



"""simple docstring"""






from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__snake_case				:  Union[str, Any] =       Lock()


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			global process_lock

			# we perform n swaps since after n swaps we know we are sorted
			# we *could* stop early if we are sorted already, but it takes as long to
			# find out we are sorted as it does to sort the list with this algorithm
			for i in range(0   ,10      ):
						if (i + position) % 2 == 0 and r_send is not None:
									# send your value to your right neighbor
									process_lock.acquire()
									r_send[1].send(UpperCamelCase__      )
									process_lock.release()

									# receive your right neighbor's value
									process_lock.acquire()
									__lowerCAmelCase :    Dict				=			rr_cv[0].recv()
									process_lock.release()

									# take the lower value since you are on the left
									__lowerCAmelCase :    Optional[Any]				=			min(UpperCamelCase__   ,UpperCamelCase__      )
						elif (i + position) % 2 != 0 and l_send is not None:
									# send your value to your left neighbor
									process_lock.acquire()
									l_send[1].send(UpperCamelCase__      )
									process_lock.release()

									# receive your left neighbor's value
									process_lock.acquire()
									__lowerCAmelCase :    Tuple				=			lr_cv[0].recv()
									process_lock.release()

									# take the higher value since you are on the right
									__lowerCAmelCase :    Optional[Any]				=			max(UpperCamelCase__   ,UpperCamelCase__      )
    # after all swaps are performed, send the values back to main
			result_pipe[1].send(UpperCamelCase__      )


def      _lowercase							(    __snake_case      )		->  Any:
			__lowerCAmelCase :    Optional[Any]				=			[]
			__lowerCAmelCase :    Optional[Any]				=			[]
			# initialize the list of pipes where the values will be retrieved
			for _ in arr:
						result_pipe.append(Pipe()      )
			# creates the processes
			# the first and last process only have one neighbor so they are made outside
			# of the loop
			__lowerCAmelCase :    Optional[int]				=			Pipe()
			__lowerCAmelCase :    List[str]				=			Pipe()
			process_array_.append(
			    Process(
			        target=UpperCamelCase__   ,args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])   ,)      )
			__lowerCAmelCase :    int				=			temp_rs
			__lowerCAmelCase :    int				=			temp_rr

			for i in range(1   ,len(UpperCamelCase__      ) - 1      ):
						__lowerCAmelCase :    Any				=			Pipe()
						__lowerCAmelCase :    List[str]				=			Pipe()
						process_array_.append(
						    Process(
						        target=UpperCamelCase__   ,args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])   ,)      )
						__lowerCAmelCase :    Any				=			temp_rs
						__lowerCAmelCase :    Optional[int]				=			temp_rr

			process_array_.append(
			    Process(
			        target=UpperCamelCase__   ,args=(
			            len(UpperCamelCase__      ) - 1,
			            arr[len(UpperCamelCase__      ) - 1],
			            temp_ls,
			            None,
			            temp_lr,
			            None,
			            result_pipe[len(UpperCamelCase__      ) - 1],
			        )   ,)      )

			# start the processes
			for p in process_array_:
						p.start()

			# wait for the processes to end and write their values to the list
			for p in range(0   ,len(UpperCamelCase__      )      ):
						__lowerCAmelCase :    Dict				=			result_pipe[p][0].recv()
						process_array_[p].join()
			return arr


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Union[str, Any]				=			list(range(10   ,0   ,-1      )      )
			print("Initial List"      )
			print(*UpperCamelCase__      )
			__lowerCAmelCase :    Union[str, Any]				=			odd_even_transposition(UpperCamelCase__      )
			print("Sorted List\n"      )
			print(*UpperCamelCase__      )


if __name__ == "__main__":
	main()
'''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       (		UpperCamelCase__					: Tuple							):


	"""simple docstring"""
	__UpperCAmelCase      					=      filter(lambda UpperCamelCase__							: p.requires_grad     ,       model.parameters()							)
	__UpperCAmelCase      					=      sum([np.prod(p.size()							) for p in model_parameters]							)
	return params


__lowerCAmelCase						:							List[str]    							=						logging.getLogger(__name__)





def    lowerCAmelCase       (		UpperCamelCase__					: Any     ,       UpperCamelCase__					: str							):


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

	__UpperCAmelCase      					=      ModelCheckpoint(
	    dirpath=UpperCamelCase__     ,       filename=UpperCamelCase__     ,       monitor=f"""val_{metric}"""     ,       mode='''max'''     ,       save_top_k=1     ,       every_n_epochs=1     ,       )

	return checkpoint_callback





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple     ,       UpperCamelCase__					: Optional[Any]							):


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






class     A ( pl.Callback ):





							def    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				int  ,		__a					:				Any						)				->						Optional[Any]:
								__UpperCAmelCase      					=      {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    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule  ,		__a					:				str  ,		__a					:				Any=True						)				->						None:
								logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
								__UpperCAmelCase      					=      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
								__UpperCAmelCase      					=      Path(pl_module.hparams.output_dir						)
								if type_path == "test":
									__UpperCAmelCase      					=      od / '''test_results.txt'''
									__UpperCAmelCase      					=      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.
									__UpperCAmelCase      					=      od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
									__UpperCAmelCase      					=      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
										__UpperCAmelCase      					=      metrics[key]
										if isinstance(__a  ,		torch.Tensor						):
											__UpperCAmelCase      					=      val.item()
										__UpperCAmelCase      					=      f"""{key}: {val:.6f}\n"""
										writer.write(__a						)

								if not save_generations:
									return

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





							@rank_zero_only
							def    snake_case__    (   self					:				List[Any]  ,		__a					:				Optional[int]  ,		__a					:				Union[str, Any]						)				->						List[Any]:
								try:
									__UpperCAmelCase      					=      pl_module.model.model.num_parameters()
								except AttributeError:
									__UpperCAmelCase      					=      pl_module.model.num_parameters()

								__UpperCAmelCase      					=      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    snake_case__    (   self					:				List[str]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule						)				->						Optional[int]:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								return self._write_logs(__a  ,		__a  ,		'''test'''						)






							@rank_zero_only
							def    snake_case__    (   self					:				Any  ,		__a					:				pl.Trainer  ,		__a					:				Union[str, Any]						)				->						Tuple:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")
