



"""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   :      str     ):


						'''simple docstring'''






						snake_case_ :			List[str]            =      filter(lambda __UpperCamelCase     : p.requires_grad       ,			model.parameters()     )
						snake_case_ :			List[str]            =      sum([np.prod(p.size()     ) for p in model_parameters]     )
						return params


__lowerCAmelCase							:       Tuple          =       logging.getLogger(__name__)
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Optional[Any]       ,			__UpperCamelCase   :      Optional[Any]     ):


						'''simple docstring'''






						if metric == "rouge2":
												snake_case_ :			List[str]            =      """{val_avg_rouge2:.4f}-{step_count}"""
						elif metric == "bleu":
												snake_case_ :			Optional[int]            =      """{val_avg_bleu:.4f}-{step_count}"""
						elif metric == "em":
												snake_case_ :			Union[str, Any]            =      """{val_avg_em:.4f}-{step_count}"""
						elif metric == "loss":
												snake_case_ :			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."""     )

						snake_case_ :			Union[str, Any]            =      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   :      int     ):


						'''simple docstring'''






						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  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      {f'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
								pl_module.logger.log_metrics(_lowercase )





		@rank_zero_only
		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase=True )			->    None:







								'''simple docstring'''

								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_ :			List[str]            =      Path(pl_module.hparams.output_dir )
								if type_path == "test":
														snake_case_ :			Dict            =      od / """test_results.txt"""
														snake_case_ :			Dict            =      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_ :			str            =      od / f'{type_path}_results/{trainer.global_step:05d}.txt'
														snake_case_ :			Tuple            =      od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
														results_file.parent.mkdir(exist_ok=_lowercase )
														generations_file.parent.mkdir(exist_ok=_lowercase )
								with open(_lowercase				,     """a+""" ) as writer:
														for key in sorted(_lowercase ):
																				if key in ["log", "progress_bar", "preds"]:
																										continue
																				snake_case_ :			int            =      metrics[key]
																				if isinstance(_lowercase				,     torch.Tensor ):
																										snake_case_ :			Optional[Any]            =      val.item()
																				snake_case_ :			int            =      f'{key}: {val:.6f}\n'
																				writer.write(_lowercase )

								if not save_generations:
														return

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





		@rank_zero_only
		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase )			->    Optional[Any]:







								'''simple docstring'''

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

								snake_case_ :			str            =      count_trainable_parameters(_lowercase )
								# 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 )			->    List[str]:







								'''simple docstring'''

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





		@rank_zero_only
		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase )			->    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")






'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

snake_case_					:      Optional[Any]    			=    TypeVar('KEY')
snake_case_					:      Dict    			=    TypeVar('VAL')






@dataclass(frozen=lowercase		,   slots=lowercase  )
class 				lowercase__      (				Generic[KEY, VAL]  ):
	lowercase__    							=     42
	lowercase__    							=     42






class 				lowercase__      (				_Item  ):





	def __init__(					self   :							Union[str, Any]					):







							'''simple docstring'''
							super().__init__(lowerCamelCase__					,lowerCamelCase__					)





	def __bool__(					self   :							Tuple					):







							'''simple docstring'''
							return False
snake_case_					:      int    			=    _DeletedItem()






class 				lowercase__      (				MutableMapping[KEY, VAL]  ):





	def __init__(					self   :							Any					,lowerCamelCase__   :							int = 8					,lowerCamelCase__   :							float = 0.7_5					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  initial_block_size
							_UpperCamelCase   :						list[_Item | None]												=  [None] * initial_block_size
							assert 0.0 < capacity_factor < 1.0
							_UpperCamelCase   :						Optional[int]												=  capacity_factor
							_UpperCamelCase   :						List[Any]												=  0





	def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							KEY					):







							'''simple docstring'''
							return hash(lowerCamelCase__					) % len(self._buckets					)





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							return (ind + 1) % len(self._buckets					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							int					,lowerCamelCase__   :							KEY					,lowerCamelCase__   :							VAL					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  self._buckets[ind]
							if not stored:
													_UpperCamelCase   :						str												=  _Item(lowerCamelCase__					,lowerCamelCase__					)
													self._len += 1
													return True
							elif stored.key == key:
													_UpperCamelCase   :						Union[str, Any]												=  _Item(lowerCamelCase__					,lowerCamelCase__					)
													return True
							else:
													return False





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  len(self._buckets					) * self._capacity_factor
							return len(self					) >= int(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							if len(self._buckets					) <= self._initial_block_size:
													return False
							_UpperCamelCase   :						str												=  len(self._buckets					) * self._capacity_factor / 2
							return len(self					) < limit





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  self._buckets
							_UpperCamelCase   :						Dict												=  [None] * new_size
							_UpperCamelCase   :						str												=  0
							for item in old_buckets:
													if item:
																			self._add_item(item.key					,item.val					)





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							self._resize(len(self._buckets					) * 2					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							self._resize(len(self._buckets					) // 2					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							KEY					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  self._get_bucket_index(lowerCamelCase__					)
							for _ in range(len(self._buckets					)					):
													yield ind
													_UpperCamelCase   :						Optional[int]												=  self._get_next_ind(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							KEY					,lowerCamelCase__   :							VAL					):







							'''simple docstring'''
							for ind in self._iterate_buckets(lowerCamelCase__					):
													if self._try_set(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					):
																			break





	def __setitem__(					self   :							Dict					,lowerCamelCase__   :							KEY					,lowerCamelCase__   :							VAL					):







							'''simple docstring'''
							if self._is_full():
													self._size_up()

							self._add_item(lowerCamelCase__					,lowerCamelCase__					)





	def __delitem__(					self   :							Optional[Any]					,lowerCamelCase__   :							KEY					):







							'''simple docstring'''
							for ind in self._iterate_buckets(lowerCamelCase__					):
													_UpperCamelCase   :						Any												=  self._buckets[ind]
													if item is None:
																			raise KeyError(lowerCamelCase__					)
													if item is _deleted:
																			continue
													if item.key == key:
																			_UpperCamelCase   :						Optional[Any]												=  _deleted
																			self._len -= 1
																			break
							if self._is_sparse():
													self._size_down()





	def __getitem__(					self   :							Dict					,lowerCamelCase__   :							KEY					):







							'''simple docstring'''
							for ind in self._iterate_buckets(lowerCamelCase__					):
													_UpperCamelCase   :						Any												=  self._buckets[ind]
													if item is None:
																			break
													if item is _deleted:
																			continue
													if item.key == key:
																			return item.val
							raise KeyError(lowerCamelCase__					)





	def __len__(					self   :							Dict					):







							'''simple docstring'''
							return self._len





	def __iter__(					self   :							List[str]					):







							'''simple docstring'''
							yield from (item.key for item in self._buckets if item)





	def __repr__(					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  ' ,'.join(
							    F'{item.key}: {item.val}' for item in self._buckets if item					)
							return F'HashMap({val_string})'


