






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_mbart import MBartTokenizer
else:
     __snake_case      :      str             =       None


__snake_case      :      Dict             =       logging.get_logger(__name__)


__snake_case      :      Dict             =       {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case      :      Optional[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"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

__snake_case      :      List[str]             =       {
    """facebook/mbart-large-en-ro""": 10_24,
    """facebook/mbart-large-cc25""": 10_24,
}

# fmt: off
__snake_case      :      Dict             =       ["""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       __SCREAMING_SNAKE_CASE   (				__lowercase):
      _SCREAMING_SNAKE_CASE		:    Optional[Any]						=   VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE		:    Optional[int]						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE		:    Tuple						=   PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE		:    Tuple						=   ['''input_ids''', '''attention_mask''']
      _SCREAMING_SNAKE_CASE		:    Optional[Any]						=   MBartTokenizer

      _SCREAMING_SNAKE_CASE		:    List[int]						=   []
      _SCREAMING_SNAKE_CASE		:    List[int]						=   []






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






            """simple docstring"""
            lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else mask_token

            super().__init__(
                vocab_file=_UpperCamelCase       ,  tokenizer_file=_UpperCamelCase       ,  bos_token=_UpperCamelCase       ,  eos_token=_UpperCamelCase       ,  sep_token=_UpperCamelCase       ,  cls_token=_UpperCamelCase       ,  unk_token=_UpperCamelCase       ,  pad_token=_UpperCamelCase       ,  mask_token=_UpperCamelCase       ,  src_lang=_UpperCamelCase       ,  tgt_lang=_UpperCamelCase       ,  additional_special_tokens=_UpperCamelCase       ,  **_UpperCamelCase       ,  )

            lowerCAmelCase__        =  vocab_file
            lowerCAmelCase__        =  False if not self.vocab_file else True

            lowerCAmelCase__        =  FAIRSEQ_LANGUAGE_CODES.copy()

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

            self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}				)
            lowerCAmelCase__        =  {
                lang_code: self.convert_tokens_to_ids(_UpperCamelCase				) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            lowerCAmelCase__        =  src_lang if src_lang is not None else 'en_XX'
            lowerCAmelCase__        =  self.convert_tokens_to_ids(self._src_lang				)
            lowerCAmelCase__        =  tgt_lang
            self.set_src_lang_special_tokens(self._src_lang				)






      @property
      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            return self._src_lang






      @src_lang.setter
      def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  new_src_lang
            self.set_src_lang_special_tokens(self._src_lang				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = 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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






            """simple docstring"""
            lowerCAmelCase__        =  [self.sep_token_id]
            lowerCAmelCase__        =  [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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  **_UpperCamelCase				):






            """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'				)
            lowerCAmelCase__        =  src_lang
            lowerCAmelCase__        =  self(_UpperCamelCase       ,  add_special_tokens=_UpperCamelCase       ,  return_tensors=_UpperCamelCase       ,  **_UpperCamelCase				)
            lowerCAmelCase__        =  self.convert_tokens_to_ids(_UpperCamelCase				)
            lowerCAmelCase__        =  tgt_lang_id
            return inputs






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = "en_XX"       ,  _UpperCamelCase = None       ,  _UpperCamelCase = "ro_RO"       ,  **_UpperCamelCase       ,  ):






            """simple docstring"""
            lowerCAmelCase__        =  src_lang
            lowerCAmelCase__        =  tgt_lang
            return super().prepare_seqaseq_batch(_UpperCamelCase       ,  _UpperCamelCase       ,  **_UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            return self.set_src_lang_special_tokens(self.src_lang				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            return self.set_tgt_lang_special_tokens(self.tgt_lang				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  self.convert_tokens_to_ids(_UpperCamelCase				)
            lowerCAmelCase__        =  []
            lowerCAmelCase__        =  [self.eos_token_id, self.cur_lang_code]

            lowerCAmelCase__        =  self.convert_ids_to_tokens(self.prefix_tokens				)
            lowerCAmelCase__        =  self.convert_ids_to_tokens(self.suffix_tokens				)

            lowerCAmelCase__        =  processors.TemplateProcessing(
                single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,  self.prefix_tokens + self.suffix_tokens				)				)       ,  )






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  self.convert_tokens_to_ids(_UpperCamelCase				)
            lowerCAmelCase__        =  []
            lowerCAmelCase__        =  [self.eos_token_id, self.cur_lang_code]

            lowerCAmelCase__        =  self.convert_ids_to_tokens(self.prefix_tokens				)
            lowerCAmelCase__        =  self.convert_ids_to_tokens(self.suffix_tokens				)

            lowerCAmelCase__        =  processors.TemplateProcessing(
                single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,  self.prefix_tokens + self.suffix_tokens				)				)       ,  )







      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






            """simple docstring"""
            if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.'				)

            if not os.path.isdir(_UpperCamelCase				):
                  logger.error(F"Vocabulary path ({save_directory}) should be a directory."				)
                  return
            lowerCAmelCase__        =  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				):
                  copyfile(self.vocab_file       ,  _UpperCamelCase				)

            return (out_vocab_file,)













from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

__snake_case      :      Any             =       """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""







def 		_UpperCamelCase				(      UpperCamelCase_    :   str = "mumbai"						)					->				Generator[tuple[str, str], None, None]:

						"""simple docstring"""



						lowerCAmelCase__        =  BeautifulSoup(requests.get(url + location						).content      ,    'html.parser'						)
						# This attribute finds out all the specifics listed in a job
						for job in soup.find_all('div'      ,    attrs={'data-tn-component': 'organicJob'}						):
												lowerCAmelCase__        =  job.find('a'      ,    attrs={'data-tn-element': 'jobTitle'}						).text.strip()
												lowerCAmelCase__        =  job.find('span'      ,    {'class': 'company'}						).text.strip()
												yield job_title, company_name


if __name__ == "__main__":
					for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
										print(f'Job {i:>2} is {job[0]} at {job[1]}')





