





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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=(1, 2, 1)
   UpperCAmelCase__						=(1, 1, 0, 7)
   UpperCAmelCase__						=SARIMAX(
       A				,							exog=A				,							order=A				,							seasonal_order=A     )
   UpperCAmelCase__						=model.fit(disp=A				,							maxiter=600				,							method="nm"     )
   UpperCAmelCase__						=model_fit.predict(1				,							len(A     )				,							exog=[test_match]     )
   return result[0]






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=SVR(kernel="rbf"				,							C=1				,							gamma=0.1				,							epsilon=0.1     )
   regressor.fit(A				,							A     )
   UpperCAmelCase__						=regressor.predict(A     )
   return y_pred[0]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   train_user.sort()
   UpperCAmelCase__						=np.percentile(A				,							25     )
   UpperCAmelCase__						=np.percentile(A				,							75     )
   UpperCAmelCase__						=qa - qa
   UpperCAmelCase__						=qa - (iqr * 0.1)
   return low_lim






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=0
   UpperCAmelCase__						=0
   for i in list_vote:
      if i > actual_result:
         UpperCAmelCase__						=not_safe + 1
      else:
         if abs(abs(A     ) - abs(A     )     ) <= 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)
    UpperCamelCase_												=	[[1_82_31, 0.0, 1], [2_26_21, 1.0, 2], [1_56_75, 0.0, 3], [2_35_83, 1.0, 4]]
    UpperCamelCase_												=	pd.DataFrame(
        data_input, columns=['total_user', 'total_even', 'days']
    )
    UpperCamelCase_												=	Normalizer().fit_transform(data_input_df.values)
    # split data
    UpperCamelCase_												=	normalize_df[:, 2].tolist()
    UpperCamelCase_												=	normalize_df[:, 0].tolist()
    UpperCamelCase_												=	normalize_df[:, 1].tolist()

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

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

    UpperCamelCase_												=	total_date[len(total_date) - 1 :]
    UpperCamelCase_												=	total_user[len(total_user) - 1 :]
    UpperCamelCase_												=	total_match[len(total_match) - 1 :]

    # voting system with forecasting
    UpperCamelCase_												=	[
        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
    UpperCamelCase_												=	'' if data_safety_checker(res_vote, tst_user) else 'not '
    print('Today\'s data is {not_str}safe.')










import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A=1024     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=[], []

   UpperCAmelCase__						=list(zip(A				,							A     )     )
   UpperCAmelCase__			,							UpperCAmelCase__						=sorted_examples[0]

   def is_too_big(A     ):
      return tok(A				,							return_tensors="pt"     ).input_ids.shape[1] > max_tokens

   for src, tgt in tqdm(sorted_examples[1:]     ):
      UpperCAmelCase__						=new_src + " " + src
      UpperCAmelCase__						=new_tgt + " " + tgt
      if is_too_big(A     ) or is_too_big(A     ):  # cant fit, finalize example
         finished_src.append(A     )
         finished_tgt.append(A     )
         UpperCAmelCase__			,							UpperCAmelCase__						=src, tgt
      else:  # can fit, keep adding
         UpperCAmelCase__			,							UpperCAmelCase__						=cand_src, cand_tgt

    # cleanup
   if new_src:
      assert new_tgt
      finished_src.append(A     )
      finished_tgt.append(A     )
   return finished_src, finished_tgt






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Path(A     )
   save_path.mkdir(exist_ok=A     )
   for split in ["train"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__			,							UpperCAmelCase__						=pack_examples(A				,							A				,							A				,							A     )
      print(F"""packed {split} split from {len(A     )} examples -> {len(A     )}."""     )
      Path(save_path / F"""{split}.source"""     ).open("w"     ).write("\n".join(A     )     )
      Path(save_path / F"""{split}.target"""     ).open("w"     ).write("\n".join(A     )     )
   for split in ["val", "test"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      shutil.copyfile(A				,							save_path / F"""{split}.source"""     )
      shutil.copyfile(A				,							save_path / F"""{split}.target"""     )






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=argparse.ArgumentParser()
   parser.add_argument("--tok_name"				,							type=A				,							help="like facebook/bart-large-cnn,t5-base, etc."     )
   parser.add_argument("--max_seq_len"				,							type=A				,							default=128     )
   parser.add_argument("--data_dir"				,							type=A     )
   parser.add_argument("--save_path"				,							type=A     )
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						=AutoTokenizer.from_pretrained(args.tok_name     )
   return pack_data_dir(A				,							Path(args.data_dir     )				,							args.max_seq_len				,							args.save_path     )


if __name__ == "__main__":
    packer_cli()



