




"""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 (			__lowerCamelCase							,					unittest.TestCase					):


     """simple docstring"""


     _A    :   List[Any]     =  PaintByExamplePipeline
     _A    :   Dict     =  IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     _A    :   List[Any]     =  IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _A    :   Any     =  frozenset([]					)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




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


         '''simple docstring'''





         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)    ,layers_per_block=2    ,sample_size=3_2    ,in_channels=9    ,out_channels=4    ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,cross_attention_dim=3_2    ,)
         SCREAMING_SNAKE_CASE__       =PNDMScheduler(skip_prk_steps=_UpperCamelCase						)
         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__       =AutoencoderKL(
             block_out_channels=[3_2, 6_4]    ,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__       =CLIPVisionConfig(
             hidden_size=3_2    ,projection_dim=3_2    ,intermediate_size=3_7    ,layer_norm_eps=1e-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,image_size=3_2    ,patch_size=4    ,)
         SCREAMING_SNAKE_CASE__       =PaintByExampleImageEncoder(_UpperCamelCase    ,proj_size=3_2						)
         SCREAMING_SNAKE_CASE__       =CLIPImageProcessor(crop_size=3_2    ,size=3_2						)

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




     def   __A    (     self      :			List[Any]    ,_UpperCamelCase      :			int						)		->       Union[str, Any]:


         '''simple docstring'''





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




     def   __A    (     self      :			List[Any]    ,_UpperCamelCase      :			Any="cpu"    ,_UpperCamelCase      :			int=0						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =floats_tensor((1, 3, 3_2, 3_2)    ,rng=random.Random(_UpperCamelCase						)						).to(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =image.cpu().permute(0    ,2    ,3    ,1						)[0]
         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						).resize((6_4, 6_4)						)
         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(image + 4						)						).convert("""RGB"""						).resize((6_4, 6_4)						)
         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						).resize((3_2, 3_2)						)

         if str(_UpperCamelCase						).startswith("""mps"""						):
             SCREAMING_SNAKE_CASE__       =torch.manual_seed(_UpperCamelCase						)
         else:
             SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_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   __A    (     self      :			Union[str, Any]						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.get_dummy_components()

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

         SCREAMING_SNAKE_CASE__       =self.get_dummy_inputs()
         SCREAMING_SNAKE_CASE__       =pipe(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =output.images

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

         assert image.shape == (1, 6_4, 6_4, 3)
         SCREAMING_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   __A    (     self      :			Optional[int]						)		->       Any:


         '''simple docstring'''





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

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

         SCREAMING_SNAKE_CASE__       =pipe(image=_UpperCamelCase    ,mask_image=mask_image[:, 0]    ,**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =output.images

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

         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						)
         SCREAMING_SNAKE_CASE__       =Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						)

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

         assert out_a.shape == (1, 6_4, 6_4, 3)
         assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5e-2




     def   __A    (     self      :			List[str]						)		->       List[str]:


         '''simple docstring'''





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






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


     """simple docstring"""


     def   __A    (     self      :			List[str]						)		->       Optional[int]:


         '''simple docstring'''





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




     def   __A    (     self      :			Optional[Any]						)		->       List[Any]:


         '''simple docstring'''





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

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

         SCREAMING_SNAKE_CASE__       =torch.manual_seed(3_2_1						)
         SCREAMING_SNAKE_CASE__       =pipe(
             image=_UpperCamelCase    ,mask_image=_UpperCamelCase    ,example_image=_UpperCamelCase    ,generator=_UpperCamelCase    ,guidance_scale=5.0    ,num_inference_steps=5_0    ,output_type="""np"""    ,)

         SCREAMING_SNAKE_CASE__       =output.images

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         SCREAMING_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












import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =filter(lambda __UpperCamelCase     : p.requires_grad,    model.parameters()     )
    SCREAMING_SNAKE_CASE__       =sum([np.prod(p.size()     ) for p in model_parameters]     )
    return params


lowerCamelCase_  						=       logging.getLogger(__name__)
class 				__a (			pl.Callback					):


