





"""simple docstring"""


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=False		,   _UpperCAmelCase=False		,   _UpperCAmelCase="data2vec."		)			->     Dict:

 '''simple docstring'''






 snake_case__										= "backbone." if is_semantic else ""

 snake_case__										= []
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f"{prefix}blocks.{i}.norm1.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")		)
  rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")		)
  rename_keys.append(
      (f"{prefix}blocks.{i}.attn.proj.weight", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")		)
  rename_keys.append(
      (f"{prefix}blocks.{i}.attn.proj.bias", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")		)
  rename_keys.append(
      (f"{prefix}blocks.{i}.norm2.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")		)
  rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")		)
  rename_keys.append(
      (f"{prefix}blocks.{i}.mlp.fc1.weight", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")		)
  rename_keys.append(
      (f"{prefix}blocks.{i}.mlp.fc1.bias", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")		)
  rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"{hf_prefix}encoder.layer.{i}.output.dense.weight")		)
  rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"{hf_prefix}encoder.layer.{i}.output.dense.bias")		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (f"{prefix}cls_token", f"{hf_prefix}embeddings.cls_token"),
         (f"{prefix}patch_embed.proj.weight", f"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
         (f"{prefix}patch_embed.proj.bias", f"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ("mask_token", f"{hf_prefix}embeddings.mask_token"),
          (
              "rel_pos_bias.relative_position_bias_table",
              f"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
          ),
          (
              "rel_pos_bias.relative_position_index",
              f"{hf_prefix}encoder.relative_position_bias.relative_position_index",
          ),
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
          ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
          ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
          ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("fc_norm.weight", f"{hf_prefix}pooler.layernorm.weight"),
          ("fc_norm.bias", f"{hf_prefix}pooler.layernorm.bias"),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]		)

 return rename_keys





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase=False		,   _UpperCAmelCase=False		,   _UpperCAmelCase="data2vec_vision."		)			->     Tuple:

 '''simple docstring'''






 for i in range(config.num_hidden_layers		):
  snake_case__										= "backbone." if is_semantic else ""
  # queries, keys and values
  snake_case__										= state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"		)
  snake_case__										= state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"		)
  snake_case__										= state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"		)

  snake_case__										= in_proj_weight[
      : config.hidden_size, :
  ]
  snake_case__										= q_bias
  snake_case__										= in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  snake_case__										= in_proj_weight[
      -config.hidden_size :, :
  ]
  snake_case__										= v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  snake_case__										= state_dict.pop(f"{prefix}blocks.{i}.gamma_1"		)
  snake_case__										= state_dict.pop(f"{prefix}blocks.{i}.gamma_2"		)

  snake_case__										= gamma_a
  snake_case__										= gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   snake_case__										= state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"		)
   snake_case__										= state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"		)

   snake_case__										= table
   snake_case__										= index





def 							a__	(     )			->     Tuple:

 '''simple docstring'''






 snake_case__										= argparse.ArgumentParser(
     "Convert Data2VecVision to HF for image classification and pretraining"		,   add_help=_UpperCAmelCase		)
 parser.add_argument("--hf_checkpoint_name"		,   type=_UpperCAmelCase		)
 parser.add_argument("--input_size"		,   default=2_2_4		,   type=_UpperCAmelCase		,   help="images input size"		)
 parser.add_argument("--beit_checkpoint"		,   default=""		,   help="beit checkpoint"		)

 return parser.parse_args()





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[Any]:

 '''simple docstring'''






 def load_state_dict(_UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase=""		,   _UpperCAmelCase="relative_position_index"		):
  snake_case__										= []
  snake_case__										= []
  snake_case__										= []
  # copy state_dict so _load_from_state_dict can modify it
  snake_case__										= getattr(_UpperCAmelCase		,   "_metadata"		,   _UpperCAmelCase		)
  snake_case__										= state_dict.copy()
  if metadata is not None:
   snake_case__										= metadata

  def load(_UpperCAmelCase		,   _UpperCAmelCase=""		):
   snake_case__										= {} if metadata is None else metadata.get(prefix[:-1]		,   {}		)
   module._load_from_state_dict(
       _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)
   for name, child in module._modules.items():
    if child is not None:
     load(_UpperCAmelCase		,   prefix + name + "."		)

  load(_UpperCAmelCase		,   prefix=_UpperCAmelCase		)

  snake_case__										= []
  snake_case__										= []
  for key in missing_keys:
   snake_case__										= True
   for ignore_key in ignore_missing.split("|"		):
    if ignore_key in key:
     snake_case__										= False
     break
   if keep_flag:
    warn_missing_keys.append(_UpperCAmelCase		)
   else:
    ignore_missing_keys.append(_UpperCAmelCase		)

