


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 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad						,				model.parameters()     )
		lowercase__            =      sum([np.prod(p.size()     ) for p in model_parameters]     )
		return params


lowerCAmelCase       =							logging.getLogger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if metric == "rouge2":
				lowercase__            =      '''{val_avg_rouge2:.4f}-{step_count}'''
		elif metric == "bleu":
				lowercase__            =      '''{val_avg_bleu:.4f}-{step_count}'''
		elif metric == "em":
				lowercase__            =      '''{val_avg_em:.4f}-{step_count}'''
		elif metric == "loss":
				lowercase__            =      '''{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.'''     )

		lowercase__            =      ModelCheckpoint(
		    dirpath=SCREAMING_SNAKE_CASE						,				filename=SCREAMING_SNAKE_CASE						,				monitor=f'val_{metric}'						,				mode='''max'''						,				save_top_k=1						,				every_n_epochs=1						,				)

		return checkpoint_callback
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		return EarlyStopping(
		    monitor=f'val_{metric}'						,				mode='''min''' if '''loss''' in metric else '''max'''						,				patience=SCREAMING_SNAKE_CASE						,				verbose=SCREAMING_SNAKE_CASE						,				)


class        _a							(			pl.Callback			):

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Union[str, Any]							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      {f'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
									pl_module.logger.log_metrics(UpperCamelCase_							)

							@rank_zero_only
							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	pl.Trainer    ,						UpperCamelCase_:	pl.LightningModule    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	Optional[int]=True							)			->   None:





									"""simple docstring"""







									logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'							)
									lowercase__            =      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
									lowercase__            =      Path(pl_module.hparams.output_dir							)
									if type_path == "test":
											lowercase__            =      od / '''test_results.txt'''
											lowercase__            =      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.
											lowercase__            =      od / f'{type_path}_results/{trainer.global_step:05d}.txt'
											lowercase__            =      od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
											results_file.parent.mkdir(exist_ok=UpperCamelCase_							)
											generations_file.parent.mkdir(exist_ok=UpperCamelCase_							)
									with open(UpperCamelCase_    ,						'''a+'''							) as writer:
											for key in sorted(UpperCamelCase_							):
													if key in ["log", "progress_bar", "preds"]:
															continue
													lowercase__            =      metrics[key]
													if isinstance(UpperCamelCase_    ,						torch.Tensor							):
															lowercase__            =      val.item()
													lowercase__            =      f'{key}: {val:.6f}\n'
													writer.write(UpperCamelCase_							)

									if not save_generations:
											return

									if "preds" in metrics:
											lowercase__            =      '''\n'''.join(metrics['''preds''']							)
											generations_file.open('''w+'''							).write(UpperCamelCase_							)

							@rank_zero_only
							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	Tuple							)			->   Optional[int]:





									"""simple docstring"""







									try:
											lowercase__            =      pl_module.model.model.num_parameters()
									except AttributeError:
											lowercase__            =      pl_module.model.num_parameters()

									lowercase__            =      count_trainable_parameters(UpperCamelCase_							)
									# 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:	str    ,						UpperCamelCase_:	pl.Trainer    ,						UpperCamelCase_:	pl.LightningModule							)			->   Tuple:





									"""simple docstring"""







									save_json(pl_module.metrics    ,						pl_module.metrics_save_path							)
									return self._write_logs(UpperCamelCase_    ,						UpperCamelCase_    ,						'''test'''							)


							@rank_zero_only
							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	pl.Trainer    ,						UpperCamelCase_:	List[str]							)			->   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")






'''simple docstring'''
import csv

import tweepy

# Twitter API credentials
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''


def 			a      (			__a   )  ->	None:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= tweepy.OAuthHandler(__a ,					__a   )
					auth.set_access_token(__a ,					__a   )
					UpperCamelCase__   :List[str]												= tweepy.API(__a   )

					# initialize a list to hold all the tweepy Tweets
					UpperCamelCase__   :Dict												= []

					# make initial request for most recent tweets (200 is the maximum allowed count)
					UpperCamelCase__   :Tuple												= api.user_timeline(screen_name=__a ,					count=200   )

					# save most recent tweets
					alltweets.extend(__a   )

					# save the id of the oldest tweet less one
					UpperCamelCase__   :Union[str, Any]												= alltweets[-1].id - 1

					# keep grabbing tweets until there are no tweets left to grab
					while len(__a   ) > 0:
										print(f'''getting tweets before {oldest}'''   )

										# all subsequent requests use the max_id param to prevent duplicates
										UpperCamelCase__   :Union[str, Any]												= api.user_timeline(
										    screen_name=__a ,					count=200 ,					max_id=__a   )

										# save most recent tweets
										alltweets.extend(__a   )

										# update the id of the oldest tweet less one
										UpperCamelCase__   :Tuple												= alltweets[-1].id - 1

										print(f'''...{len(__a   )} tweets downloaded so far'''   )

					# transform the tweepy tweets into a 2D array that will populate the csv
					UpperCamelCase__   :int												= [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

					# write the csv
					with open(f'''new_{screen_name}_tweets.csv''' ,					'''w'''   ) as f:
										UpperCamelCase__   :Tuple												= csv.writer(__a   )
										writer.writerow(['''id''', '''created_at''', '''text''']   )
										writer.writerows(__a   )


if __name__ == "__main__":
		# pass in the username of the account you want to download
		get_all_tweets('''FirePing32''')