






def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Dict						)     ->      int:

						"""simple docstring"""



						if start == end:
												return start if lst[start] > item else start + 1
						if start > end:
												return start

						lowerCAmelCase__			  =  (start + end) // 2
						if lst[mid] < item:
												return binary_search(UpperCamelCase_			,				UpperCamelCase_			,				mid + 1			,				UpperCamelCase_						)
						elif lst[mid] > item:
												return binary_search(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				mid - 1						)
						else:
												return mid







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]						)     ->      Tuple:

						"""simple docstring"""



						lowerCAmelCase__			  =  len(UpperCamelCase_						)

						for index in range(1			,				UpperCamelCase_						):
												lowerCAmelCase__			  =  lst[index]
												lowerCAmelCase__			  =  binary_search(UpperCamelCase_			,				UpperCamelCase_			,				0			,				index - 1						)
												lowerCAmelCase__			  =  lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

						return lst







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    int						)     ->      Any:

						"""simple docstring"""



						if not left:
												return right

						if not right:
												return left

						if left[0] < right[0]:
												return [left[0], *merge(left[1:]			,				UpperCamelCase_						)]

						return [right[0], *merge(UpperCamelCase_			,				right[1:]						)]







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  len(UpperCamelCase_						)
						lowerCAmelCase__  ,				lowerCAmelCase__			  =  [], []
						lowerCAmelCase__			  =  [lst[0]]
						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  1
						while i < length:
												if lst[i] < lst[i - 1]:
																		runs.append(UpperCamelCase_						)
																		lowerCAmelCase__			  =  [lst[i]]
												else:
																		new_run.append(lst[i]						)
												i += 1
						runs.append(UpperCamelCase_						)

						for run in runs:
												sorted_runs.append(insertion_sort(UpperCamelCase_						)						)
						for run in sorted_runs:
												lowerCAmelCase__			  =  merge(UpperCamelCase_			,				UpperCamelCase_						)

						return sorted_array







def 		_UpperCamelCase      (			)     ->      List[str]:

						"""simple docstring"""



						lowerCAmelCase__			  =  [5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7]
						lowerCAmelCase__			  =  tim_sort(UpperCamelCase_						)
						print(UpperCamelCase_						)


if __name__ == "__main__":
					main()













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      (			UpperCamelCase_				:    Tuple						)     ->      str:

						"""simple docstring"""



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


__snake_case			:			Dict			   =   logging.getLogger(__name__)


class 			__SCREAMING_SNAKE_CASE    (      pl.Callback):






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  {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 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase=True      ):






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

												if not save_generations:
																		return

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






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												try:
																		lowerCAmelCase__			  =  pl_module.model.model.num_parameters()
												except AttributeError:
																		lowerCAmelCase__			  =  pl_module.model.num_parameters()

												lowerCAmelCase__			  =  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 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												save_json(pl_module.metrics   ,  pl_module.metrics_save_path      )
												return self._write_logs(_UpperCamelCase   ,  _UpperCamelCase   ,  'test'      )







						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""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 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    List[Any]=1			,				UpperCamelCase_				:    List[str]=False						)     ->      Optional[Any]:

						"""simple docstring"""



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

						lowerCAmelCase__			  =  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      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    int						)     ->      Dict:

						"""simple docstring"""



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





