



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 					__snake_case     (      _UpperCAmelCase    ):
    if "model" in orig_key:
        __a							=    orig_key.replace('''model.'''       ,			''''''    )
    if "norm1" in orig_key:
        __a							=    orig_key.replace('''norm1'''       ,			'''attention.output.LayerNorm'''    )
    if "norm2" in orig_key:
        __a							=    orig_key.replace('''norm2'''       ,			'''output.LayerNorm'''    )
    if "norm" in orig_key:
        __a							=    orig_key.replace('''norm'''       ,			'''LayerNorm'''    )
    if "transformer" in orig_key:
        __a							=    orig_key.split('''.'''    )[0].split('''_'''    )[-1]
        __a							=    orig_key.replace(f'transformer_{layer_num}'       ,			f'encoder.layer.{layer_num}'    )
    if "mha.attn" in orig_key:
        __a							=    orig_key.replace('''mha.attn'''       ,			'''attention.self'''    )
    if "mha" in orig_key:
        __a							=    orig_key.replace('''mha'''       ,			'''attention'''    )
    if "W_q" in orig_key:
        __a							=    orig_key.replace('''W_q'''       ,			'''self.query'''    )
    if "W_k" in orig_key:
        __a							=    orig_key.replace('''W_k'''       ,			'''self.key'''    )
    if "W_v" in orig_key:
        __a							=    orig_key.replace('''W_v'''       ,			'''self.value'''    )
    if "ff1" in orig_key:
        __a							=    orig_key.replace('''ff1'''       ,			'''intermediate.dense'''    )
    if "ff2" in orig_key:
        __a							=    orig_key.replace('''ff2'''       ,			'''output.dense'''    )
    if "ff" in orig_key:
        __a							=    orig_key.replace('''ff'''       ,			'''output.dense'''    )
    if "mlm_class" in orig_key:
        __a							=    orig_key.replace('''mlm.mlm_class'''       ,			'''cls.predictions.decoder'''    )
    if "mlm" in orig_key:
        __a							=    orig_key.replace('''mlm'''       ,			'''cls.predictions.transform'''    )
    if "cls" not in orig_key:
        __a							=    '''yoso.''' + orig_key

    return orig_key





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    for key in orig_state_dict.copy().keys():
        __a							=    orig_state_dict.pop(_UpperCAmelCase    )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __a							=    val

    __a							=    orig_state_dict['''cls.predictions.decoder.bias''']
    __a							=    torch.arange(_UpperCAmelCase    ).expand((1, -1)    ) + 2

    return orig_state_dict





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    torch.load(_UpperCAmelCase       ,			map_location='''cpu'''    )['''model_state_dict''']
    __a							=    YosoConfig.from_json_file(_UpperCAmelCase    )
    __a							=    YosoForMaskedLM(_UpperCAmelCase    )

    __a							=    convert_checkpoint_helper(config.max_position_embeddings       ,			_UpperCAmelCase    )

    print(model.load_state_dict(_UpperCAmelCase    )    )
    model.eval()
    model.save_pretrained(_UpperCAmelCase    )

    print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'    )


if __name__ == "__main__":
 __snake_case						:List[Any]						=					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The json file for YOSO model config.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 __snake_case						:List[Any]						=					parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










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.''')





