



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


__lowerCAmelCase						      =       logging.getLogger(__name__)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		if metric == "rouge2":
				_a					:				Union[str, Any]				=							'{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				_a					:				Tuple				=							'{val_avg_bleu:.4f}-{step_count}'
		elif metric == "em":
				_a					:				Any				=							'{val_avg_em:.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					:				Tuple				=							ModelCheckpoint(
		    dirpath=lowerCAmelCase_						,      filename=lowerCAmelCase_						,      monitor=f"""val_{metric}"""						,      mode='max'						,      save_top_k=3						,      every_n_epochs=1						,      )
		return checkpoint_callback



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Tuple:
		return EarlyStopping(
		    monitor=f"""val_{metric}"""						,      mode='min' if 'loss' in metric else 'max'						,      patience=lowerCAmelCase_						,      verbose=lowerCAmelCase_						,      )

class      __magic_name__						(   pl.Callback       ):







							def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Union[str, Any]		):
									_a					:				Optional[Any]				=							{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 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	pl.Trainer     ,_UpperCAmelCase :	pl.LightningModule     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]=True		):
									logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""		)
									_a					:				Dict				=							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					:				Dict				=							od / 'test_results.txt'
											_a					:				Dict				=							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					:				Any				=							od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
											_a					:				Tuple				=							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
													_a					:				Tuple				=							metrics[key]
													if isinstance(_UpperCAmelCase     ,torch.Tensor		):
															_a					:				Union[str, Any]				=							val.item()
													_a					:				Optional[Any]				=							F"""{key}: {val:.6f}\n"""
													writer.write(_UpperCAmelCase		)

									if not save_generations:
											return

									if "preds" in metrics:
											_a					:				Optional[int]				=							'\n'.join(metrics['preds']		)
											generations_file.open('w+'		).write(_UpperCAmelCase		)







							@rank_zero_only
							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Optional[int]		):
									try:
											_a					:				Optional[Any]				=							pl_module.model.model.num_parameters()
									except AttributeError:
											_a					:				Any				=							pl_module.model.num_parameters()

									_a					:				List[Any]				=							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 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	pl.Trainer     ,_UpperCAmelCase :	pl.LightningModule		):
									save_json(pl_module.metrics     ,pl_module.metrics_save_path		)
									return self._write_logs(_UpperCAmelCase     ,_UpperCAmelCase     ,'test'		)







							@rank_zero_only
							def 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	pl.Trainer     ,_UpperCAmelCase :	Any		):
									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'''

# Function to print upper half of diamond (pyramid)



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:
		for i in range(0						,      lowerCAmelCase_  ):
				for _ in range(0						,      n - i - 1  ):  # printing spaces
						print(' '						,      end=''  )
				for _ in range(0						,      i + 1  ):  # printing stars
						print('* '						,      end=''  )
				print()



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		for i in range(lowerCAmelCase_						,      0						,      -1  ):
				for _ in range(lowerCAmelCase_						,      0						,      -1  ):  # printing stars
						print('* '						,      end=''  )
				print()
				for _ in range(n - i + 1						,      0						,      -1  ):  # printing spaces
						print(' '						,      end=''  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Tuple:
		if n <= 0:
				print('       ...       ....        nothing printing :('  )
				return
		floyd(lowerCAmelCase_  )  # upper half
		reverse_floyd(lowerCAmelCase_  )  # lower half


if __name__ == "__main__":
							print(r'''| /\ | |- |  |-  |--| |\  /| |-''')
							print(r'''|/  \| |- |_ |_  |__| | \/ | |_''')
							__lowerCAmelCase						      =       1
							while K:
														__lowerCAmelCase						      =       int(input('''enter the number and , and see the magic : '''))
														print()
														pretty_print(user_number)
														__lowerCAmelCase						      =       int(input('''press 0 to exit... and 1 to continue...'''))

							print('''Good Bye...''')




