



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
 from sagemaker import Session, TrainingJobAnalytics
 from sagemaker.huggingface import HuggingFace

@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''				,'''False'''   )   ) is not True				,reason='''Skipping test because should only be run when releasing minor transformers version'''				,)
@pytest.mark.usefixtures('''sm_env'''   )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]   )
class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           if self.framework == "pytorch":
               subprocess.run(
                   F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split()					,		encoding='''utf-8'''					,		check=__SCREAMING_SNAKE_CASE					,		)
           assert hasattr(self					,		'''env''')



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=1):


           '''simple docstring'''

           return HuggingFace(
               entry_point=self.script					,		source_dir=self.env.test_path					,		role=self.env.role					,		image_uri=self.env.image_uri					,		base_job_name=F'{self.env.base_job_name}-single'					,		instance_count=__SCREAMING_SNAKE_CASE					,		instance_type=self.instance_type					,		debugger_hook_config=__SCREAMING_SNAKE_CASE					,		hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,		metric_definitions=self.env.metric_definitions					,		py_version='''py36'''					,		)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           TrainingJobAnalytics(__SCREAMING_SNAKE_CASE).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv')





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    self.create_estimator()

           # run training
           estimator.fit()

           # result dataframe
           __a							=    TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

           # extract kpis
           __a							=    list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
           __a							=    list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
           # get train time from SageMaker job, this includes starting, preprocessing, stopping
           __a							=    (
               Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''					,		999_999)
           )

           # assert kpis
           assert train_runtime <= self.results["train_runtime"]
           assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy)
           assert all(t <= self.results['''eval_loss'''] for t in eval_loss)

           # dump tests result into json file to share in PR
           with open(F'{estimator.latest_training_job.name}.json'					,		'''w''') as outfile:
               json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}					,		__SCREAMING_SNAKE_CASE)










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.''')





