

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__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							filter(lambda UpperCAmelCase__      : p.requires_grad		,					model.parameters()      )
					__lowerCAmelCase					=							sum([np.prod(p.size()      ) for p in model_parameters]      )
					return params


lowerCamelCase      				=     logging.getLogger(__name__)


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if metric == "rouge2":
										__lowerCAmelCase					=							'{val_avg_rouge2:.4f}-{step_count}'
					elif metric == "bleu":
										__lowerCAmelCase					=							'{val_avg_bleu:.4f}-{step_count}'
					elif metric == "em":
										__lowerCAmelCase					=							'{val_avg_em:.4f}-{step_count}'
					elif metric == "loss":
										__lowerCAmelCase					=							'{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.'      )

					__lowerCAmelCase					=							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__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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





class     snake_case_			(				pl.Callback							):


							"""simple docstring"""





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

												if not save_generations:
																	return

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




							@rank_zero_only
							def        A__  ( self					,						_A					,						_A							):
												try:
																	__lowerCAmelCase					=							pl_module.model.model.num_parameters()
												except AttributeError:
																	__lowerCAmelCase					=							pl_module.model.num_parameters()

												__lowerCAmelCase					=							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        A__  ( self					,						_A					,						_A							):
												save_json(pl_module.metrics					,						pl_module.metrics_save_path							)
												return self._write_logs(_A					,						_A					,						'test'							)




							@rank_zero_only
							def        A__  ( self					,						_A					,						_A							):
												save_json(pl_module.metrics					,						pl_module.metrics_save_path							)
												# Uncommenting this will save val generations
												# return self._write_logs(trainer, pl_module, "valid")









from __future__ import annotations

from collections.abc import MutableSequence





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A							):
												if len(_A							) != degree + 1:
																	raise ValueError(
																	    'The number of coefficients should be equal to the degree + 1.'							)

												__lowerCAmelCase					=							list(_A							)
												__lowerCAmelCase					=							degree




							def __add__( self					,						_A							):

												if self.degree > polynomial_a.degree:
																	__lowerCAmelCase					=							self.coefficients[:]
																	for i in range(polynomial_a.degree + 1							):
																						coefficients[i] += polynomial_a.coefficients[i]
																	return Polynomial(self.degree					,						_A							)
												else:
																	__lowerCAmelCase					=							polynomial_a.coefficients[:]
																	for i in range(self.degree + 1							):
																						coefficients[i] += self.coefficients[i]
																	return Polynomial(polynomial_a.degree					,						_A							)




							def __sub__( self					,						_A							):
												return self + polynomial_a * Polynomial(0					,						[-1]							)




							def __neg__( self							):
												return Polynomial(self.degree					,						[-c for c in self.coefficients]							)




							def __mul__( self					,						_A							):
												__lowerCAmelCase					=							[0] * (self.degree + polynomial_a.degree + 1)
												for i in range(self.degree + 1							):
																	for j in range(polynomial_a.degree + 1							):
																						coefficients[i + j] += (
																						    self.coefficients[i] * polynomial_a.coefficients[j]
																						)

												return Polynomial(self.degree + polynomial_a.degree					,						_A							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							0
												for i in range(self.degree + 1							):
																	result += self.coefficients[i] * (substitution**i)
												return result




							def __str__( self							):
												__lowerCAmelCase					=							''
												for i in range(self.degree					,						-1					,						-1							):
																	if self.coefficients[i] == 0:
																						continue
																	elif self.coefficients[i] > 0:
																						if polynomial:
																											polynomial += " + "
																	else:
																						polynomial += " - "

																	if i == 0:
																						polynomial += str(abs(self.coefficients[i]							)							)
																	elif i == 1:
																						polynomial += str(abs(self.coefficients[i]							)							) + "x"
																	else:
																						polynomial += str(abs(self.coefficients[i]							)							) + "x^" + str(_A							)

												return polynomial




							def __repr__( self							):
												return self.__str__()




							def        A__  ( self							):
												__lowerCAmelCase					=							[0] * self.degree
												for i in range(self.degree							):
																	__lowerCAmelCase					=							self.coefficients[i + 1] * (i + 1)
												return Polynomial(self.degree - 1					,						_A							)




							def        A__  ( self					,						_A = 0							):
												__lowerCAmelCase					=							[0] * (self.degree + 2)
												__lowerCAmelCase					=							constant
												for i in range(self.degree + 1							):
																	__lowerCAmelCase					=							self.coefficients[i] / (i + 1)
												return Polynomial(self.degree + 1					,						_A							)




							def __eq__( self					,						_A							):
												if not isinstance(_A					,						_A							):
																	return False

												if self.degree != polynomial_a.degree:
																	return False

												for i in range(self.degree + 1							):
																	if self.coefficients[i] != polynomial_a.coefficients[i]:
																						return False

												return True




							def __ne__( self					,						_A							):
												return not self.__eq__(_A							)






