



"""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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):
					snake_case_					:		Optional[int] 					=					filter(lambda SCREAMING_SNAKE_CASE__			: p.requires_grad				,    model.parameters()			)
					snake_case_					:		int 					=					sum([np.prod(p.size()			) for p in model_parameters]			)
					return params


a_			   =					logging.getLogger(__name__)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]			):
					if metric == "rouge2":
										snake_case_					:		Tuple 					=					"""{val_avg_rouge2:.4f}-{step_count}"""
					elif metric == "bleu":
										snake_case_					:		int 					=					"""{val_avg_bleu:.4f}-{step_count}"""
					elif metric == "em":
										snake_case_					:		Optional[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."""			)

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]			):
					return EarlyStopping(
					    monitor=f'val_{metric}'				,    mode="""min""" if """loss""" in metric else """max"""				,    patience=__snake_case				,    verbose=__snake_case				,    )







class    __lowercase     (					pl.Callback):







						"""simple docstring"""







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

											if not save_generations:
																return

											if "preds" in metrics:
																snake_case_					:		Union[str, Any] 					=					"""\n""".join(metrics["""preds"""]       )
																generations_file.open("""w+"""       ).write(_A       )







						@rank_zero_only
						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											try:
																snake_case_					:		Optional[Any] 					=					pl_module.model.model.num_parameters()
											except AttributeError:
																snake_case_					:		List[Any] 					=					pl_module.model.num_parameters()

											snake_case_					:		Dict 					=					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 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											save_json(pl_module.metrics   ,		pl_module.metrics_save_path       )
											return self._write_logs(_A   ,		_A   ,		"""test"""       )







						@rank_zero_only
						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											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 copy import deepcopy







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__ = None   ,		lowercase__ = None       ):

											if arr is None and size is not None:
																snake_case_					:		str 					=					size
																snake_case_					:		Optional[Any] 					=					[0] * size
											elif arr is not None:
																self.init(lowercase__       )
											else:
																raise ValueError("""Either arr or size must be specified"""       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Optional[Any] 					=					len(lowercase__       )
											snake_case_					:		int 					=					deepcopy(lowercase__       )
											for i in range(1   ,		self.size       ):
																snake_case_					:		Optional[Any] 					=					self.next_(lowercase__       )
																if j < self.size:
																					self.tree[j] += self.tree[i]







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					self.tree[:]
											for i in range(self.size - 1   ,		0   ,		-1       ):
																snake_case_					:		Optional[int] 					=					self.next_(lowercase__       )
																if j < self.size:
																					arr[j] -= arr[i]
											return arr







						@staticmethod
						def 						__UpperCamelCase							(lowercase__       ):
											return index + (index & (-index))







						@staticmethod
						def 						__UpperCamelCase							(lowercase__       ):
											return index - (index & (-index))







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if index == 0:
																self.tree[0] += value
																return
											while index < self.size:
																self.tree[index] += value
																snake_case_					:		Tuple 					=					self.next_(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											self.add(lowercase__   ,		value - self.get(lowercase__       )       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if right == 0:
																return 0
											snake_case_					:		List[str] 					=					self.tree[0]
											right -= 1  # make right inclusive
											while right > 0:
																result += self.tree[right]
																snake_case_					:		Optional[int] 					=					self.prev(lowercase__       )
											return result







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											return self.prefix(lowercase__       ) - self.prefix(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											return self.query(lowercase__   ,		index + 1       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											value -= self.tree[0]
											if value < 0:
																return -1

											snake_case_					:		Tuple 					=					1  # Largest power of 2 <= size
											while j * 2 < self.size:
																j *= 2

											snake_case_					:		Tuple 					=					0

											while j > 0:
																if i + j < self.size and self.tree[i + j] <= value:
																					value -= self.tree[i + j]
																					i += j
																j //= 2
											return i


if __name__ == "__main__":
			import doctest

			doctest.testmod()




