






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_a     : Optional[Any]   	=      pd.read_csv(
    """https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"""
    """position_salaries.csv"""
)
_a     : List[Any]   	=      dataset.iloc[:, 1:2].values
_a     : Union[str, Any]   	=      dataset.iloc[:, 2].values


_a	,					_a	,					_a	,					_a     : Any   	=      train_test_split(X, y, test_size=0.2, random_state=0)


_a     : Any   	=      PolynomialFeatures(degree=4)
_a     : int   	=      poly_reg.fit_transform(X)
_a     : int   	=      LinearRegression()
pol_reg.fit(X_poly, y)


def 					snake_case__				(				):
    plt.scatter(lowercase__     ,      lowercase__     ,      color="red"							)
    plt.plot(lowercase__     ,      pol_reg.predict(poly_reg.fit_transform(lowercase__							)							)     ,      color="blue"							)
    plt.title("Truth or Bluff (Linear Regression)"							)
    plt.xlabel("Position level"							)
    plt.ylabel("Salary"							)
    plt.show()


if __name__ == "__main__":
 viz_polymonial()

 # Predicting a new result with Polymonial Regression
 pol_reg.predict(poly_reg.fit_transform([[5.5]]))
 # output should be 132148.43750003






'''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


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	'''▁'''

UpperCAmelCase					 =	{'''vocab_file''': '''sentencepiece.bpe.model'''}

UpperCAmelCase					 =	{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

UpperCAmelCase					 =	{
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
UpperCAmelCase					 =	['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			["input_ids", "attention_mask"]

       lowerCAmelCase_												=			[]
       lowerCAmelCase_												=			[]







       def __init__(				self      :			Dict    ,   __lowercase      :			List[str]    ,   __lowercase      :			int="<s>"    ,   __lowercase      :			Any="</s>"    ,   __lowercase      :			Tuple="</s>"    ,   __lowercase      :			Dict="<s>"    ,   __lowercase      :			Dict="<unk>"    ,   __lowercase      :			List[Any]="<pad>"    ,   __lowercase      :			Dict="<mask>"    ,   __lowercase      :			Tuple=None    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Optional[Dict[str, Any]] = None    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			List[str]=False    ,   **__lowercase      :			Union[str, Any]    ,   ):







            """simple docstring"""







            __lowercase      		=AddedToken(__lowercase    ,   lstrip=__lowercase    ,   rstrip=__lowercase   ) if isinstance(__lowercase    ,   __lowercase   ) else mask_token

            __lowercase      		={} if sp_model_kwargs is None else sp_model_kwargs
            __lowercase      		=legacy_behaviour

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

            __lowercase      		=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(str(__lowercase   )   )
            __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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
            # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

            # 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(__lowercase   )
            }
            __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 'eng_Latn'
            __lowercase      		=self.lang_code_to_id[self._src_lang]
            __lowercase      		=tgt_lang
            self.set_src_lang_special_tokens(self._src_lang   )







       def __getstate__(				self      :			Tuple   ):







            """simple docstring"""







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







       def __setstate__(				self      :			List[Any]    ,   __lowercase      :			List[Any]   ):







            """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       snake_case (				self      :			Dict   ):







            """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       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            return self._src_lang







       @src_lang.setter
       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=new_src_lang
            self.set_src_lang_special_tokens(self._src_lang   )







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			List[int]    ,   __lowercase      :			Optional[List[int]] = None    ,   __lowercase      :			bool = False   ):







            """simple docstring"""







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

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







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			List[int]    ,   __lowercase      :			Optional[List[int]] = 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       snake_case (				self      :			int    ,   __lowercase      :			List[int]    ,   __lowercase      :			Optional[List[int]] = 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       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			Dict    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str]    ,   __lowercase      :			Optional[str]    ,   **__lowercase      :			Optional[int]   ):







            """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(__lowercase    ,   add_special_tokens=__lowercase    ,   return_tensors=__lowercase    ,   **__lowercase   )
            __lowercase      		=self.convert_tokens_to_ids(__lowercase   )
            __lowercase      		=tgt_lang_id
            return inputs







       def       snake_case (				self      :			Any   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Any    ,   __lowercase      :			str   ):







            """simple docstring"""







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







       def       snake_case (				self      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







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

            # 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       snake_case (				self      :			int    ,   __lowercase      :			Any   ):







            """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       snake_case (				self      :			str    ,   __lowercase      :			List[str]   ):







            """simple docstring"""







            __lowercase      		=''.join(__lowercase   ).replace(__lowercase    ,   ' '   ).strip()
            return out_string







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = None   ):







            """simple docstring"""







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

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

            return (out_vocab_file,)







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			List[str]    ,   __lowercase      :			str = "eng_Latn"    ,   __lowercase      :			Optional[List[str]] = None    ,   __lowercase      :			str = "fra_Latn"    ,   **__lowercase      :			List[str]    ,   ):







            """simple docstring"""







            __lowercase      		=src_lang
            __lowercase      		=tgt_lang
            return super().prepare_seqaseq_batch(__lowercase    ,   __lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            return self.set_src_lang_special_tokens(self.src_lang   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            return self.set_tgt_lang_special_tokens(self.tgt_lang   )







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			List[str]   ):







            """simple docstring"""







            __lowercase      		=self.lang_code_to_id[src_lang]
            if self.legacy_behaviour:
                 __lowercase      		=[]
                 __lowercase      		=[self.eos_token_id, self.cur_lang_code]
            else:
                 __lowercase      		=[self.cur_lang_code]
                 __lowercase      		=[self.eos_token_id]







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=self.lang_code_to_id[lang]
            if self.legacy_behaviour:
                 __lowercase      		=[]
                 __lowercase      		=[self.eos_token_id, self.cur_lang_code]
            else:
                 __lowercase      		=[self.cur_lang_code]
                 __lowercase      		=[self.eos_token_id]


