'''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							(     UpperCAmelCase					)     ->			Any:




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


lowerCAmelCase_	:		Optional[Any]			=     logging.getLogger(__name__)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			str:




						'''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}'
						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=3     ,every_n_epochs=1     ,)
						return checkpoint_callback
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Any:




						'''simple docstring'''
						return EarlyStopping(
						    monitor=f'''val_{metric}'''     ,mode="min" if "loss" in metric else "max"     ,patience=lowerCamelCase_     ,verbose=lowerCamelCase_     ,)






class 		SCREAMING_SNAKE_CASE     (					pl.Callback	):







				'''simple docstring'''





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						Union[str, Any]       ,		lowercase__					:						Any					)							->Dict:


										'''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(UpperCAmelCase_					)





				@rank_zero_only
				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						pl.Trainer       ,		lowercase__					:						pl.LightningModule       ,		lowercase__					:						str       ,		lowercase__					:						List[Any]=True					)							->Optional[Any]:


										'''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=UpperCAmelCase_					)
																generations_file.parent.mkdir(exist_ok=UpperCAmelCase_					)
										with open(UpperCAmelCase_       ,		"a+"					) as writer:
																for key in sorted(UpperCAmelCase_					):
																						if key in ["log", "progress_bar", "preds"]:
																												continue
																						_UpperCamelCase   			=						metrics[key]
																						if isinstance(UpperCAmelCase_       ,		torch.Tensor					):
																												_UpperCamelCase   			=						val.item()
																						_UpperCamelCase   			=						f'''{key}: {val:.6f}\n'''
																						writer.write(UpperCAmelCase_					)

										if not save_generations:
																return

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





				@rank_zero_only
				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						str       ,		lowercase__					:						Union[str, Any]					)							->List[str]:


										'''simple docstring'''




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

										_UpperCamelCase   			=						count_trainable_parameters(UpperCAmelCase_					)
										# 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  snake_case__				(						self					:						Tuple       ,		lowercase__					:						pl.Trainer       ,		lowercase__					:						pl.LightningModule					)							->List[str]:


										'''simple docstring'''




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







				@rank_zero_only
				def  snake_case__				(						self					:						str       ,		lowercase__					:						pl.Trainer       ,		lowercase__					:						Optional[int]					)							->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")



'''simple docstring'''





lowerCAmelCase_	:		Union[str, Any]			=     """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCAmelCase_	:		List[Any]			=     [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCAmelCase_	:		int			=     {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


