"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase       :			Optional[int]   	=logging.get_logger(__name__)

__lowerCAmelCase       :			List[Any]   	="""▁"""

__lowerCAmelCase       :			str   	={"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCAmelCase       :			Union[str, Any]   	={
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

__lowerCAmelCase       :			Optional[Any]   	={
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
__lowerCAmelCase       :			Tuple   	=["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]





class 					_A		(			__a      ):
      snake_case__     :  Dict				    =      VOCAB_FILES_NAMES
      snake_case__     :  List[str]				    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__     :  Union[str, Any]				    =      PRETRAINED_VOCAB_FILES_MAP
      snake_case__     :  int				    =      ["""input_ids""", """attention_mask"""]

      snake_case__     :  List[int]				    =      []
      snake_case__     :  List[int]				    =      []

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase="<s>"						,			__lowerCAmelCase="</s>"						,			__lowerCAmelCase="</s>"						,			__lowerCAmelCase="<s>"						,			__lowerCAmelCase="<unk>"						,			__lowerCAmelCase="<pad>"						,			__lowerCAmelCase="<mask>"						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase = None						,			__lowerCAmelCase=None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      AddedToken(_lowerCamelCase						,			lstrip=_lowerCamelCase						,			rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase						,			_lowerCamelCase		) else mask_token

            lowercase								=      {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=_lowerCamelCase						,			eos_token=_lowerCamelCase						,			unk_token=_lowerCamelCase						,			sep_token=_lowerCamelCase						,			cls_token=_lowerCamelCase						,			pad_token=_lowerCamelCase						,			mask_token=_lowerCamelCase						,			tokenizer_file=_lowerCamelCase						,			src_lang=_lowerCamelCase						,			tgt_lang=_lowerCamelCase						,			additional_special_tokens=_lowerCamelCase						,			sp_model_kwargs=self.sp_model_kwargs						,			**_lowerCamelCase						,			)

            lowercase								=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(str(_lowerCamelCase		)		)
            lowercase								=      vocab_file

            # Original fairseq vocab and spm vocab must be "aligned":
            # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
            # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
            # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
            # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

            # Mimic fairseq token-to-id alignment for the first 4 token
            lowercase								=      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

            # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
            lowercase								=      1

            lowercase								=      len(self.sp_model		)
            lowercase								=      {
                code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowerCamelCase		)
            }
            lowercase								=      {v: k for k, v in self.lang_code_to_id.items()}
            lowercase								=      len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

            self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
            lowercase								=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
            lowercase								=      list(self.lang_code_to_id.keys()		)

            if additional_special_tokens is not None:
                  # Only add those special tokens if they are not already there.
                  self._additional_special_tokens.extend(
                      [t for t in additional_special_tokens if t not in self._additional_special_tokens]		)

            lowercase								=      src_lang if src_lang is not None else """en_XX"""
            lowercase								=      self.lang_code_to_id[self._src_lang]
            lowercase								=      tgt_lang
            self.set_src_lang_special_tokens(self._src_lang		)

      def __getstate__(		self		):







            """simple docstring"""



            lowercase								=      self.__dict__.copy()
            lowercase								=      None
            lowercase								=      self.sp_model.serialized_model_proto()
            return state

      def __setstate__(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      d

            # for backward compatibility
            if not hasattr(self						,			"""sp_model_kwargs"""		):
                  lowercase								=      {}

            lowercase								=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self._src_lang

      @src_lang.setter
      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      new_src_lang
            self.set_src_lang_special_tokens(self._src_lang		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = False		):







            """simple docstring"""



            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=_lowerCamelCase						,			token_ids_a=_lowerCamelCase						,			already_has_special_tokens=_lowerCamelCase		)

            lowercase								=      [1] * len(self.prefix_tokens		)
            lowercase								=      [1] * len(self.suffix_tokens		)
            if token_ids_a is None:
                  return prefix_ones + ([0] * len(_lowerCamelCase		)) + suffix_ones
            return prefix_ones + ([0] * len(_lowerCamelCase		)) + ([0] * len(_lowerCamelCase		)) + suffix_ones

