
"""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 lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   filter(lambda _snake_case						: p.requires_grad   ,model.parameters()						)
							SCREAMING_SNAKE_CASE__						:       int           =   sum([np.prod(p.size()						) for p in model_parameters]						)
							return params


UpperCAmelCase__      :    str    							= logging.getLogger(__name__)

def lowercase_  (   _snake_case   ,_snake_case						):





							if metric == "rouge2":
														SCREAMING_SNAKE_CASE__						:       Any           =   '''{val_avg_rouge2:.4f}-{step_count}'''
							elif metric == "bleu":
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   '''{val_avg_bleu:.4f}-{step_count}'''
							elif metric == "em":
														SCREAMING_SNAKE_CASE__						:       List[str]           =   '''{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."""						)

							SCREAMING_SNAKE_CASE__						:       List[Any]           =   ModelCheckpoint(
							    dirpath=_UpperCAmelCase   ,filename=_UpperCAmelCase   ,monitor=f'''val_{metric}'''   ,mode="""max"""   ,save_top_k=3   ,every_n_epochs=1   ,)
							return checkpoint_callback

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



class 	lowerCAmelCase_						(pl.Callback  ):


				"""simple docstring"""



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   {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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=True    )					->		Tuple:



											"""simple docstring"""




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

											if not save_generations:
																		return

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



				@rank_zero_only
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											try:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   pl_module.model.model.num_parameters()
											except AttributeError:
																		SCREAMING_SNAKE_CASE__						:       Any           =   pl_module.model.num_parameters()

											SCREAMING_SNAKE_CASE__						:       int           =   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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




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



				@rank_zero_only
				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""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        a						(      _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	0
		__UpperCAmelCase    :     str													=	len(_UpperCAmelCase						)

		for i in range(n - 1						):
				for j in range(i + 1 ,   _UpperCAmelCase						):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions

def        a						(      _UpperCAmelCase							:					str						):







		'''simple docstring'''





		if len(_UpperCAmelCase						) <= 1:
				return arr, 0
		__UpperCAmelCase    :     Dict													=	len(_UpperCAmelCase						) // 2
		__UpperCAmelCase    :     Union[str, Any]													=	arr[0:mid]
		__UpperCAmelCase    :     Optional[Any]													=	arr[mid:]

		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	_count_cross_inversions(_UpperCAmelCase ,   _UpperCAmelCase						)

		__UpperCAmelCase    :     List[str]													=	inversion_p + inversions_q + cross_inversions
		return c, num_inversions

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Any													=	0
		while i < len(_UpperCAmelCase						) and j < len(_UpperCAmelCase						):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(_UpperCAmelCase						) - i
						r.append(q[j]						)
						j += 1
				else:
						r.append(p[i]						)
						i += 1

		if i < len(_UpperCAmelCase						):
				r.extend(p[i:]						)
		else:
				r.extend(q[j:]						)

		return r, num_inversion

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[10, 2, 1, 5, 5, 2, 11]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		__UpperCAmelCase    :     List[str]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = ''' ,   _UpperCAmelCase						)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		__UpperCAmelCase    :     Any													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = ''' ,   _UpperCAmelCase						)

		# an empty list should also have zero inversions
		__UpperCAmelCase    :     Tuple													=	[]
		__UpperCAmelCase    :     Union[str, Any]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = ''' ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()





