



"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase=     []
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
   )
   rename_keys.append(
       (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
   )
   rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
   rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
   rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
   rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
   rename_keys.append(
       (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
   )
   rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
   rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
   rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
   )
   rename_keys.append(
       (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
   )
   rename_keys.append(
       (
           f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
           f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
       )
   )
   rename_keys.append(
       (
           f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
           f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
       )
   )
   rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
   rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
   rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
   rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
   rename_keys.append(
       (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
   )
   rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
   rename_keys.append(
       (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
   )
   rename_keys.append(
       (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
   )
   rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
   rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	List[str]            =state_dict.pop(lowercase	)
   SCREAMING_SNAKE_CASE_:	Dict            =val





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Any            =OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         SCREAMING_SNAKE_CASE_:	int            =key.replace("""backbone.0.body"""							,     """backbone.conv_encoder.model"""	)
         SCREAMING_SNAKE_CASE_:	List[str]            =value
      else:
         SCREAMING_SNAKE_CASE_:	Dict            =value

   return new_state_dict





def        __magic_name__			(							lowercase							,     lowercase=False	):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =""""""
   if is_panoptic:
      SCREAMING_SNAKE_CASE_:	str            ="""detr."""

   # first: transformer encoder
   for i in range(6	):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      SCREAMING_SNAKE_CASE_:	int            =state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
      SCREAMING_SNAKE_CASE_:	Any            =state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''	)
      # next, add query, keys and values (in that order) to the state dict
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =in_proj_weight[:256, :]
      SCREAMING_SNAKE_CASE_:	Dict            =in_proj_bias[:256]
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =in_proj_weight[256:512, :]
      SCREAMING_SNAKE_CASE_:	str            =in_proj_bias[256:512]
      SCREAMING_SNAKE_CASE_:	Tuple            =in_proj_weight[-256:, :]
      SCREAMING_SNAKE_CASE_:	Tuple            =in_proj_bias[-256:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6	):
      # read in weights + bias of input projection layer of self-attention
      SCREAMING_SNAKE_CASE_:	List[Any]            =state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
      SCREAMING_SNAKE_CASE_:	Tuple            =state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''	)
      # next, add query, keys and values (in that order) to the state dict
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =in_proj_weight[:256, :]
      SCREAMING_SNAKE_CASE_:	Any            =in_proj_bias[:256]
      SCREAMING_SNAKE_CASE_:	Any            =in_proj_weight[256:512, :]
      SCREAMING_SNAKE_CASE_:	Tuple            =in_proj_bias[256:512]
      SCREAMING_SNAKE_CASE_:	Dict            =in_proj_weight[-256:, :]
      SCREAMING_SNAKE_CASE_:	int            =in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =state_dict.pop(
          f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
      SCREAMING_SNAKE_CASE_:	Optional[int]            =state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''	)
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      SCREAMING_SNAKE_CASE_:	Dict            =in_proj_weight_cross_attn[:256, :]
      SCREAMING_SNAKE_CASE_:	Tuple            =in_proj_bias_cross_attn[:256]
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =in_proj_weight_cross_attn[256:512, :]
      SCREAMING_SNAKE_CASE_:	Optional[int]            =in_proj_bias_cross_attn[256:512]
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =in_proj_weight_cross_attn[-256:, :]
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =in_proj_bias_cross_attn[-256:]





def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	str            ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
   SCREAMING_SNAKE_CASE_:	Any            =Image.open(requests.get(lowercase							,     stream=lowercase	).raw	)

   return im





@torch.no_grad()
def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	int            =DetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      SCREAMING_SNAKE_CASE_:	Any            ="""resnet101"""
   if "dc5" in model_name:
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =True
   SCREAMING_SNAKE_CASE_:	Any            ="""panoptic""" in model_name
   if is_panoptic:
      SCREAMING_SNAKE_CASE_:	Dict            =250
   else:
      SCREAMING_SNAKE_CASE_:	Any            =91
      SCREAMING_SNAKE_CASE_:	Dict            ="""huggingface/label-files"""
      SCREAMING_SNAKE_CASE_:	str            ="""coco-detection-id2label.json"""
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =json.load(open(hf_hub_download(lowercase							,     lowercase							,     repo_type="""dataset"""	)							,     """r"""	)	)
      SCREAMING_SNAKE_CASE_:	Optional[int]            ={int(lowercase	): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_:	Dict            =idalabel
      SCREAMING_SNAKE_CASE_:	Any            ={v: k for k, v in idalabel.items()}

   # load image processor
   SCREAMING_SNAKE_CASE_:	Optional[int]            ="""coco_panoptic""" if is_panoptic else """coco_detection"""
   SCREAMING_SNAKE_CASE_:	Dict            =DetrImageProcessor(format=lowercase	)

   # prepare image
   SCREAMING_SNAKE_CASE_:	Any            =prepare_img()
   SCREAMING_SNAKE_CASE_:	int            =image_processor(images=lowercase							,     return_tensors="""pt"""	)
   SCREAMING_SNAKE_CASE_:	List[Any]            =encoding["""pixel_values"""]

   logger.info(f'''Converting model {model_name}...'''	)