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if token_ids_a is None:
                  return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      [self.sep_token_id]
            lowercase								=      [self.cls_token_id]

            if token_ids_a is None:
                  return len(cls + token_ids_a + sep		) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            if src_lang is None or tgt_lang is None:
                  raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""		)
            lowercase								=      src_lang
            lowercase								=      self(_lowerCamelCase						,			add_special_tokens=_lowerCamelCase						,			return_tensors=_lowerCamelCase						,			**_lowerCamelCase		)
            lowercase								=      self.convert_tokens_to_ids(_lowerCamelCase		)
            lowercase								=      tgt_lang_id
            return inputs

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      {self.convert_ids_to_tokens(_lowerCamelCase		): i for i in range(self.vocab_size		)}
            vocab.update(self.added_tokens_encoder		)
            return vocab

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.sp_model.encode(_lowerCamelCase						,			out_type=_lowerCamelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            lowercase								=      self.sp_model.PieceToId(_lowerCamelCase		)

            # Need to return unknown token if the SP model returned 0
            return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(index - self.fairseq_offset		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      """""".join(_lowerCamelCase		).replace(_lowerCamelCase						,			""" """		).strip()
            return out_string

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if not os.path.isdir(_lowerCamelCase		):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
                  return
            lowercase								=      os.path.join(
                _lowerCamelCase						,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

            if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCamelCase		) and os.path.isfile(self.vocab_file		):
                  copyfile(self.vocab_file						,			_lowerCamelCase		)
            elif not os.path.isfile(self.vocab_file		):
                  with open(_lowerCamelCase						,			"""wb"""		) as fi:
                        lowercase								=      self.sp_model.serialized_model_proto()
                        fi.write(_lowerCamelCase		)

            return (out_vocab_file,)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = "en_XX"						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "ro_RO"						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      src_lang
            lowercase								=      tgt_lang
            return super().prepare_seqaseq_batch(_lowerCamelCase						,			_lowerCamelCase						,			**_lowerCamelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            return self.set_src_lang_special_tokens(self.src_lang		)

      def 							A__			(		self		):







            """simple docstring"""



            return self.set_tgt_lang_special_tokens(self.tgt_lang		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.lang_code_to_id[src_lang]
            lowercase								=      []
            lowercase								=      [self.eos_token_id, self.cur_lang_code]







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.lang_code_to_id[lang]
            lowercase								=      []
            lowercase								=      [self.eos_token_id, self.cur_lang_code]





"""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      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__      )]      )
			__lowercase       =       np.array(__magic_name__      )
			__lowercase       =       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,   __magic_name__      )      )			,   x.transpose()      )			,   __magic_name__      )
			return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       (1, 2, 1)
			__lowercase       =       (1, 1, 0, 7)
			__lowercase       =       SARIMAX(
			    __magic_name__			,   exog=__magic_name__			,   order=__magic_name__			,   seasonal_order=__magic_name__      )
			__lowercase       =       model.fit(disp=__magic_name__			,   maxiter=6_0_0			,   method="nm"      )
			__lowercase       =       model_fit.predict(1			,   len(__magic_name__      )			,   exog=[test_match]      )
			return result[0]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       SVR(kernel="rbf"			,   C=1			,   gamma=0.1			,   epsilon=0.1      )
			regressor.fit(__magic_name__			,   __magic_name__      )
			__lowercase       =       regressor.predict(__magic_name__      )
			return y_pred[0]



def      lowerCAmelCase__			(		__magic_name__      )						->float:
			train_user.sort()
			__lowercase       =       np.percentile(__magic_name__			,   2_5      )
			__lowercase       =       np.percentile(__magic_name__			,   7_5      )
			__lowercase       =       qa - qa
			__lowercase       =       qa - (iqr * 0.1)
			return low_lim



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->bool:
			__lowercase       =       0
			__lowercase       =       0
			for i in list_vote:
						if i > actual_result:
									__lowercase       =       not_safe + 1
						else:
									if abs(abs(__magic_name__      ) - abs(__magic_name__      )      ) <= 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)
					_lowercase							=				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
					_lowercase							=				pd.DataFrame(
					    data_input, columns=['''total_user''', '''total_even''', '''days''']
					)
					_lowercase							=				Normalizer().fit_transform(data_input_df.values)
					# split data
					_lowercase							=				normalize_df[:, 2].tolist()
					_lowercase							=				normalize_df[:, 0].tolist()
					_lowercase							=				normalize_df[:, 1].tolist()

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

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

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

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





