





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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       float:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						(1, 2, 1)
      SCREAMING_SNAKE_CASE		=						(1, 1, 0, 7)
      SCREAMING_SNAKE_CASE		=						SARIMAX(
          _SCREAMING_SNAKE_CASE			,						exog=_SCREAMING_SNAKE_CASE			,						order=_SCREAMING_SNAKE_CASE			,						seasonal_order=_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						model.fit(disp=_SCREAMING_SNAKE_CASE			,						maxiter=6_00			,						method="""nm"""     )
      SCREAMING_SNAKE_CASE		=						model_fit.predict(1			,						len(_SCREAMING_SNAKE_CASE     )			,						exog=[test_match]     )
      return result[0]



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       float:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       float:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						0
      for i in list_vote:
            if i > actual_result:
                  SCREAMING_SNAKE_CASE		=						not_safe + 1
            else:
                  if abs(abs(_SCREAMING_SNAKE_CASE     ) - abs(_SCREAMING_SNAKE_CASE     )     ) <= 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)
      SCREAMING_SNAKE_CASE_						       =	[[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
      SCREAMING_SNAKE_CASE_						       =	pd.DataFrame(
          data_input, columns=["""total_user""", """total_even""", """days"""]
      )
      SCREAMING_SNAKE_CASE_						       =	Normalizer().fit_transform(data_input_df.values)
      # split data
      SCREAMING_SNAKE_CASE_						       =	normalize_df[:, 2].tolist()
      SCREAMING_SNAKE_CASE_						       =	normalize_df[:, 0].tolist()
      SCREAMING_SNAKE_CASE_						       =	normalize_df[:, 1].tolist()

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

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

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

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









import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           if not self.initialized:
                 SCREAMING_SNAKE_CASE		=						RagRetriever(
                     lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						True






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           self.retriever.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.retriever._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return doc_ids, retrieved_doc_embeds

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Dict=None	)					->		Any:




           '''simple docstring'''


           if index is not None and index.is_initialized() and len(lowerCamelCase__	) > 0:
                 raise ValueError(
                     """When using Ray for distributed fine-tuning, """
                     """you'll need to provide the paths instead, """
                     """as the dataset and the index are loaded """
                     """separately. More info in examples/rag/use_own_knowledge_dataset.py """	)
           super().__init__(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						retrieval_workers
           if len(self.retrieval_workers	) > 0:
                 ray.get(
                     [
                         worker.create_rag_retriever.remote(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
                         for worker in self.retrieval_workers
                     ]	)






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




           '''simple docstring'''


           logger.info("""initializing retrieval"""	)

           if len(self.retrieval_workers	) > 0:
                 ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]	)
           else:
                 # Non-distributed training. Load index into this same process.
                 self.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   int	)					->		Dict:




           '''simple docstring'''


           if len(self.retrieval_workers	) > 0:
                 # Select a random retrieval actor.
                 SCREAMING_SNAKE_CASE		=						self.retrieval_workers[random.randint(0						,len(self.retrieval_workers	) - 1	)]
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						ray.get(random_worker.retrieve.remote(lowerCamelCase__						,lowerCamelCase__	)	)
           else:
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]=None						,**lowerCamelCase__						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           return super(lowerCamelCase__						,cls	).get_tokenizers(lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]=None						,**lowerCamelCase__						:   Any	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.pop("""config"""						,lowerCamelCase__	) or RagConfig.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RagTokenizer.from_pretrained(lowerCamelCase__						,config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.question_encoder
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.generator
           if indexed_dataset is not None:
                 SCREAMING_SNAKE_CASE		=						"""custom"""
                 SCREAMING_SNAKE_CASE		=						CustomHFIndex(config.retrieval_vector_size						,lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						cls._build_index(lowerCamelCase__	)
           return cls(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,retrieval_workers=lowerCamelCase__						,index=lowerCamelCase__						,)


