
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 import save_json






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
 lowercase					  =   filter(lambda __SCREAMING_SNAKE_CASE							: p.requires_grad			,							model.parameters()							)
 lowercase					  =   sum([np.prod(p.size()							) for p in model_parameters]							)
 return params


UpperCAmelCase     				=			logging.getLogger(__name__)







class 	A_       (      pl.Callback       ):




 '''simple docstring'''


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

  if not save_generations:
   return

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





 @rank_zero_only
 def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):





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

  lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
  save_json(pl_module.metrics		,   pl_module.metrics_save_path	)
  return self._write_logs(_UpperCamelCase		,   _UpperCamelCase		,   'test'	)





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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=1			,							__SCREAMING_SNAKE_CASE=False							):
 if metric == "rouge2":
  lowercase					  =   '{val_avg_rouge2:.4f}-{step_count}'
 elif metric == "bleu":
  lowercase					  =   '{val_avg_bleu:.4f}-{step_count}'
 elif metric == "loss":
  lowercase					  =   '{val_avg_loss:.4f}-{step_count}'
 else:
  raise NotImplementedError(
      F'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
      ' this function.'							)

 lowercase					  =   ModelCheckpoint(
     dirpath=__UpperCamelCase			,							filename=__UpperCamelCase			,							monitor=F'''val_{metric}'''			,							mode='min' if 'loss' in metric else 'max'			,							save_top_k=__UpperCamelCase			,							)
 return checkpoint_callback






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
 return EarlyStopping(
     monitor=F'''val_{metric}'''			,							mode='min' if 'loss' in metric else 'max'			,							patience=__UpperCamelCase			,							verbose=__UpperCamelCase			,							)



import unittest
from timeit import timeit






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   first_num if first_num >= second_num else second_num
	lowercase					  =   max_num
	while (common_mult % first_num > 0) or (common_mult % second_num > 0):
		common_mult += max_num
	return common_mult






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return b if a == 0 else greatest_common_divisor(b % a			,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return first_num // greatest_common_divisor(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) * second_num






def 							UpperCAmelCase_		(							):
	lowercase					  =   (
	    'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
	)
	print(
	    'least_common_multiple_slow():'			,							timeit('least_common_multiple_slow(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)
	print(
	    'least_common_multiple_fast():'			,							timeit('least_common_multiple_fast(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Any										=	(
	    (10, 20),
	    (13, 15),
	    (4, 31),
	    (10, 42),
	    (43, 34),
	    (5, 12),
	    (12, 25),
	    (10, 25),
	    (6, 9),
	)
	_UpperCamelCase					:					int										=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i, (first_num, second_num) in enumerate(self.test_inputs	):
			lowercase					  =   least_common_multiple_slow(snake_case		,   snake_case	)
			lowercase					  =   least_common_multiple_fast(snake_case		,   snake_case	)
			with self.subTest(i=snake_case	):
				self.assertEqual(snake_case		,   self.expected_results[i]	)
				self.assertEqual(snake_case		,   self.expected_results[i]	)


if __name__ == "__main__":
	benchmark()
	unittest.main()

