






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        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[int]				):
			__a						:  Dict									=     filter(lambda lowerCamelCase_				: p.requires_grad   ,  model.parameters()				)
			__a						:  Union[str, Any]									=     sum([np.prod(p.size()				) for p in model_parameters]				)
			return params


SCREAMING_SNAKE_CASE__				     =			logging.getLogger(__name__)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       Optional[Any]				):
			if metric == "rouge2":
						__a						:  int									=     '{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						__a						:  Optional[Any]									=     '{val_avg_bleu:.4f}-{step_count}'
			elif metric == "em":
						__a						:  Any									=     '{val_avg_em:.4f}-{step_count}'
			elif metric == "loss":
						__a						:  int									=     '{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.'				)

			__a						:  Union[str, Any]									=     ModelCheckpoint(
			    dirpath=lowerCamelCase_   ,  filename=lowerCamelCase_   ,  monitor=f'''val_{metric}'''   ,  mode='max'   ,  save_top_k=1   ,  every_n_epochs=1   ,  )

			return checkpoint_callback

def        UpperCAmelCase__		(						lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Any				):
			return EarlyStopping(
			    monitor=f'''val_{metric}'''   ,  mode='min' if 'loss' in metric else 'max'   ,  patience=lowerCamelCase_   ,  verbose=lowerCamelCase_   ,  )






class 			_UpperCamelCase(       pl.Callback		):

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):



					'''simple docstring'''



					__a						:  List[str]									=     {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		:    str    ,       SCREAMING_SNAKE_CASE__		:    pl.Trainer    ,       SCREAMING_SNAKE_CASE__		:    pl.LightningModule    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Any=True    ):



					'''simple docstring'''



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

					if not save_generations:
								return

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

		@rank_zero_only
		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					try:
								__a						:  Optional[int]									=     pl_module.model.model.num_parameters()
					except AttributeError:
								__a						:  List[Any]									=     pl_module.model.num_parameters()

					__a						:  Dict									=     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		:    int    ,       SCREAMING_SNAKE_CASE__		:    pl.Trainer    ,       SCREAMING_SNAKE_CASE__		:    pl.LightningModule    ):



					'''simple docstring'''



					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		:    Any    ,       SCREAMING_SNAKE_CASE__		:    pl.Trainer    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''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")













"""simple docstring"""





from random import randint
from tempfile import TemporaryFile

import numpy as np



def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    Optional[int]  )    ->							int:
						__magic_name__:	Optional[int]							=       0
						if start < end:
												__magic_name__:	Union[str, Any]							=       randint(__UpperCAmelCase      ,   __UpperCAmelCase  )
												__magic_name__:	int							=       a[end]
												__magic_name__:	Optional[int]							=       a[pivot]
												__magic_name__:	Tuple							=       temp

												__magic_name__,			__magic_name__:	int							=       _in_place_partition(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
												count += _in_place_quick_sort(__UpperCAmelCase      ,   __UpperCAmelCase      ,   p - 1  )
												count += _in_place_quick_sort(__UpperCAmelCase      ,   p + 1      ,   __UpperCAmelCase  )
						return count



def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    List[Any]  )    ->							Union[str, Any]:
						__magic_name__:	Union[str, Any]							=       0
						__magic_name__:	str							=       randint(__UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       a[end]
						__magic_name__:	Optional[int]							=       a[pivot]
						__magic_name__:	Optional[int]							=       temp
						__magic_name__:	Dict							=       start - 1
						for index in range(__UpperCAmelCase      ,   __UpperCAmelCase  ):
												count += 1
												if a[index] < a[end]:  # check if current val is less than pivot value
																		__magic_name__:	List[Any]							=       new_pivot_index + 1
																		__magic_name__:	Any							=       a[new_pivot_index]
																		__magic_name__:	int							=       a[index]
																		__magic_name__:	Union[str, Any]							=       temp

						__magic_name__:	List[Any]							=       a[new_pivot_index + 1]
						__magic_name__:	Union[str, Any]							=       a[end]
						__magic_name__:	Dict							=       temp
						return new_pivot_index + 1, count


__lowerCamelCase							=  TemporaryFile()
__lowerCamelCase							=  1_00  # 1000 elements are to be sorted


__lowerCamelCase		,					__lowerCamelCase							=  0, 1  # mean and standard deviation
__lowerCamelCase							=  np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
__lowerCamelCase							=  np.load(outfile)
__lowerCamelCase							=  len(M) - 1
__lowerCamelCase							=  _in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)

print(z)



