
'''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_rag import save_json


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

		"""simple docstring"""






		_UpperCamelCase						    =	filter(lambda lowerCAmelCase : p.requires_grad						,  model.parameters() )
		_UpperCamelCase						    =	sum([np.prod(p.size() ) for p in model_parameters] )
		return params


lowerCamelCase__	=							logging.getLogger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		if metric == "rouge2":
				_UpperCamelCase						    =	"""{val_avg_rouge2:.4f}-{step_count}"""
		elif metric == "bleu":
				_UpperCamelCase						    =	"""{val_avg_bleu:.4f}-{step_count}"""
		elif metric == "em":
				_UpperCamelCase						    =	"""{val_avg_em:.4f}-{step_count}"""
		elif metric == "loss":
				_UpperCamelCase						    =	"""{val_avg_loss:.4f}-{step_count}"""
		else:
				raise NotImplementedError(
				    F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
				    """ function.""" )

		_UpperCamelCase						    =	ModelCheckpoint(
		    dirpath=lowerCAmelCase						,  filename=lowerCAmelCase						,  monitor=F'val_{metric}'						,  mode="""max"""						,  save_top_k=1						,  every_n_epochs=1						,  )

		return checkpoint_callback


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






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







class        lowerCAmelCase__      (   pl.Callback	):

						def  A_				(			self       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

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

						@rank_zero_only
						def  A_				(			self       ,							a       ,							a       ,							a       ,							a=True				)					->      None:


								'''simple docstring'''

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

								if not save_generations:
										return

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

						@rank_zero_only
						def  A_				(			self       ,							a       ,							a				)					->      List[str]:


								'''simple docstring'''

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

								_UpperCamelCase						    =	count_trainable_parameters(a				)
								# 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       ,							a       ,							a				)					->      int:


								'''simple docstring'''

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







						@rank_zero_only
						def  A_				(			self       ,							a       ,							a				)					->      Union[str, Any]:


								'''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")





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		StableDiffusionInpaintPipeline
						UpperCamelCase_							: int	=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						UpperCamelCase_							: Union[str, Any]	=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						UpperCamelCase_							: Tuple	=		frozenset(
						    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						UpperCamelCase_							: int	=		frozenset([]	)

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


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	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       ,							attention_head_dim=(2, 4)       ,							use_linear_projection=a       ,							)
								_UpperCamelCase						    =	PNDMScheduler(skip_prk_steps=a				)
								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       ,							sample_size=1_28       ,							)
								torch.manual_seed(0				)
								_UpperCamelCase						    =	CLIPTextConfig(
								    bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=32       ,							intermediate_size=37       ,							layer_norm_eps=1e-05       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=10_00       ,							hidden_act="""gelu"""       ,							projection_dim=5_12       ,							)
								_UpperCamelCase						    =	CLIPTextModel(a				)
								_UpperCamelCase						    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

								_UpperCamelCase						    =	{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components

						def  A_				(			self       ,							a       ,							a=0				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(a				)				).to(a				)
								_UpperCamelCase						    =	image.cpu().permute(0       ,							2       ,							3       ,							1				)[0]
								_UpperCamelCase						    =	Image.fromarray(np.uinta(a				)				).convert("""RGB"""				).resize((64, 64)				)
								_UpperCamelCase						    =	Image.fromarray(np.uinta(image + 4				)				).convert("""RGB"""				).resize((64, 64)				)
								if str(a				).startswith("""mps"""				):
										_UpperCamelCase						    =	torch.manual_seed(a				)
								else:
										_UpperCamelCase						    =	torch.Generator(device=a				).manual_seed(a				)
								_UpperCamelCase						    =	{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """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				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline(**a				)
								_UpperCamelCase						    =	sd_pipe.to(a				)
								sd_pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	sd_pipe(**a				).images
								_UpperCamelCase						    =	image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								_UpperCamelCase						    =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]				)

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







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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







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

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


								'''simple docstring'''

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

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench.npy"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline.from_pretrained(a       ,							safety_checker=a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								pipe.enable_attention_slicing()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							output_type="""np"""       ,							)
								_UpperCamelCase						    =	output.images[0]

								assert image.shape == (5_12, 5_12, 3)
								assert np.abs(expected_image - image				).max() < 9e-3

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline.from_pretrained(
								    a       ,							torch_dtype=torch.floataa       ,							safety_checker=a       ,							)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								pipe.enable_attention_slicing()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							output_type="""np"""       ,							)
								_UpperCamelCase						    =	output.images[0]

								assert image.shape == (5_12, 5_12, 3)
								assert np.abs(expected_image - image				).max() < 5e-1







						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	PNDMScheduler.from_pretrained(a       ,							subfolder="""scheduler"""				)
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline.from_pretrained(
								    a       ,							safety_checker=a       ,							scheduler=a       ,							torch_dtype=torch.floataa       ,							)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								pipe.enable_attention_slicing(1				)
								pipe.enable_sequential_cpu_offload()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							num_inference_steps=2       ,							output_type="""np"""       ,							)

								_UpperCamelCase						    =	torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.65 * 10**9



