


"""simple docstring"""



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   			=    None


__snake_case   			=    logging.get_logger(__name__)


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

__snake_case   			=    {
    """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   			=    {
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
__snake_case   			=    ["""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 _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[int]							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase :       Union[str, Any]							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase :       Optional[Any]							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase :       str							  =  ['''input_ids''', '''attention_mask''']
   __UpperCAmelCase :       List[str]							  =  MBartTokenizer

   __UpperCAmelCase :       List[int]							  =  []
   __UpperCAmelCase :       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__						,	)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    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__						,	)

       snake_case    :      Tuple      					=    vocab_file
       snake_case    :      Tuple      					=    False if not self.vocab_file else True

       snake_case    :      int      					=    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}						)
       snake_case    :      int      					=    {
           lang_code: self.convert_tokens_to_ids(UpperCamelCase__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       snake_case    :      List[str]      					=    src_lang if src_lang is not None else "en_XX"
       snake_case    :      Dict      					=    self.convert_tokens_to_ids(self._src_lang						)
       snake_case    :      int      					=    tgt_lang
       self.set_src_lang_special_tokens(self._src_lang						)







   @property
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       return self._src_lang







   @src_lang.setter
   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		None:

       '''simple docstring'''




       snake_case    :      str      					=    new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		List[int]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    [self.sep_token_id]
       snake_case    :      Union[str, 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						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	**UpperCamelCase__						)	->		Any:

       '''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"						)
       snake_case    :      List[Any]      					=    src_lang
       snake_case    :      Tuple      					=    self(UpperCamelCase__						,	add_special_tokens=UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    self.convert_tokens_to_ids(UpperCamelCase__						)
       snake_case    :      int      					=    tgt_lang_id
       return inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = "en_XX"						,	UpperCamelCase__ = None						,	UpperCamelCase__ = "ro_RO"						,	**UpperCamelCase__						,	)	->		BatchEncoding:

       '''simple docstring'''




       snake_case    :      Any      					=    src_lang
       snake_case    :      Union[str, Any]      					=    tgt_lang
       return super().prepare_seqaseq_batch(UpperCamelCase__						,	UpperCamelCase__						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       return self.set_src_lang_special_tokens(self.src_lang						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       return self.set_tgt_lang_special_tokens(self.tgt_lang						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		None:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.convert_tokens_to_ids(UpperCamelCase__						)
       snake_case    :      Any      					=    []
       snake_case    :      List[str]      					=    [self.eos_token_id, self.cur_lang_code]

       snake_case    :      Any      					=    self.convert_ids_to_tokens(self.prefix_tokens						)
       snake_case    :      List[Any]      					=    self.convert_ids_to_tokens(self.suffix_tokens						)

       snake_case    :      Any      					=    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  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		None:

       '''simple docstring'''




       snake_case    :      int      					=    self.convert_tokens_to_ids(UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    []
       snake_case    :      Tuple      					=    [self.eos_token_id, self.cur_lang_code]

       snake_case    :      Optional[int]      					=    self.convert_ids_to_tokens(self.prefix_tokens						)
       snake_case    :      Union[str, Any]      					=    self.convert_ids_to_tokens(self.suffix_tokens						)

       snake_case    :      List[str]      					=    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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						)	->		Tuple[str]:

       '''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
       snake_case    :      Optional[Any]      					=    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,)









"""simple docstring"""



import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__snake_case   			=    {
    """User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
    """ (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"""
}







def   __lowerCAmelCase						(						lowercase  :  str = "dhaka"						,							lowercase  :  int = 5 )	->						int:


    """simple docstring"""





    snake_case    :      List[Any]      					=    min(lowercase						,							50 )  # Prevent abuse!
    snake_case    :      Optional[Any]      					=    {
        "q": query,
        "tbm": "isch",
        "hl": "en",
        "ijn": "0",
    }

    snake_case    :      str      					=    requests.get("https://www.google.com/search"						,							params=lowercase						,							headers=lowercase )
    snake_case    :      List[str]      					=    BeautifulSoup(html.text						,							"html.parser" )
    snake_case    :      List[Any]      					=    "".join(
        re.findall(R"AF_initDataCallback\(([^<]+)\);"						,							str(soup.select("script" ) ) ) )

    snake_case    :      Optional[Any]      					=    json.dumps(lowercase )
    snake_case    :      str      					=    json.loads(lowercase )

    snake_case    :      List[str]      					=    re.findall(
        R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","						,							lowercase						,							)
    if not matched_google_image_data:
        return 0

    snake_case    :      List[str]      					=    re.sub(
        R"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"						,							""						,							str(lowercase )						,							)

    snake_case    :      Dict      					=    re.findall(
        R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"						,							lowercase						,							)
    for index, fixed_full_res_image in enumerate(lowercase ):
        if index >= max_images:
            return index
        snake_case    :      List[str]      					=    bytes(lowercase						,							"ascii" ).decode(
            "unicode-escape" )
        snake_case    :      Dict      					=    bytes(lowercase						,							"ascii" ).decode(
            "unicode-escape" )
        snake_case    :      int      					=    urllib.request.build_opener()
        snake_case    :      int      					=    [
            (
                "User-Agent",
                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
                " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
            )
        ]
        urllib.request.install_opener(lowercase )
        snake_case    :      Optional[int]      					=    F'query_{query.replace(" "						,							"_" )}'
        if not os.path.exists(lowercase ):
            os.makedirs(lowercase )
        urllib.request.urlretrieve(  # noqa: S310
            lowercase						,							F'{path_name}/original_size_img_{index}.jpg' )
    return index


if __name__ == "__main__":
  try:
    __snake_case   			=    download_images_from_google_query(sys.argv[1])
    print(F'''{image_count} images were downloaded to disk.''')
  except IndexError:
    print("""Please provide a search term.""")
    raise