     """simple docstring"""


     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			Any						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ={f"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
         pl_module.logger.log_metrics(_UpperCamelCase						)




     @rank_zero_only
     def   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			pl.LightningModule    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Tuple=True						)		->       None:


         '''simple docstring'''





         logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
         SCREAMING_SNAKE_CASE__       =trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}						)
         # Log results
         SCREAMING_SNAKE_CASE__       =Path(pl_module.hparams.output_dir						)
         if type_path == "test":
             SCREAMING_SNAKE_CASE__       =od / """test_results.txt"""
             SCREAMING_SNAKE_CASE__       =od / """test_generations.txt"""
         else:
             # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
             # If people want this it will be easy enough to add back.
             SCREAMING_SNAKE_CASE__       =od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
             SCREAMING_SNAKE_CASE__       =od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
             results_file.parent.mkdir(exist_ok=_UpperCamelCase						)
             generations_file.parent.mkdir(exist_ok=_UpperCamelCase						)
         with open(_UpperCamelCase    ,"""a+"""						) as writer:
             for key in sorted(_UpperCamelCase						):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 SCREAMING_SNAKE_CASE__       =metrics[key]
                 if isinstance(_UpperCamelCase    ,torch.Tensor						):
                     SCREAMING_SNAKE_CASE__       =val.item()
                 SCREAMING_SNAKE_CASE__       =f"""{key}: {val:.6f}\n"""
                 writer.write(_UpperCamelCase						)

         if not save_generations:
             return

         if "preds" in metrics:
             SCREAMING_SNAKE_CASE__       ="""\n""".join(metrics["""preds"""]						)
             generations_file.open("""w+"""						).write(_UpperCamelCase						)




     @rank_zero_only
     def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Dict						)		->       Dict:


         '''simple docstring'''





         try:
             SCREAMING_SNAKE_CASE__       =pl_module.model.model.num_parameters()
         except AttributeError:
             SCREAMING_SNAKE_CASE__       =pl_module.model.num_parameters()

         SCREAMING_SNAKE_CASE__       =count_trainable_parameters(_UpperCamelCase						)
         # mp stands for million parameters
         trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6}						)




     @rank_zero_only
     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			pl.LightningModule						)		->       Any:


         '''simple docstring'''





         save_json(pl_module.metrics    ,pl_module.metrics_save_path						)
         return self._write_logs(_UpperCamelCase    ,_UpperCamelCase    ,"""test"""						)




     @rank_zero_only
     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			pl.Trainer    ,_UpperCamelCase      :			List[Any]						)		->       Dict:


         '''simple docstring'''





         save_json(pl_module.metrics    ,pl_module.metrics_save_path						)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")






def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=1,    __UpperCamelCase=False     ):
    if metric == "rouge2":
        SCREAMING_SNAKE_CASE__       ="""{val_avg_rouge2:.4f}-{step_count}"""
    elif metric == "bleu":
        SCREAMING_SNAKE_CASE__       ="""{val_avg_bleu:.4f}-{step_count}"""
    elif metric == "loss":
        SCREAMING_SNAKE_CASE__       ="""{val_avg_loss:.4f}-{step_count}"""
    else:
        raise NotImplementedError(
            f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
            """ this function."""     )

    SCREAMING_SNAKE_CASE__       =ModelCheckpoint(
        dirpath=__UpperCamelCase,    filename=__UpperCamelCase,    monitor=f"""val_{metric}""",    mode="""min""" if """loss""" in metric else """max""",    save_top_k=__UpperCamelCase,    )
    return checkpoint_callback




def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    return EarlyStopping(
        monitor=f"""val_{metric}""",    mode="""min""" if """loss""" in metric else """max""",    patience=__UpperCamelCase,    verbose=__UpperCamelCase,    )






