
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        __lowercase(							__snake_case				:		List[Any]					)       ->   Tuple:
       __snake_case						       =   filter(lambda __snake_case					: p.requires_grad    ,model.parameters()					)
       __snake_case						       =   sum([np.prod(p.size()					) for p in model_parameters]					)
       return params


lowerCamelCase_				:							List[Any]       =    logging.getLogger(__name__)







class  _lowerCamelCase			(pl.Callback					):

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   {f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
            pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_    )

     @rank_zero_only
     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=True    ):
            logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
            __snake_case						       =   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
            __snake_case						       =   Path(pl_module.hparams.output_dir    )
            if type_path == "test":
                   __snake_case						       =   od / 'test_results.txt'
                   __snake_case						       =   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.
                   __snake_case						       =   od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
                   __snake_case						       =   od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
                   results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_    )
                   generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_    )
            with open(SCREAMING_SNAKE_CASE_							,       'a+'    ) as writer:
                   for key in sorted(SCREAMING_SNAKE_CASE_    ):
                          if key in ["log", "progress_bar", "preds"]:
                                 continue
                          __snake_case						       =   metrics[key]
                          if isinstance(SCREAMING_SNAKE_CASE_							,       torch.Tensor    ):
                                 __snake_case						       =   val.item()
                          __snake_case						       =   f'''{key}: {val:.6f}\n'''
                          writer.write(SCREAMING_SNAKE_CASE_    )

            if not save_generations:
                   return

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

     @rank_zero_only
     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            try:
                   __snake_case						       =   pl_module.model.model.num_parameters()
            except AttributeError:
                   __snake_case						       =   pl_module.model.num_parameters()

            __snake_case						       =   count_trainable_parameters(SCREAMING_SNAKE_CASE_    )
            # 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							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            save_json(pl_module.metrics							,       pl_module.metrics_save_path    )
            return self._write_logs(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       'test'    )

     @rank_zero_only
     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_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        __lowercase(							__snake_case				:		int    ,__snake_case				:		List[Any]    ,__snake_case				:		Union[str, Any]=1    ,__snake_case				:		Union[str, Any]=False					)       ->   Any:




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

       __snake_case						       =   ModelCheckpoint(
           dirpath=__snake_case    ,filename=__snake_case    ,monitor=f'''val_{metric}'''    ,mode='min' if 'loss' in metric else 'max'    ,save_top_k=__snake_case    ,)
       return checkpoint_callback
def        __lowercase(							__snake_case				:		Optional[Any]    ,__snake_case				:		Optional[int]					)       ->   Optional[Any]:
       return EarlyStopping(
           monitor=f'''val_{metric}'''    ,mode='min' if 'loss' in metric else 'max'    ,patience=__snake_case    ,verbose=__snake_case    ,)





'''simple docstring'''



def 	__lowerCAmelCase       (				lowerCamelCase :    list[list[int]]					,		lowerCamelCase :    list[list[int]]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	[]
	__lowerCAmelCase       				=	len(lowerCamelCase       )
	for i in range(lowerCamelCase       ):
		__lowerCAmelCase       				=	[]
		for j in range(lowerCamelCase       ):
			__lowerCAmelCase       				=	0
			for k in range(lowerCamelCase       ):
				__lowerCAmelCase       				=	val + matrix_a[i][k] * matrix_b[k][j]
			list_a.append(lowerCamelCase       )
		matrix_c.append(lowerCamelCase       )
	return matrix_c



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	return [[int(row == column       ) for column in range(lowerCamelCase       )] for row in range(lowerCamelCase       )]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	identity(2       )
	__lowerCAmelCase       				=	[[1, 1], [1, 0]]
	__lowerCAmelCase       				=	n - 1
	while n > 0:
		if n % 2 == 1:
			__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	multiply(lowerCamelCase					,		lowerCamelCase       )
		__lowerCAmelCase       				=	int(n / 2       )
	return res_matrix[0][0]



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if n <= 1:
		return n
	__lowerCAmelCase       				=	0
	__lowerCAmelCase       				=	1
	for _ in range(2					,		n + 1       ):
		__lowerCAmelCase				,							__lowerCAmelCase       				=	fiba, fiba + fiba
	return fiba



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
		__lowerCAmelCase       				=	int("".join(c for c in ordinal if c in "0123456789"       )       )  # 1000th --> 1000
		print(
		    f'''{ordinal} fibonacci number using matrix exponentiation is '''
		    f'''{nth_fibonacci_matrix(lowerCamelCase       )} and using bruteforce is '''
		    f'''{nth_fibonacci_bruteforce(lowerCamelCase       )}\n'''       )
	# from timeit import timeit
	# print(timeit("nth_fibonacci_matrix(1000000)",
	#              "from main import nth_fibonacci_matrix", number=5))
	# print(timeit("nth_fibonacci_bruteforce(1000000)",
	#              "from main import nth_fibonacci_bruteforce", number=5))
	# 2.3342058970001744
	# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()