  snake_case__										= warn_missing_keys

  if len(_UpperCAmelCase		) > 0:
   print(
       "Weights of {} not initialized from pretrained model: {}".format(
           model.__class__.__name__		,   _UpperCAmelCase		)		)
  if len(_UpperCAmelCase		) > 0:
   print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__		,   _UpperCAmelCase		)		)
  if len(_UpperCAmelCase		) > 0:
   print(
       "Ignored weights of {} not initialized from pretrained model: {}".format(
           model.__class__.__name__		,   _UpperCAmelCase		)		)
  if len(_UpperCAmelCase		) > 0:
   print("\n".join(_UpperCAmelCase		)		)

 snake_case__										= {
     "pretrained": False,
     "use_shared_rel_pos_bias": True,
     "use_abs_pos_emb": False,
     "init_values": 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          "num_classes": 1_0_0_0,
          "use_mean_pooling": True,
          "init_scale": 0.001,
          "use_rel_pos_bias": True,
      }		)

 snake_case__										= create_model(
     "beit_large_patch16_224" if is_large else "beit_base_patch16_224"		,   **_UpperCAmelCase		,   )
 snake_case__										= model.patch_embed.patch_size
 snake_case__										= (args.input_size // patch_size[0], args.input_size // patch_size[1])
 snake_case__										= torch.load(args.beit_checkpoint		,   map_location="cpu"		)

 print(f"Load ckpt from {args.beit_checkpoint}"		)
 snake_case__										= None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   snake_case__										= checkpoint[model_key]
   print(f"Load state_dict by model_key = {model_key}"		)
   break

 snake_case__										= list(checkpoint_model.keys()		)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(_UpperCAmelCase		)

  if "relative_position_bias_table" in key:
   snake_case__										= checkpoint_model[key]
   snake_case__							,      snake_case__										= rel_pos_bias.size()
   snake_case__							,      snake_case__										= model.state_dict()[key].size()
   snake_case__										= model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(_UpperCAmelCase		,   _UpperCAmelCase		,   prefix=""		)

 return model





def 							a__	(     )			->     str:

 '''simple docstring'''






 snake_case__										= get_args()

 snake_case__										= "ft1k" in args.hf_checkpoint_name
 snake_case__										= "large" in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 snake_case__										= DataaVecVisionConfig()
 if is_finetuned:
  snake_case__										= True
  snake_case__										= False
  snake_case__										= True
  snake_case__										= 1_0_0_0

  snake_case__										= "huggingface/label-files"
  snake_case__										= "imagenet-1k-id2label.json"
  snake_case__										= json.load(open(hf_hub_download(_UpperCAmelCase		,   _UpperCAmelCase		,   repo_type="dataset"		)		,   "r"		)		)
  snake_case__										= {int(_UpperCAmelCase		): v for k, v in idalabel.items()}
  snake_case__										= idalabel
  snake_case__										= {v: k for k, v in idalabel.items()}
 else:
  snake_case__										= False
  snake_case__										= True
  snake_case__										= False

 if is_large:
  snake_case__										= 1_0_2_4
  snake_case__										= 4_0_9_6
  snake_case__										= 2_4
  snake_case__										= 1_6

 # 2. Load Beit model
 snake_case__										= load_beit_model(_UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)
 orig_model.eval()

 # 3. Forward Beit model
 snake_case__										= BeitImageProcessor(size=config.image_size		,   do_center_crop=_UpperCAmelCase		)
 snake_case__										= Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"		)
 snake_case__										= image_processor(images=_UpperCAmelCase		,   return_tensors="pt"		)
 snake_case__										= encoding["pixel_values"]

 snake_case__										= (pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  snake_case__										= orig_model(*_UpperCAmelCase		)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  snake_case__										= DataaVecVisionForImageClassification(_UpperCAmelCase		)
  hf_model.eval()
  snake_case__										= False
  snake_case__										= "data2vec_vision."
 else:
  snake_case__										= DataaVecVisionModel(_UpperCAmelCase		)
  hf_model.eval()
  snake_case__										= True
  snake_case__										= ""

 snake_case__										= create_rename_keys(_UpperCAmelCase		,   hf_prefix=_UpperCAmelCase		,   has_lm_head=_UpperCAmelCase		)
 snake_case__										= orig_model.state_dict()
 for src, dest in rename_keys:
  snake_case__										= state_dict.pop(_UpperCAmelCase		)
  snake_case__										= val

