

'''simple docstring'''


import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


lowerCAmelCase__						:       List[str]										=						logging.getLogger(__name__)




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      Any			,			snake_case_					:      List[str]			,			snake_case_					:      List[str]			,			snake_case_					:      int			,			snake_case_					:      Optional[Any]=None  ):



								'''simple docstring'''



								super().__init__(
								    UpperCamelCase_			,			question_encoder_tokenizer=UpperCamelCase_			,			generator_tokenizer=UpperCamelCase_			,			index=UpperCamelCase_			,			init_retrieval=UpperCamelCase_			,			)
								snake_case__						:       Optional[int]       						=  None







						def       __magic_name__							(							self					:      str			,			snake_case_					:      int  ):



								'''simple docstring'''



								logger.info('''initializing retrieval'''  )

								# initializing a separate process group for retrieval as the default
								# nccl backend doesn't support gather/scatter operations while gloo
								# is too slow to replace nccl for the core gpu communication
								if dist.is_initialized():
										logger.info('''dist initialized'''  )
										# needs to be set manually
										snake_case__						:       int       						=  self._infer_socket_ifname()
										# avoid clash with the NCCL port
										snake_case__						:       Union[str, Any]       						=  str(distributed_port + 1  )
										snake_case__						:       Optional[int]       						=  dist.new_group(ranks=UpperCamelCase_			,			backend='''gloo'''  )

								# initialize retriever only on the main worker
								if not dist.is_initialized() or self._is_main():
										logger.info('''dist not initialized / main'''  )
										self.index.init_index()

								# all processes wait untill the retriever is initialized by the main process
								if dist.is_initialized():
										torch.distributed.barrier(group=self.process_group  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								return dist.get_rank(group=self.process_group  ) == 0







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      int			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[Any]=torch.floataa  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  torch.empty(UpperCamelCase_			,			dtype=UpperCamelCase_  )
								dist.scatter(UpperCamelCase_			,			src=0			,			scatter_list=UpperCamelCase_			,			group=self.process_group  )
								return target_tensor







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  psutil.net_if_addrs()
								# a hacky way to deal with varying network interface names
								snake_case__						:       List[str]       						=  next((addr for addr in addrs if addr.startswith('''e'''  ))			,			UpperCamelCase_  )
								return ifname







						def       __magic_name__							(							self					:      str			,			snake_case_					:      np.ndarray			,			snake_case_					:      int  ):



								'''simple docstring'''



								if not dist.is_initialized():
										snake_case__						:       Optional[Any]       						=  self._main_retrieve(UpperCamelCase_			,			UpperCamelCase_  )
										return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_  )

								# distributed training
								snake_case__						:       int       						=  dist.get_world_size(group=self.process_group  )

								# gather logic
								snake_case__						:       Any       						=  None
								if self._is_main():
										snake_case__						:       Optional[int]       						=  [torch.empty(question_hidden_states.shape			,			dtype=torch.floataa  ) for _ in range(UpperCamelCase_  )]
								dist.gather(torch.tensor(UpperCamelCase_  )			,			dst=0			,			gather_list=UpperCamelCase_			,			group=self.process_group  )

								# scatter logic
								snake_case__						:       Optional[Any]       						=  question_hidden_states.shape[0]
								snake_case__						:       Optional[Any]       						=  []
								snake_case__						:       int       						=  []
								if self._is_main():
										assert len(UpperCamelCase_  ) == world_size
										snake_case__						:       Any       						=  self._main_retrieve(torch.cat(UpperCamelCase_  ).numpy()			,			UpperCamelCase_  )
										snake_case__						:       int       						=  torch.tensor(UpperCamelCase_  ), torch.tensor(UpperCamelCase_  )
										snake_case__						:       str       						=  self._chunk_tensor(UpperCamelCase_			,			UpperCamelCase_  )
										snake_case__						:       int       						=  self._chunk_tensor(UpperCamelCase_			,			UpperCamelCase_  )
								snake_case__						:       List[Any]       						=  self._scattered(UpperCamelCase_			,			[n_queries, n_docs]			,			target_type=torch.intaa  )
								snake_case__						:       List[Any]       						=  self._scattered(UpperCamelCase_			,			[n_queries, n_docs, question_hidden_states.shape[1]]  )

								return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(UpperCamelCase_  )






'''simple docstring'''


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 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase     )]     )
		snake_case__						:       Dict       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()   ,							__lowerCAmelCase     )     )   ,							x.transpose()     )   ,							__lowerCAmelCase     )
		return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  (1, 2, 1)
		snake_case__						:       Union[str, Any]       						=  (1, 1, 0, 7)
		snake_case__						:       List[str]       						=  SARIMAX(
		    __lowerCAmelCase   ,							exog=__lowerCAmelCase   ,							order=__lowerCAmelCase   ,							seasonal_order=__lowerCAmelCase     )
		snake_case__						:       str       						=  model.fit(disp=__lowerCAmelCase   ,							maxiter=6_00   ,							method='''nm'''     )
		snake_case__						:       List[str]       						=  model_fit.predict(1   ,							len(__lowerCAmelCase     )   ,							exog=[test_match]     )
		return result[0]




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  SVR(kernel='''rbf'''   ,							C=1   ,							gamma=0.1   ,							epsilon=0.1     )
		regressor.fit(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  regressor.predict(__lowerCAmelCase     )
		return y_pred[0]




def 	_a       (				__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		train_user.sort()
		snake_case__						:       int       						=  np.percentile(__lowerCAmelCase   ,							25     )
		snake_case__						:       List[str]       						=  np.percentile(__lowerCAmelCase   ,							75     )
		snake_case__						:       Dict       						=  qa - qa
		snake_case__						:       Optional[int]       						=  qa - (iqr * 0.1)
		return low_lim




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       int       						=  0
		snake_case__						:       List[Any]       						=  0
		for i in list_vote:
				if i > actual_result:
						snake_case__						:       Optional[Any]       						=  not_safe + 1
				else:
						if abs(abs(__lowerCAmelCase     ) - abs(__lowerCAmelCase     )     ) <= 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)
					lowerCAmelCase__						:       Optional[int]										=						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					lowerCAmelCase__						:       List[Any]										=						pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					lowerCAmelCase__						:       Union[str, Any]										=						Normalizer().fit_transform(data_input_df.values)
					# split data
					lowerCAmelCase__						:       Union[str, Any]										=						normalize_df[:, 2].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 0].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					lowerCAmelCase__						:       Dict										=						normalize_df[:, [1, 2]].tolist()
					lowerCAmelCase__						:       Union[str, Any]										=						x[: len(x) - 1]
					lowerCAmelCase__						:       List[str]										=						x[len(x) - 1 :]

					# for linear regression & sarimax
					lowerCAmelCase__						:       Union[str, Any]										=						total_date[: len(total_date) - 1]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[: len(total_user) - 1]
					lowerCAmelCase__						:       List[Any]										=						total_match[: len(total_match) - 1]

					lowerCAmelCase__						:       Optional[int]										=						total_date[len(total_date) - 1 :]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[len(total_user) - 1 :]
					lowerCAmelCase__						:       List[Any]										=						total_match[len(total_match) - 1 :]

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



