


"""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_       ( snake_case__		)					->  List[Any]:
			_UpperCamelCase					:Any					     =	filter(lambda snake_case__		: p.requires_grad       ,					model.parameters()		)
			_UpperCamelCase					:Any					     =	sum([np.prod(p.size()		) for p in model_parameters]		)
			return params


UpperCamelCase__				:Union[str, Any]			   =	logging.getLogger(__name__)





def 				A_       ( snake_case__       ,					snake_case__		)					->  List[Any]:
			if metric == "rouge2":
						_UpperCamelCase					:str					     =	'''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						_UpperCamelCase					:Dict					     =	'''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "em":
						_UpperCamelCase					:List[Any]					     =	'''{val_avg_em:.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					:str					     =	ModelCheckpoint(
			    dirpath=snake_case__       ,					filename=snake_case__       ,					monitor=f"val_{metric}"       ,					mode='''max'''       ,					save_top_k=3       ,					every_n_epochs=1       ,					)
			return checkpoint_callback





def 				A_       ( snake_case__       ,					snake_case__		)					->  Dict:
			return EarlyStopping(
			    monitor=f"val_{metric}"       ,					mode='''min''' if '''loss''' in metric else '''max'''       ,					patience=snake_case__       ,					verbose=snake_case__       ,					)






class      A(  pl.Callback		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	{f"lr_group_{i}": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
					pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__   )
		@rank_zero_only
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=True   )      ->	None:

					"""simple docstring"""



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

					if not save_generations:
								return

					if "preds" in metrics:
								_UpperCamelCase					:Optional[int]					     =	'''\n'''.join(metrics['''preds''']   )
								generations_file.open('''w+'''   ).write(SCREAMING_SNAKE_CASE__   )
		@rank_zero_only
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					try:
								_UpperCamelCase					:Optional[Any]					     =	pl_module.model.model.num_parameters()
					except AttributeError:
								_UpperCamelCase					:int					     =	pl_module.model.num_parameters()

					_UpperCamelCase					:Optional[int]					     =	count_trainable_parameters(SCREAMING_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   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



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





		@rank_zero_only
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[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")





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	UNetaDModel(
					    block_out_channels=(32, 64)					,				layers_per_block=2					,				sample_size=32					,				in_channels=3					,				out_channels=3					,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,				)
					return model





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

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






@slow
@require_torch
class      A(  unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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

