






'''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 		_UpperCAmelCase   (							__A							:   Optional[int]      ):
				a_		:  Union[str, Any]											=      filter(lambda __A      : p.requires_grad						,		model.parameters()      )
				a_		:  List[str]											=      sum([np.prod(p.size()      ) for p in model_parameters]      )
				return params


__lowerCAmelCase       		=      logging.getLogger(__name__)





def 		_UpperCAmelCase   (							__A							:   Union[str, Any]						,		__A							:   Any      ):



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

				a_		:  str											=      ModelCheckpoint(
				    dirpath=lowerCamelCase_						,		filename=lowerCamelCase_						,		monitor=f'val_{metric}'						,		mode='''max'''						,		save_top_k=3						,		every_n_epochs=1						,		)
				return checkpoint_callback





def 		_UpperCAmelCase   (							__A							:   Any						,		__A							:   Optional[Any]      ):
				return EarlyStopping(
				    monitor=f'val_{metric}'						,		mode='''min''' if '''loss''' in metric else '''max'''						,		patience=lowerCamelCase_						,		verbose=lowerCamelCase_						,		)





class 	SCREAMING_SNAKE_CASE					(					pl.Callback       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			Tuple:
							a_		:  Tuple											=      {f'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
							pl_module.logger.log_metrics(lowerCamelCase_					)






			@rank_zero_only
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			pl.Trainer  ,						__SCREAMING_SNAKE_CASE      :			pl.LightningModule  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			List[str]=True					)			->			int:
							logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'					)
							a_		:  Union[str, 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
							a_		:  Union[str, Any]											=      Path(pl_module.hparams.output_dir					)
							if type_path == "test":
											a_		:  Optional[Any]											=      od / """test_results.txt"""
											a_		:  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.
											a_		:  Optional[int]											=      od / f'{type_path}_results/{trainer.global_step:05d}.txt'
											a_		:  int											=      od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
											results_file.parent.mkdir(exist_ok=lowerCamelCase_					)
											generations_file.parent.mkdir(exist_ok=lowerCamelCase_					)
							with open(lowerCamelCase_  ,						'''a+'''					) as writer:
											for key in sorted(lowerCamelCase_					):
															if key in ["log", "progress_bar", "preds"]:
																			continue
															a_		:  Any											=      metrics[key]
															if isinstance(lowerCamelCase_  ,						torch.Tensor					):
																			a_		:  Optional[int]											=      val.item()
															a_		:  Union[str, Any]											=      f'{key}: {val:.6f}\n'
															writer.write(lowerCamelCase_					)

							if not save_generations:
											return

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






			@rank_zero_only
			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Any					)			->			Optional[int]:



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

							a_		:  str											=      count_trainable_parameters(lowerCamelCase_					)
							# 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 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			pl.Trainer  ,						__SCREAMING_SNAKE_CASE      :			pl.LightningModule					)			->			Union[str, Any]:
							save_json(pl_module.metrics  ,						pl_module.metrics_save_path					)
							return self._write_logs(lowerCamelCase_  ,						lowerCamelCase_  ,						'''test'''					)







			@rank_zero_only
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			pl.Trainer  ,						__SCREAMING_SNAKE_CASE      :			Any					)			->			List[str]:
							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'''







import requests
from bsa import BeautifulSoup





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   dict      ):
				a_		:  Tuple											=      BeautifulSoup(requests.get(__A						,		params=__A      ).content						,		'''html.parser'''      )
				a_		:  List[str]											=      soup.find('''div'''						,		attrs={'''class''': '''gs_ri'''}      )
				a_		:  List[str]											=      div.find('''div'''						,		attrs={'''class''': '''gs_fl'''}      ).find_all('''a'''      )
				return anchors[2].get_text()


if __name__ == "__main__":
		__lowerCAmelCase       		=      {
		    'title': (
		        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
		        'capacitance, volumetric capacitance, and energy density'
		    ),
		    'journal': 'Chem. Mater.',
		    'volume': 30,
		    'pages': '3979-3990',
		    'year': 2_018,
		    'hl': 'en',
		}
		print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