   # load original model from torch hub
   SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.hub.load("""facebookresearch/detr"""							,     lowercase							,     pretrained=lowercase	).eval()
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         SCREAMING_SNAKE_CASE_:	List[str]            ="""detr.""" + src
      rename_key(lowercase							,     lowercase							,     lowercase	)
   SCREAMING_SNAKE_CASE_:	List[str]            =rename_backbone_keys(lowercase	)
   # query, key and value matrices need special treatment
   read_in_q_k_v(lowercase							,     is_panoptic=lowercase	)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   SCREAMING_SNAKE_CASE_:	Optional[int]            ="""detr.model.""" if is_panoptic else """model."""
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("""detr"""	)
             and not key.startswith("""class_labels_classifier"""	)
             and not key.startswith("""bbox_predictor"""	)
         ):
            SCREAMING_SNAKE_CASE_:	str            =state_dict.pop(lowercase	)
            SCREAMING_SNAKE_CASE_:	Union[str, Any]            =val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            SCREAMING_SNAKE_CASE_:	List[str]            =state_dict.pop(lowercase	)
            SCREAMING_SNAKE_CASE_:	List[Any]            =val
         elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
            continue
         else:
            SCREAMING_SNAKE_CASE_:	Union[str, Any]            =state_dict.pop(lowercase	)
            SCREAMING_SNAKE_CASE_:	Tuple            =val
      else:
         if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
            SCREAMING_SNAKE_CASE_:	Optional[int]            =state_dict.pop(lowercase	)
            SCREAMING_SNAKE_CASE_:	Tuple            =val
    # finally, create HuggingFace model and load state dict
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =DetrForSegmentation(lowercase	) if is_panoptic else DetrForObjectDetection(lowercase	)
   model.load_state_dict(lowercase	)
   model.eval()
   # verify our conversion
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =detr(lowercase	)
   SCREAMING_SNAKE_CASE_:	Tuple            =model(lowercase	)
   assert torch.allclose(outputs.logits							,     original_outputs["""pred_logits"""]							,     atol=1e-4	)
   assert torch.allclose(outputs.pred_boxes							,     original_outputs["""pred_boxes"""]							,     atol=1e-4	)
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks							,     original_outputs["""pred_masks"""]							,     atol=1e-4	)

   # Save model and image processor
   logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''	)
   Path(lowercase	).mkdir(exist_ok=lowercase	)
   model.save_pretrained(lowercase	)
   image_processor.save_pretrained(lowercase	)


if __name__ == "__main__":
   _UpperCAmelCase=     argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR model you'd like to convert."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
   )
   _UpperCAmelCase=     parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""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    a		(					UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			Dict         =   PaintByExamplePipeline
  UpperCamelCase		:			str         =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  UpperCamelCase		:			Tuple         =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase		:			List[str]         =   frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	Any            =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    ,						)
     SCREAMING_SNAKE_CASE_:	int            =PNDMScheduler(skip_prk_steps=lowerCAmelCase						)
     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	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						)
     SCREAMING_SNAKE_CASE_:	Tuple            =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    ,						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =PaintByExampleImageEncoder(lowerCAmelCase    ,						proj_size=32						)
     SCREAMING_SNAKE_CASE_:	Tuple            =CLIPImageProcessor(crop_size=32    ,						size=32						)

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



  def 		lowerCamelCase__    (  self	:   str    ,						lowerCAmelCase	:   Dict						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =np.array(image.convert("""RGB"""						)						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =image[None].transpose(0    ,						3    ,						1    ,						2						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.from_numpy(lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
     return image



  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   int="cpu"    ,						lowerCAmelCase	:   Dict=0						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Tuple            =image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
     SCREAMING_SNAKE_CASE_:	Any            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						).resize((64, 64)						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(image + 4						)						).convert("""RGB"""						).resize((64, 64)						)
     SCREAMING_SNAKE_CASE_:	List[str]            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						).resize((32, 32)						)

     if str(lowerCAmelCase						).startswith("""mps"""						):
        SCREAMING_SNAKE_CASE_:	Dict            =torch.manual_seed(lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	str            ={
         """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 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.get_dummy_components()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =PaintByExamplePipeline(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Any            =pipe.to("""cpu"""						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.get_dummy_inputs()
     SCREAMING_SNAKE_CASE_:	int            =pipe(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	List[str]            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)

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



  def 		lowerCamelCase__    (  self	:   Any						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            ="""cpu"""
     SCREAMING_SNAKE_CASE_:	Any            =self.get_dummy_inputs()
     inputs.pop("""mask_image"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.convert_to_pt(inputs.pop("""image"""						)						)
     SCREAMING_SNAKE_CASE_:	int            =image.clamp(0    ,						1						) / 2

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =PaintByExamplePipeline(**self.get_dummy_components()						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(image=lowerCAmelCase    ,						mask_image=mask_image[:, 0]    ,						**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	str            =image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
     SCREAMING_SNAKE_CASE_:	List[Any]            =mask_image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]

     SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(**self.get_dummy_inputs()						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =output.images

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





  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Dict:


     '''simple docstring'''




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







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



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Any:


     '''simple docstring'''




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





  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/dog_in_bucket.png"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/mask.png"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/panda.jpg"""						)

     SCREAMING_SNAKE_CASE_:	Tuple            =PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	str            =torch.manual_seed(321						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =pipe(
         image=lowerCAmelCase    ,						mask_image=lowerCAmelCase    ,						example_image=lowerCAmelCase    ,						generator=lowerCAmelCase    ,						guidance_scale=5.0    ,						num_inference_steps=50    ,						output_type="""np"""    ,						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =output.images

     SCREAMING_SNAKE_CASE_:	Any            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE_:	Any            =np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)

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



