


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


A   :					List[str]	=      logging.get_logger(__name__)


A   :					Optional[Any]	=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A   :					Union[str, Any]	=      {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

A   :					Optional[int]	=      {
    "allenai/led-base-16384": 16384,
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    VOCAB_FILES_NAMES
       A__      		=    PRETRAINED_VOCAB_FILES_MAP
       A__      		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A__      		=    LEDTokenizer
       A__      		=    ["input_ids", "attention_mask"]





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


             '''simple docstring'''



             super().__init__(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   tokenizer_file=__lowerCamelCase      ,   errors=__lowerCamelCase      ,   bos_token=__lowerCamelCase      ,   eos_token=__lowerCamelCase      ,   sep_token=__lowerCamelCase      ,   cls_token=__lowerCamelCase      ,   unk_token=__lowerCamelCase      ,   pad_token=__lowerCamelCase      ,   mask_token=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   trim_offsets=__lowerCamelCase      ,   **__lowerCamelCase      ,   )

             lowerCamelCase__	:       Optional[int]					=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
             if pre_tok_state.get("add_prefix_space"      ,   __lowerCamelCase ) != add_prefix_space:
                   lowerCamelCase__	:       List[str]					=						getattr(__lowerCamelCase      ,   pre_tok_state.pop("type" ) )
                   lowerCamelCase__	:       Tuple					=						add_prefix_space
                   lowerCamelCase__	:       str					=						pre_tok_class(**__lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						add_prefix_space

             # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
             lowerCamelCase__	:       Dict					=						"post_processor"
             lowerCamelCase__	:       List[Any]					=						getattr(self.backend_tokenizer      ,   __lowerCamelCase      ,   __lowerCamelCase )
             if tokenizer_component_instance:
                   lowerCamelCase__	:       str					=						json.loads(tokenizer_component_instance.__getstate__() )

                   # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                   if "sep" in state:
                         lowerCamelCase__	:       List[str]					=						tuple(state["sep"] )
                   if "cls" in state:
                         lowerCamelCase__	:       List[str]					=						tuple(state["cls"] )

                   lowerCamelCase__	:       str					=						False

                   if state.get("add_prefix_space"      ,   __lowerCamelCase ) != add_prefix_space:
                         lowerCamelCase__	:       Any					=						add_prefix_space
                         lowerCamelCase__	:       Dict					=						True

                   if state.get("trim_offsets"      ,   __lowerCamelCase ) != trim_offsets:
                         lowerCamelCase__	:       int					=						trim_offsets
                         lowerCamelCase__	:       Any					=						True

                   if changes_to_apply:
                         lowerCamelCase__	:       Union[str, Any]					=						getattr(__lowerCamelCase      ,   state.pop("type" ) )
                         lowerCamelCase__	:       Union[str, Any]					=						component_class(**__lowerCamelCase )
                         setattr(self.backend_tokenizer      ,   __lowerCamelCase      ,   __lowerCamelCase )





       @property
       # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             if self._mask_token is None:
                   if self.verbose:
                         logger.error("Using mask_token, but it is not set yet." )
                   return None
             return str(self._mask_token )





       @mask_token.setter
       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,   __lowerCamelCase ) else value
             lowerCamelCase__	:       Optional[Any]					=						value





       def 		lowerCAmelCase  (     self							:    Tuple      ,   *__lowerCamelCase							:    Optional[Any]      ,   **__lowerCamelCase							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						kwargs.get("is_split_into_words"      ,   __lowerCamelCase )

             if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                       "to use it with pretokenized inputs." )

             return super()._batch_encode_plus(*__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   *__lowerCamelCase							:    Dict      ,   **__lowerCamelCase							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						kwargs.get("is_split_into_words"      ,   __lowerCamelCase )

             if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                       "to use it with pretokenized inputs." )

             return super()._encode_plus(*__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[str] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self._tokenizer.model.save(__lowerCamelCase      ,   name=__lowerCamelCase )
             return tuple(__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Dict=None ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                   return output

             return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						[self.sep_token_id]
             lowerCamelCase__	:       Optional[Any]					=						[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 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    Union[Dict[str, EncodedInput], BatchEncoding]      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    Optional[bool] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						super()._pad(
                 encoded_inputs=__lowerCamelCase      ,   max_length=__lowerCamelCase      ,   padding_strategy=__lowerCamelCase      ,   pad_to_multiple_of=__lowerCamelCase      ,   return_attention_mask=__lowerCamelCase      ,   )

             # Load from model defaults
             if return_attention_mask is None:
                   lowerCamelCase__	:       Optional[Any]					=						"attention_mask" in self.model_input_names

             if return_attention_mask and "global_attention_mask" in encoded_inputs:
                   lowerCamelCase__	:       Any					=						encoded_inputs[self.model_input_names[0]]
                   # `global_attention_mask` need to have the same length as other (sequential) inputs.
                   lowerCamelCase__	:       int					=						len(encoded_inputs["global_attention_mask"] ) != len(__lowerCamelCase )

                   if needs_to_be_padded:
                         lowerCamelCase__	:       Any					=						len(__lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )

                         if self.padding_side == "right":
                               # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                               lowerCamelCase__	:       Optional[Any]					=						(
                                   encoded_inputs["global_attention_mask"] + [-1] * difference
                               )
                         elif self.padding_side == "left":
                               lowerCamelCase__	:       Dict					=						[-1] * difference + encoded_inputs[
                                   "global_attention_mask"
                               ]
                         else:
                               raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

             return encoded_inputs






import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
    print("Googling.....")
    A   :					str	=      "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
    A   :					Optional[int]	=      requests.get(url, headers={"UserAgent": UserAgent().random})
    # res.raise_for_status()
    with open("project1a.html", "wb") as out_file:  # only for knowing the class
        for data in res.iter_content(10000):
            out_file.write(data)
    A   :					int	=      BeautifulSoup(res.text, "html.parser")
    A   :					Any	=      list(soup.select(".eZt8xd"))[:5]

    print(len(links))
    for link in links:
        if link.text == "Maps":
            webbrowser.open(link.get("href"))
        else:
            webbrowser.open(f'https://google.com{link.get("href")}')


