




"""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 							UpperCAmelCase		(		snake_case			:					Optional[int]    ):
					_lowerCAmelCase:str									=  filter(lambda snake_case    : p.requires_grad      ,       model.parameters()    )
					_lowerCAmelCase:int									=  sum([np.prod(p.size()    ) for p in model_parameters]    )
					return params


UpperCamelCase__						=			logging.getLogger(__name__)





def 							UpperCAmelCase		(		snake_case			:					Any      ,       snake_case			:					List[str]    ):
					if metric == "rouge2":
										_lowerCAmelCase:Union[str, Any]									=  '''{val_avg_rouge2:.4f}-{step_count}'''
					elif metric == "bleu":
										_lowerCAmelCase:Any									=  '''{val_avg_bleu:.4f}-{step_count}'''
					elif metric == "em":
										_lowerCAmelCase:Dict									=  '''{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.'''    )

					_lowerCAmelCase:Optional[int]									=  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 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					Any    ):
					return EarlyStopping(
					    monitor=F'val_{metric}'      ,       mode='''min''' if '''loss''' in metric else '''max'''      ,       patience=snake_case      ,       verbose=snake_case      ,       )





class   a__			(       pl.Callback  ):





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




											"""simple docstring"""







											_lowerCAmelCase: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(a__)





						@rank_zero_only
						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	pl.Trainer     ,a__      :	pl.LightningModule     ,a__      :	str     ,a__      :	List[Any]=True)						->       None:




											"""simple docstring"""







											logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****')
											_lowerCAmelCase: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
											_lowerCAmelCase:str									=  Path(pl_module.hparams.output_dir)
											if type_path == "test":
																_lowerCAmelCase:Optional[Any]									=  od / '''test_results.txt'''
																_lowerCAmelCase:Optional[Any]									=  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.
																_lowerCAmelCase:Optional[Any]									=  od / F'{type_path}_results/{trainer.global_step:05d}.txt'
																_lowerCAmelCase:Optional[Any]									=  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
																					_lowerCAmelCase:int									=  metrics[key]
																					if isinstance(a__     ,torch.Tensor):
																										_lowerCAmelCase:Optional[Any]									=  val.item()
																					_lowerCAmelCase:Optional[int]									=  F'{key}: {val:.6f}\n'
																					writer.write(a__)

											if not save_generations:
																return

											if "preds" in metrics:
																_lowerCAmelCase:Union[str, Any]									=  '''\n'''.join(metrics['''preds'''])
																generations_file.open('''w+''').write(a__)





						@rank_zero_only
						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Dict)						->       List[Any]:




											"""simple docstring"""







											try:
																_lowerCAmelCase:List[str]									=  pl_module.model.model.num_parameters()
											except AttributeError:
																_lowerCAmelCase:Optional[Any]									=  pl_module.model.num_parameters()

											_lowerCAmelCase:Optional[Any]									=  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       __UpperCamelCase     (							self      :	Optional[Any]     ,a__      :	pl.Trainer     ,a__      :	pl.LightningModule)						->       Tuple:




											"""simple docstring"""







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







						@rank_zero_only
						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	pl.Trainer     ,a__      :	Dict)						->       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")












"""simple docstring"""






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)