 read_in_q_k_v(_UpperCAmelCase		,   _UpperCAmelCase		,   hf_prefix=_UpperCAmelCase		,   has_lm_head=_UpperCAmelCase		)
 snake_case__							,      snake_case__										= hf_model.load_state_dict(_UpperCAmelCase		,   strict=_UpperCAmelCase		)
 print("HF missing"		,   _UpperCAmelCase		)
 print("HF unexpected_keys"		,   _UpperCAmelCase		)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  snake_case__										= hf_model(_UpperCAmelCase		)

 snake_case__										= hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 snake_case__										= torch.max(torch.abs(hf_output - orig_model_output		)		).item()

 print(f"max_absolute_diff = {max_absolute_diff}"		)
 snake_case__										= torch.allclose(_UpperCAmelCase		,   _UpperCAmelCase		,   atol=1e-3		)
 print("Do both models output the same tensors?"		,   "🔥" if success else "💩"		)
 if not success:
  raise Exception("Something went wRoNg"		)

 # 7. Save
 print(f"Saving to {args.hf_checkpoint_name}"		)
 hf_model.save_pretrained(args.hf_checkpoint_name		)
 image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
   main()
   # Run the following to convert checkpoints
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./pretrained_base.pt \
   #          --hf_checkpoint_name "./data2vec-vision-base"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./finetuned_base.pt \
   #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./pretrained_large.pt \
   #          --hf_checkpoint_name "./data2vec-vision-large"
   #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
   #          --beit_checkpoint ./finetuned_large.pt \
   #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"








"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class    lowercase_ (   __lowerCAmelCase ,unittest.TestCase							):
       snake_case__ :							List[str] 							=   PaintByExamplePipeline
       snake_case__ :							Any 							=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__ :							Optional[int] 							=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__ :							int 							=   frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        torch.manual_seed(0   )
        snake_case__										= UNetaDConditionModel(
            block_out_channels=(32, 64)      ,    layers_per_block=2      ,    sample_size=32      ,    in_channels=9      ,    out_channels=4      ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,    cross_attention_dim=32      ,    )
        snake_case__										= PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
        torch.manual_seed(0   )
        snake_case__										= 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   )
        snake_case__										= CLIPVisionConfig(
            hidden_size=32      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    image_size=32      ,    patch_size=4      ,    )
        snake_case__										= PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE      ,    proj_size=32   )
        snake_case__										= CLIPImageProcessor(crop_size=32      ,    size=32   )

        snake_case__										= {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "image_encoder": image_encoder,
            "safety_checker": None,
            "feature_extractor": feature_extractor,
        }
        return components

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= np.array(image.convert("RGB"   )   )
        snake_case__										= image[None].transpose(0      ,    3      ,    1      ,    2   )
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(dtype=torch.floataa   ) / 127.5 - 1.0
        return image

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE="cpu"      ,    __SCREAMING_SNAKE_CASE=0   ):






        '''simple docstring'''

        snake_case__										= floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   ).resize((32, 32)   )

        if str(__SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
         snake_case__										= torch.manual_seed(__SCREAMING_SNAKE_CASE   )
        else:
         snake_case__										= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
        snake_case__										= {
            "example_image": example_image,
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= pipe.to("cpu"   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= self.get_dummy_inputs()
        snake_case__										= pipe(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        snake_case__										= np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= "cpu"
        snake_case__										= self.get_dummy_inputs()
        inputs.pop("mask_image"   )
        snake_case__										= self.convert_to_pt(inputs.pop("image"   )   )
        snake_case__										= image.clamp(0      ,    1   ) / 2

        # make sure here that pndm scheduler skips prk
        snake_case__										= PaintByExamplePipeline(**self.get_dummy_components()   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= pipe(image=__SCREAMING_SNAKE_CASE      ,    mask_image=mask_image[:, 0]      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

        snake_case__										= image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]
        snake_case__										= mask_image.cpu().permute(0      ,    2      ,    3      ,    1   )[0]

        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )

        snake_case__										= pipe(**self.get_dummy_inputs()   )
        snake_case__										= output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()   ).max() < 5e-2

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/dog_in_bucket.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/mask.png"   )
        snake_case__										= load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/panda.jpg"   )

        snake_case__										= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"   )
        snake_case__										= pipe.to(__SCREAMING_SNAKE_CASE   )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

        snake_case__										= torch.manual_seed(321   )
        snake_case__										= pipe(
            image=__SCREAMING_SNAKE_CASE      ,    mask_image=__SCREAMING_SNAKE_CASE      ,    example_image=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    guidance_scale=5.0      ,    num_inference_steps=50      ,    output_type="np"      ,    )

        snake_case__										= output.images

        snake_case__										= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        snake_case__										= np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

