


"""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 import save_json






def 						lowercase				(	_snake_case		: str	)	->Tuple:
	"""simple docstring"""





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


SCREAMING_SNAKE_CASE					:       Tuple							   =							logging.getLogger(__name__)



class      _UpperCAmelCase					(				pl.Callback							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_   ):





			'''simple docstring'''

			__snake_case      :		int         =							{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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			a_							,			a_=True   ):





			'''simple docstring'''

			logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
			__snake_case      :		int         =							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      :		Optional[int]         =							Path(pl_module.hparams.output_dir   )
			if type_path == "test":
				__snake_case      :		int         =							od / '''test_results.txt'''
				__snake_case      :		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.
				__snake_case      :		int         =							od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
				__snake_case      :		List[str]         =							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
					__snake_case      :		Any         =							metrics[key]
					if isinstance(a_							,			torch.Tensor   ):
						__snake_case      :		str         =							val.item()
					__snake_case      :		Union[str, Any]         =							f"""{key}: {val:.6f}\n"""
					writer.write(a_   )

			if not save_generations:
				return

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



		@rank_zero_only
		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_   ):





			'''simple docstring'''

			try:
				__snake_case      :		Dict         =							pl_module.model.model.num_parameters()
			except AttributeError:
				__snake_case      :		Tuple         =							pl_module.model.num_parameters()

			__snake_case      :		str         =							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 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_   ):





			'''simple docstring'''

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



		@rank_zero_only
		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_   ):





			'''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")






def 						lowercase				(	_snake_case		: str	,  _snake_case		: int	,  _snake_case		: int=1	,  _snake_case		: Any=False	)	->List[str]:
	"""simple docstring"""





	if metric == "rouge2":
		__snake_case      :		int         =							'''{val_avg_rouge2:.4f}-{step_count}'''
	elif metric == "bleu":
		__snake_case      :		Union[str, Any]         =							'''{val_avg_bleu:.4f}-{step_count}'''
	elif metric == "loss":
		__snake_case      :		Tuple         =							'''{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      :		int         =							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		: Tuple	,  _snake_case		: Tuple	)	->Union[str, Any]:
	"""simple docstring"""





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






'''simple docstring'''




def 	_UpperCAmelCase       (				_UpperCamelCase       :      list[list[int]],  _UpperCamelCase       :      list[list[int]]  )      -> list[list[int]]:
    A_      =  []
    A_      =  len(_UpperCamelCase  )
    for i in range(_UpperCamelCase  ):
        A_      =  []
        for j in range(_UpperCamelCase  ):
            A_      =  0
            for k in range(_UpperCamelCase  ):
                A_      =  val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(_UpperCamelCase  )
        matrix_c.append(_UpperCamelCase  )
    return matrix_c





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> list[list[int]]:
    return [[int(row == column  ) for column in range(_UpperCamelCase  )] for row in range(_UpperCamelCase  )]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  identity(2  )
    A_      =  [[1, 1], [1, 0]]
    A_      =  n - 1
    while n > 0:
        if n % 2 == 1:
            A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  int(n / 2  )
    return res_matrix[0][0]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  0
    A_      =  1
    for _ in range(2,  n + 1  ):
        A_  ,A_      =  fiba, fiba + fiba
    return fiba





def 	_UpperCAmelCase       (				)      -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        A_      =  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(_UpperCamelCase  )} and using bruteforce is '''
            F'''{nth_fibonacci_bruteforce(_UpperCamelCase  )}\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()






