





'''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       _lowerCAmelCase  ( lowerCamelCase_	:     str ):
			__lowercase   =							filter(lambda lowerCamelCase_ : p.requires_grad						,				model.parameters() )
			__lowercase   =							sum([np.prod(p.size() ) for p in model_parameters] )
			return params


_SCREAMING_SNAKE_CASE    =			logging.getLogger(__name__)






class 							__lowercase				(					pl.Callback  ):







	'''simple docstring'''

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







				'''simple docstring'''





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


	@rank_zero_only
	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase=True       )							->							None:







				'''simple docstring'''





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

				if not save_generations:
							return

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


	@rank_zero_only
	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





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

				__lowercase   =							count_trainable_parameters(_lowerCamelCase       )
				# 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   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				save_json(pl_module.metrics   ,pl_module.metrics_save_path       )
				return self._write_logs(_lowerCamelCase   ,_lowerCamelCase   ,'''test'''       )


	@rank_zero_only
	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							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       _lowerCAmelCase  ( lowerCamelCase_	:     Any						,				lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     str=1						,				lowerCamelCase_	:     List[str]=False ):
			if metric == "rouge2":
						__lowercase   =							'''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						__lowercase   =							'''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "loss":
						__lowercase   =							'''{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.''' )

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




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[Any]						,				lowerCamelCase_	:     Any ):
			return EarlyStopping(
			    monitor=f"val_{metric}"						,				mode='''min''' if '''loss''' in metric else '''max'''						,				patience=lowerCamelCase_						,				verbose=lowerCamelCase_						,				)












'''simple docstring'''



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 							__lowercase				(					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					unittest.TestCase  ):







	'''simple docstring'''

	a			: str      =				StableDiffusionModelEditingPipeline
	a			: int      =				TEXT_TO_IMAGE_PARAMS
	a			: List[Any]      =				TEXT_TO_IMAGE_BATCH_PARAMS
	a			: Tuple      =				TEXT_TO_IMAGE_IMAGE_PARAMS
	a			: Any      =				TEXT_TO_IMAGE_IMAGE_PARAMS


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0       )
				__lowercase   =							UNetaDConditionModel(
				    block_out_channels=(32, 64)   ,layers_per_block=2   ,sample_size=32   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=32   ,)
				__lowercase   =							DDIMScheduler()
				torch.manual_seed(0       )
				__lowercase   =							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       )
				__lowercase   =							CLIPTextConfig(
				    bos_token_id=0   ,eos_token_id=2   ,hidden_size=32   ,intermediate_size=37   ,layer_norm_eps=1E-0_5   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1000   ,)
				__lowercase   =							CLIPTextModel(_lowerCamelCase       )
				__lowercase   =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

				__lowercase   =							{
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    # Setting height and width to None to prevent OOMs on CPU.
				    '''height''': None,
				    '''width''': None,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							sd_pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]       )

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


	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							'''french fries'''
				__lowercase   =							sd_pipe(**_lowerCamelCase   ,negative_prompt=_lowerCamelCase       )
				__lowercase   =							output.images
				__lowercase   =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]       )

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


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0_0_8_5   ,beta_end=0.0_1_2   ,beta_schedule='''scaled_linear'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

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

				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]       )

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


	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							PNDMScheduler()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				# the pipeline does not expect pndm so test if it raises error.
				with self.assertRaises(_lowerCamelCase       ):
							__lowercase   =							sd_pipe(**_lowerCamelCase       ).images


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				super().test_inference_batch_single_identical(expected_max_diff=5E-3       )


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )






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







	'''simple docstring'''

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







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    '''num_inference_steps''': 3,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)

				__lowercase   =							np.array(
				    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]       )

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

				# make sure image changes after editing
				pipe.edit_model('''A pack of roses'''   ,'''A pack of blue roses'''       )

				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(expected_slice - image_slice       ).max() > 1E-1


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







				'''simple docstring'''





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

				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							DDIMScheduler.from_pretrained(_lowerCamelCase   ,subfolder='''scheduler'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(
				    _lowerCamelCase   ,scheduler=_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				__lowercase   =							pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing(1       )
				pipe.enable_sequential_cpu_offload()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       )

				__lowercase   =							torch.cuda.max_memory_allocated()
				# make sure that less than 4.4 GB is allocated
				assert mem_bytes < 4.4 * 10**9





