
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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Union[str, Any])					->    Tuple:






							'''simple docstring'''





							__UpperCamelCase	:   List[Any]			   =					filter(lambda _lowerCamelCase: p.requires_grad						,  model.parameters())
							__UpperCamelCase	:   Dict			   =					sum([np.prod(p.size()) for p in model_parameters])
							return params


lowercase  :					int													=       logging.getLogger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			Optional[int])					->    Union[str, Any]:






							'''simple docstring'''





							if metric == "rouge2":
														__UpperCamelCase	:   Optional[int]			   =					"{val_avg_rouge2:.4f}-{step_count}"
							elif metric == "bleu":
														__UpperCamelCase	:   Optional[Any]			   =					"{val_avg_bleu:.4f}-{step_count}"
							elif metric == "em":
														__UpperCamelCase	:   List[Any]			   =					"{val_avg_em:.4f}-{step_count}"
							elif metric == "loss":
														__UpperCamelCase	:   int			   =					"{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	:   List[str]			   =					ModelCheckpoint(
							    dirpath=A__						,  filename=A__						,  monitor=F'val_{metric}'						,  mode="max"						,  save_top_k=1						,  every_n_epochs=1						,  )

							return checkpoint_callback




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[str]						,  _lowerCamelCase :			Dict)					->    int:






							'''simple docstring'''





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




class  lowerCamelCase__							(				pl.Callback):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Dict     ,   a		:Any						)		-> Dict:
								__UpperCamelCase	:   List[str]			   =					{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    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:pl.Trainer     ,   a		:pl.LightningModule     ,   a		:str     ,   a		:Optional[Any]=True						)		-> None:
								logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'						)
								__UpperCamelCase	:   Dict			   =					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	:   str			   =					Path(pl_module.hparams.output_dir						)
								if type_path == "test":
															__UpperCamelCase	:   str			   =					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	:   Optional[int]			   =					od / f'{type_path}_results/{trainer.global_step:05d}.txt'
															__UpperCamelCase	:   Optional[Any]			   =					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
																						__UpperCamelCase	:   List[Any]			   =					metrics[key]
																						if isinstance(lowerCAmelCase_     ,   torch.Tensor						):
																													__UpperCamelCase	:   str			   =					val.item()
																						__UpperCamelCase	:   Tuple			   =					f'{key}: {val:.6f}\n'
																						writer.write(lowerCAmelCase_						)

								if not save_generations:
															return

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







	@rank_zero_only
	def    _lowerCamelCase				(					self		:List[str]     ,   a		:List[Any]     ,   a		:Optional[Any]						)		-> Tuple:
								try:
															__UpperCamelCase	:   Tuple			   =					pl_module.model.model.num_parameters()
								except AttributeError:
															__UpperCamelCase	:   Optional[int]			   =					pl_module.model.num_parameters()

								__UpperCamelCase	:   Dict			   =					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    _lowerCamelCase				(					self		:List[str]     ,   a		:pl.Trainer     ,   a		:pl.LightningModule						)		-> Optional[Any]:
								save_json(pl_module.metrics     ,   pl_module.metrics_save_path						)
								return self._write_logs(lowerCAmelCase_     ,   lowerCAmelCase_     ,   "test"						)







	@rank_zero_only
	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:pl.Trainer     ,   a		:Dict						)		-> List[str]:
								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_					=      [
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]

def 				snake_case      (   A__						):
			UpperCAmelCase_     : List[str]			     =					{"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
			UpperCAmelCase_     : Optional[Any]			     =					0
			UpperCAmelCase_     : Tuple			     =					0
			while place < len(A__						):
						if (place + 1 < len(A__						)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					[]
			for arabic, roman in ROMAN:
						((UpperCAmelCase_)     ,	(UpperCAmelCase_))     : str			     =					divmod(A__	,A__						)
						result.append(roman * factor						)
						if number == 0:
									break
			return "".join(A__						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



