



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


__snake_case						:Dict						=					logging.getLogger(__name__)





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    if metric == "rouge2":
        __a							=    '''{val_avg_rouge2:.4f}-{step_count}'''
    elif metric == "bleu":
        __a							=    '''{val_avg_bleu:.4f}-{step_count}'''
    elif metric == "em":
        __a							=    '''{val_avg_em:.4f}-{step_count}'''
    elif metric == "loss":
        __a							=    '''{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.'''    )

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

    return checkpoint_callback





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

class    _A	(     pl.Callback   ):



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

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



       @rank_zero_only
       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					pl.Trainer					,		__SCREAMING_SNAKE_CASE							:					pl.LightningModule					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple=True):


           '''simple docstring'''

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

           if not save_generations:
               return

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



       @rank_zero_only
       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

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

           __a							=    count_trainable_parameters(__SCREAMING_SNAKE_CASE)
           # 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							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					pl.Trainer					,		__SCREAMING_SNAKE_CASE							:					pl.LightningModule):


           '''simple docstring'''

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





       @rank_zero_only
       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					pl.Trainer					,		__SCREAMING_SNAKE_CASE							:					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")









import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase    )]    )
    __a							=    np.array(_UpperCAmelCase    )
    __a							=    np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()       ,			_UpperCAmelCase    )    )       ,			x.transpose()    )       ,			_UpperCAmelCase    )
    return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    (1, 2, 1)
    __a							=    (1, 1, 0, 7)
    __a							=    SARIMAX(
        _UpperCAmelCase       ,			exog=_UpperCAmelCase       ,			order=_UpperCAmelCase       ,			seasonal_order=_UpperCAmelCase    )
    __a							=    model.fit(disp=_UpperCAmelCase       ,			maxiter=600       ,			method='''nm'''    )
    __a							=    model_fit.predict(1       ,			len(_UpperCAmelCase    )       ,			exog=[test_match]    )
    return result[0]





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    SVR(kernel='''rbf'''       ,			C=1       ,			gamma=0.1       ,			epsilon=0.1    )
    regressor.fit(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    regressor.predict(_UpperCAmelCase    )
    return y_pred[0]





def 					__snake_case     (      _UpperCAmelCase    ):
    train_user.sort()
    __a							=    np.percentile(_UpperCAmelCase       ,			25    )
    __a							=    np.percentile(_UpperCAmelCase       ,			75    )
    __a							=    qa - qa
    __a							=    qa - (iqr * 0.1)
    return low_lim





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    0
    __a							=    0
    for i in list_vote:
        if i > actual_result:
            __a							=    not_safe + 1
        else:
            if abs(abs(_UpperCAmelCase    ) - abs(_UpperCAmelCase    )    ) <= 0.1:
                safe += 1
            else:
                not_safe += 1
    return safe > not_safe


if __name__ == "__main__":
 # data_input_df = pd.read_csv("ex_data.csv", header=None)
 __snake_case						:Any						=					[[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
 __snake_case						:List[Any]						=					pd.DataFrame(
     data_input, columns=['''total_user''', '''total_even''', '''days''']
 )
 __snake_case						:Dict						=					Normalizer().fit_transform(data_input_df.values)
 # split data
 __snake_case						:Any						=					normalize_df[:, 2].tolist()
 __snake_case						:Tuple						=					normalize_df[:, 0].tolist()
 __snake_case						:Union[str, Any]						=					normalize_df[:, 1].tolist()

 # for svr (input variable = total date and total match)
 __snake_case						:Tuple						=					normalize_df[:, [1, 2]].tolist()
 __snake_case						:List[str]						=					x[: len(x) - 1]
 __snake_case						:int						=					x[len(x) - 1 :]

 # for linear regression & sarimax
 __snake_case						:int						=					total_date[: len(total_date) - 1]
 __snake_case						:Dict						=					total_user[: len(total_user) - 1]
 __snake_case						:int						=					total_match[: len(total_match) - 1]

 __snake_case						:List[Any]						=					total_date[len(total_date) - 1 :]
 __snake_case						:Tuple						=					total_user[len(total_user) - 1 :]
 __snake_case						:List[str]						=					total_match[len(total_match) - 1 :]

 # voting system with forecasting
 __snake_case						:Optional[Any]						=					[
     linear_regression_prediction(
         trn_date, trn_user, trn_match, tst_date, tst_match
     ),
     sarimax_predictor(trn_user, trn_match, tst_match),
     support_vector_regressor(x_train, x_test, trn_user),
 ]

 # check the safety of today's data
 __snake_case						:Union[str, Any]						=					'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
 print('''Today\'s data is {not_str}safe.''')





