




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase			:	Tuple							 =     """▁"""

_lowerCamelCase			:	str							 =     {"""vocab_file""": """spiece.model"""}

_lowerCamelCase			:	List[Any]							 =     {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

_lowerCamelCase			:	List[str]							 =     {
    """google/pegasus-xsum""": 512,
}


_lowerCamelCase			:	Union[str, Any]							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   VOCAB_FILES_NAMES

    UpperCAmelCase__													=   VOCAB_FILES_NAMES
    UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__													=   ['''input_ids''', '''attention_mask''']


    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Union[str, Any]="<pad>"      ,				UpperCAmelCase__   :			Tuple="</s>"      ,				UpperCAmelCase__   :			Optional[int]="<unk>"      ,				UpperCAmelCase__   :			Dict="<mask_2>"      ,				UpperCAmelCase__   :			Union[str, Any]="<mask_1>"      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			int=103      ,				UpperCAmelCase__   :			Optional[Dict[str, Any]] = None      ,				**UpperCAmelCase__   :			int      ,				)		->None:





           '''simple docstring'''

           A__   				=					offset
           if additional_special_tokens is not None:
                  if not isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                         raise TypeError(
                             f"""additional_special_tokens should be of type {type(UpperCAmelCase__)}, but is"""
                             f""" {type(UpperCAmelCase__)}""")

                  A__   				=					(
                      ([mask_token_sent] + additional_special_tokens)
                      if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                      else additional_special_tokens
                  )
                  # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                  additional_special_tokens_extended += [
                      f"""<unk_{i}>""" for i in range(len(UpperCAmelCase__)      ,				self.offset - 1)
                  ]

                  if len(set(UpperCAmelCase__)) != len(UpperCAmelCase__):
                         raise ValueError(
                             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                             f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""")
                  A__   				=					additional_special_tokens_extended
           else:
                  A__   				=					[mask_token_sent] if mask_token_sent is not None else []
                  additional_special_tokens += [f"""<unk_{i}>""" for i in range(2      ,				self.offset)]

           A__   				=					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCAmelCase__      ,				unk_token=UpperCAmelCase__      ,				mask_token=UpperCAmelCase__      ,				pad_token=UpperCAmelCase__      ,				mask_token_sent=UpperCAmelCase__      ,				offset=UpperCAmelCase__      ,				additional_special_tokens=UpperCAmelCase__      ,				sp_model_kwargs=self.sp_model_kwargs      ,				**UpperCAmelCase__      ,				)
           A__   				=					mask_token_sent
           A__   				=					vocab_file
           A__   				=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCAmelCase__)

           # add special tokens to encoder dict
           A__   				=					{
               0: self.pad_token,
               1: self.eos_token,
           }

           if self.mask_token_sent is not None:
                  self.encoder.update(
                      {
                          2: self.mask_token_sent,
                          3: self.mask_token,
                      })

           if self.offset > 0:
                  # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                  # mask_token_sent is already added to list -> so start at 1
                  self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1      ,				self.offset - 1)})

           A__   				=					{v: k for k, v in self.encoder.items()}


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           return len(self.sp_model) + self.offset


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict[str, int]:





           '''simple docstring'''

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


    def __getstate__(    self   :			List[Any])		->Optional[int]:





           '''simple docstring'''

           A__   				=					self.__dict__.copy()
           A__   				=					None
           return state


    def __setstate__(    self   :			Dict      ,				UpperCAmelCase__   :			Optional[int])		->Tuple:





           '''simple docstring'''

           A__   				=					d

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

           A__   				=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			str)		->List[str]:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				UpperCAmelCase__   :			str)		->int:





           '''simple docstring'''

           if token in self.decoder:
                  return self.decoder[token]
           elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
           A__   				=					self.sp_model.piece_to_id(UpperCAmelCase__)
           return sp_id + self.offset


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			int)		->str:





           '''simple docstring'''

           if index in self.encoder:
                  return self.encoder[index]
           elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
           else:
                  A__   				=					self.sp_model.IdToPiece(index - self.offset)
           return token


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			Tuple)		->Optional[int]:





           '''simple docstring'''

           A__   				=					[]
           A__   				=					''''''
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(UpperCAmelCase__) + token
                         A__   				=					[]
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Dict=False)		->Optional[Any]:





           '''simple docstring'''

           return 1


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Optional[int])		->Dict:





           '''simple docstring'''

           A__   				=					set(self.all_special_ids)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

           return [1 if x in all_special_ids else 0 for x in seq]


    def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				UpperCAmelCase__   :			List      ,				UpperCAmelCase__   :			Optional[List] = None      ,				UpperCAmelCase__   :			bool = False)		->List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  return self._special_token_mask(UpperCAmelCase__)
           elif token_ids_a is None:
                  return self._special_token_mask(UpperCAmelCase__) + [1]
           else:
                  return self._special_token_mask(token_ids_a + token_ids_a) + [1]


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Union[str, Any]=None)		->List[int]:





           '''simple docstring'''

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





    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[str] = None)		->Tuple[str]:





           '''simple docstring'''

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

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

           return (out_vocab_file,)








from collections import namedtuple

import requests
from lxml import html  # type: ignore

_lowerCamelCase			:	Optional[int]							 =     namedtuple("""covid_data""", """cases deaths recovered""")







def SCREAMING_SNAKE_CASE       ( lowercase_ = "https://www.worldometers.info/coronavirus/"		)     -> covid_data:



       """simple docstring"""

       A__   				=					'''//div[@class = "maincounter-number"]/span/text()'''
       return covid_data(*html.fromstring(requests.get(lowercase_		).content		).xpath(lowercase_		)		)


_lowerCamelCase			:	Optional[int]							 =     """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))


