

'''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   UpperCamelCase__   (       lowercase__							,							unittest.TestCase       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Tuple       =							PaintByExamplePipeline
		SCREAMING_SNAKE_CASE__  :     Optional[Any]       =							IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		SCREAMING_SNAKE_CASE__  :     Optional[Any]       =							IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		SCREAMING_SNAKE_CASE__  :     int       =							frozenset([]       )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







		def 				A_	(			self      ):


							'''simple docstring'''

							torch.manual_seed(0      )
							UpperCAmelCase		:							Any        =       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   ,      )
							UpperCAmelCase		:							Union[str, Any]        =       PNDMScheduler(skip_prk_steps=snake_case      )
							torch.manual_seed(0      )
							UpperCAmelCase		:							Any        =       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      )
							UpperCAmelCase		:							Dict        =       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   ,      )
							UpperCAmelCase		:							Optional[Any]        =       PaintByExampleImageEncoder(snake_case   ,      proj_size=3_2      )
							UpperCAmelCase		:							List[Any]        =       CLIPImageProcessor(crop_size=3_2   ,      size=3_2      )

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







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

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







		def 				A_	(			self   ,      snake_case="cpu"   ,      snake_case=0      ):


							'''simple docstring'''

							UpperCAmelCase		:							str        =       floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(snake_case      )      ).to(snake_case      )
							UpperCAmelCase		:							str        =       image.cpu().permute(0   ,      2   ,      3   ,      1      )[0]
							UpperCAmelCase		:							int        =       Image.fromarray(np.uinta(snake_case      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
							UpperCAmelCase		:							Union[str, Any]        =       Image.fromarray(np.uinta(image + 4      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
							UpperCAmelCase		:							int        =       Image.fromarray(np.uinta(snake_case      )      ).convert("RGB"      ).resize((3_2, 3_2)      )

							if str(snake_case      ).startswith("mps"      ):
												UpperCAmelCase		:							Tuple        =       torch.manual_seed(snake_case      )
							else:
												UpperCAmelCase		:							Optional[Any]        =       torch.Generator(device=snake_case      ).manual_seed(snake_case      )
							UpperCAmelCase		:							Dict        =       {
							    "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      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							UpperCAmelCase		:							int        =       PaintByExamplePipeline(**snake_case      )
							UpperCAmelCase		:							int        =       pipe.to("cpu"      )
							pipe.set_progress_bar_config(disable=snake_case      )

							UpperCAmelCase		:							Union[str, Any]        =       self.get_dummy_inputs()
							UpperCAmelCase		:							Optional[int]        =       pipe(**snake_case      )
							UpperCAmelCase		:							Tuple        =       output.images

							UpperCAmelCase		:							Tuple        =       image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							UpperCAmelCase		:							List[Any]        =       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      ):


							'''simple docstring'''

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

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

							UpperCAmelCase		:							Union[str, Any]        =       pipe(image=snake_case   ,      mask_image=mask_image[:, 0]   ,      **snake_case      )
							UpperCAmelCase		:							Any        =       output.images

							UpperCAmelCase		:							Optional[int]        =       image.cpu().permute(0   ,      2   ,      3   ,      1      )[0]
							UpperCAmelCase		:							List[str]        =       mask_image.cpu().permute(0   ,      2   ,      3   ,      1      )[0]

							UpperCAmelCase		:							List[Any]        =       Image.fromarray(np.uinta(snake_case      )      ).convert("RGB"      )
							UpperCAmelCase		:							str        =       Image.fromarray(np.uinta(snake_case      )      ).convert("RGB"      )

							UpperCAmelCase		:							int        =       pipe(**self.get_dummy_inputs()      )
							UpperCAmelCase		:							Optional[int]        =       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      ):


							'''simple docstring'''

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







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


		"""simple docstring"""

		def 				A_	(			self      ):


							'''simple docstring'''

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







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"      )
							UpperCAmelCase		:							Union[str, Any]        =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"      )
							UpperCAmelCase		:							Any        =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"      )

							UpperCAmelCase		:							Any        =       PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"      )
							UpperCAmelCase		:							Optional[Any]        =       pipe.to(snake_case      )
							pipe.set_progress_bar_config(disable=snake_case      )

							UpperCAmelCase		:							Dict        =       torch.manual_seed(3_2_1      )
							UpperCAmelCase		:							str        =       pipe(
							    image=snake_case   ,      mask_image=snake_case   ,      example_image=snake_case   ,      generator=snake_case   ,      guidance_scale=5.0   ,      num_inference_steps=5_0   ,      output_type="np"   ,      )

							UpperCAmelCase		:							int        =       output.images

							UpperCAmelCase		:							Optional[int]        =       image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							UpperCAmelCase		:							str        =       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



'''simple docstring'''

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 				lowercase (					__magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Any        =       filter(lambda __magic_name__							: p.requires_grad			,   model.parameters()							)
					UpperCAmelCase		:							Union[str, Any]        =       sum([np.prod(p.size()							) for p in model_parameters]							)
					return params


a       :      int  	=			logging.getLogger(__name__)







class   UpperCamelCase__   (       pl.Callback       ):


		"""simple docstring"""

		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
							pl_module.logger.log_metrics(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case=True      ):


							'''simple docstring'''

							logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
							UpperCAmelCase		:							Optional[Any]        =       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
							UpperCAmelCase		:							List[str]        =       Path(pl_module.hparams.output_dir      )
							if type_path == "test":
												UpperCAmelCase		:							Dict        =       od / "test_results.txt"
												UpperCAmelCase		:							Dict        =       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.
												UpperCAmelCase		:							Union[str, Any]        =       od / f"{type_path}_results/{trainer.global_step:05d}.txt"
												UpperCAmelCase		:							int        =       od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
												results_file.parent.mkdir(exist_ok=snake_case      )
												generations_file.parent.mkdir(exist_ok=snake_case      )
							with open(snake_case   ,      "a+"      ) as writer:
												for key in sorted(snake_case      ):
																	if key in ["log", "progress_bar", "preds"]:
																						continue
																	UpperCAmelCase		:							Optional[Any]        =       metrics[key]
																	if isinstance(snake_case   ,      torch.Tensor      ):
																						UpperCAmelCase		:							Union[str, Any]        =       val.item()
																	UpperCAmelCase		:							List[str]        =       f"{key}: {val:.6f}\n"
																	writer.write(snake_case      )

							if not save_generations:
												return

							if "preds" in metrics:
												UpperCAmelCase		:							List[str]        =       "\n".join(metrics["preds"]      )
												generations_file.open("w+"      ).write(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							try:
												UpperCAmelCase		:							Tuple        =       pl_module.model.model.num_parameters()
							except AttributeError:
												UpperCAmelCase		:							int        =       pl_module.model.num_parameters()

							UpperCAmelCase		:							List[Any]        =       count_trainable_parameters(snake_case      )
							# 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   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

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







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''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 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__=1			,   __magic_name__=False							):



					'''simple docstring'''







					if metric == "rouge2":
										UpperCAmelCase		:							Dict        =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										UpperCAmelCase		:							int        =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "loss":
										UpperCAmelCase		:							Tuple        =       "{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."							)

					UpperCAmelCase		:							int        =       ModelCheckpoint(
					    dirpath=__magic_name__			,   filename=__magic_name__			,   monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   save_top_k=__magic_name__			,   )
					return checkpoint_callback





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					return EarlyStopping(
					    monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   patience=__magic_name__			,   verbose=__magic_name__			,   )
