


'''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 			_lowercase       (				UpperCamelCase__       :					List[str]			):
	__A  :		Tuple       =			filter(lambda UpperCamelCase__			: p.requires_grad,				model.parameters()			)
	__A  :		Any       =			sum([np.prod(p.size()			) for p in model_parameters]			)
	return params


UpperCAmelCase_ :     Dict								=      logging.getLogger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Any			):
	if metric == "rouge2":
		__A  :		Any       =			'{val_avg_rouge2:.4f}-{step_count}'
	elif metric == "bleu":
		__A  :		Dict       =			'{val_avg_bleu:.4f}-{step_count}'
	elif metric == "em":
		__A  :		Optional[int]       =			'{val_avg_em:.4f}-{step_count}'
	elif metric == "loss":
		__A  :		Tuple       =			'{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.'			)

	__A  :		List[Any]       =			ModelCheckpoint(
	    dirpath=UpperCamelCase__,				filename=UpperCamelCase__,				monitor=f"""val_{metric}""",				mode='max',				save_top_k=1,				every_n_epochs=1,				)

	return checkpoint_callback


def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Union[str, Any]			):
	return EarlyStopping(
	    monitor=f"""val_{metric}""",				mode='min' if 'loss' in metric else 'max',				patience=UpperCamelCase__,				verbose=UpperCamelCase__,				)


class 						_lowerCamelCase					(    pl.Callback				):




	'''simple docstring'''


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

		"""simple docstring"""







		__A  :		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(__lowercase						)


	@rank_zero_only
	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase=True						):

		"""simple docstring"""







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

		if not save_generations:
			return

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


	@rank_zero_only
	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

		"""simple docstring"""







		try:
			__A  :		Any       =			pl_module.model.model.num_parameters()
		except AttributeError:
			__A  :		List[Any]       =			pl_module.model.num_parameters()

		__A  :		Optional[int]       =			count_trainable_parameters(__lowercase						)
		# 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      ,      __lowercase      ,      __lowercase						):

		"""simple docstring"""







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







	@rank_zero_only
	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

		"""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'''
from collections.abc import Sequence


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__			)			)


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 __A  :		Optional[Any]       =			0.0
 for coeff in reversed(UpperCamelCase__			):
  __A  :		List[str]       =			result * x + coeff
 return result


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      (0.0, 0.0, 5.0, 9.3, 7.0)
 UpperCAmelCase_ :     Any								=      10.0
 print(evaluate_poly(poly, x))
 print(horner(poly, x))






