple docstring"""
  _UpperCAmelCase						 =      example["""content"""].splitlines()
  _UpperCAmelCase						 =      0
  for line in lines:
    counter += line.lower().count("""="""		)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      tokenizer(example["""content"""]   ,truncation=lowercase		)["""input_ids"""]
  _UpperCAmelCase						 =      len(example["""content"""]		) / len(lowercase		)
  return {"ratio": ratio}






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      {}
  results.update(get_hash(lowercase		)		)
  results.update(line_stats(lowercase		)		)
  results.update(alpha_stats(lowercase		)		)
  results.update(char_token_ratio(lowercase		)		)
  results.update(is_autogenerated(lowercase		)		)
  results.update(is_config_or_test(lowercase		)		)
  results.update(has_no_keywords(lowercase		)		)
  results.update(has_few_assignments(lowercase		)		)
  return results






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  if not check_uniques(lowercase   ,lowercase		):
    return False
  elif example["autogenerated"]:
    return False
  elif example["line_max"] > args.line_max:
    return False
  elif example["line_mean"] > args.line_mean:
    return False
  elif example["alpha_frac"] < args.alpha_frac:
    return False
  elif example["ratio"] < args.min_token_ratio:
    return False
  elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_few_assignments"]:
    return False
  else:
    return True






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  with open(lowercase   ,"""rb"""		) as f_in:
    with gzip.open(str(lowercase		) + """.gz"""   ,"""wb"""   ,compresslevel=6		) as f_out:
      shutil.copyfileobj(lowercase   ,lowercase		)
  os.unlink(lowercase		)


# Settings
UpperCAmelCase__  		=						HfArgumentParser(PreprocessingArguments)
UpperCAmelCase__  		=						parser.parse_args()
if args.num_workers is None:
    UpperCAmelCase__  		=						multiprocessing.cpu_count()
UpperCAmelCase__  		=						AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
UpperCAmelCase__  		=						time.time()
UpperCAmelCase__  		=						load_dataset(args.dataset_name, split="""train""")
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
UpperCAmelCase__  		=						time.time()
UpperCAmelCase__  		=						ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
UpperCAmelCase__  		=						set(ds.unique("""hash"""))
UpperCAmelCase__  		=						len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
UpperCAmelCase__  		=						time.time()
UpperCAmelCase__  		=						ds.filter(filter, fn_kwargs={"""uniques""": uniques, """args""": args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
    UpperCAmelCase__  		=						time.time()
    UpperCAmelCase__		,   UpperCAmelCase__  		=						deduplicate_dataset(ds_filter, args.jaccard_threshold)
    print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
    print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
UpperCAmelCase__  		=						Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
    with open(output_dir / """duplicate_clusters.json""", """w""") as f:
        json.dump(duplicate_clusters, f)

UpperCAmelCase__  		=						output_dir / """data"""
data_dir.mkdir(exist_ok=True)

UpperCAmelCase__  		=						time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
    UpperCAmelCase__  		=						str(data_dir / F'''file-{file_number+1:012}.json''')
    UpperCAmelCase__  		=						min(len(ds_filter), index + args.samples_per_file)
    ds_filter.select(list(range(index, end_index))).to_json(file_path)
    compress_file(file_path)

print(F'''Time to save dataset: {time.time()-t_start:.2f}''')

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


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      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=0.00_085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				clip_sample=__lowerCAmelCase   ,				set_alpha_to_one=__lowerCAmelCase   ,				)
      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-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)
      _UpperCAmelCase						 =      CLIPTextModel(__lowerCAmelCase					)
      _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	:  int   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[str]=0					):
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)

      _UpperCAmelCase						 =      2
      _UpperCAmelCase						 =      randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,				generator=__lowerCAmelCase   ,				device=torch.device(__lowerCAmelCase					)   ,				)
      _UpperCAmelCase						 =      floats_tensor(control_image.shape   ,				rng=random.Random(__lowerCAmelCase					)					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      image.cpu().permute(0   ,				2   ,				3   ,				1					)[0]
      _UpperCAmelCase						 =      Image.fromarray(np.uinta(__lowerCAmelCase					)					).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	:  Dict					):
      return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3					)


    @unittest.skipIf(
        torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
    def      lowerCAmelCase_  (					self	:  Tuple					):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)


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



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


    def      lowerCAmelCase_  (					self	:  Any					):
      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(__lowerCAmelCase	:  str					):
        if isinstance(__lowerCAmelCase   ,				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(__lowerCAmelCase					)

      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(__lowerCAmelCase					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDIMScheduler(
          beta_start=0.00_085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				clip_sample=__lowerCAmelCase   ,				set_alpha_to_one=__lowerCAmelCase   ,				)
      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-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)
      _UpperCAmelCase						 =      CLIPTextModel(__lowerCAmelCase					)
      _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	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  List[str]=0					):
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)

      _UpperCAmelCase						 =      2

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

      _UpperCAmelCase						 =      floats_tensor(control_image[0].shape   ,				rng=random.Random(__lowerCAmelCase					)					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      image.cpu().permute(0   ,				2   ,				3   ,				1					)[0]
      _UpperCAmelCase						 =      Image.fromarray(np.uinta(__lowerCAmelCase					)					).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]					):
      _UpperCAmelCase						 =      self.get_dummy_components()
      _UpperCAmelCase						 =      self.pipeline_class(**__lowerCAmelCase					)
      pipe.to(__lowerCAmelCase					)

      _UpperCAmelCase						 =      10.0
      _UpperCAmelCase						 =      4

      _UpperCAmelCase						 =      self.get_dummy_inputs(__lowerCAmelCase					)
      _UpperCAmelCase						 =      steps
      _UpperCAmelCase						 =      scale
      _UpperCAmelCase						 =      pipe(**__lowerCAmelCase					)[0]

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

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

      _UpperCAmelCase						 =      self.get_dummy_inputs(__lowerCAmelCase					)
      _UpperCAmelCase						 =      steps
      _UpperCAmelCase						 =      scale
      _UpperCAmelCase						 =      pipe(**__lowerCAmelCase   ,				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	:  Union[str, 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      lowerCAmelCase_  (					self	:  Dict					):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      self._test_inference_batch_single_identical(expected_max_diff=2e-3					)


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      self.get_dummy_components()
      _UpperCAmelCase						 =      self.pipeline_class(**__lowerCAmelCase					)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      with tempfile.TemporaryDirectory() as tmpdir:
        try:
          # save_pretrained is not implemented for Multi-ControlNet
          pipe.save_pretrained(__lowerCAmelCase					)
        except NotImplementedError:
          pass



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


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""					)

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

      _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(
          __lowerCAmelCase   ,				__lowerCAmelCase   ,				control_image=__lowerCAmelCase   ,				generator=__lowerCAmelCase   ,				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
