




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A						:							Optional[int]							=  logging.get_logger(__name__)
__A						:							int							=  {"""vocab_file""": """spiece.model"""}

__A						:							Optional[int]							=  {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

__A						:							Dict							=  {
    """albert-base-v1""": 5_1_2,
    """albert-large-v1""": 5_1_2,
    """albert-xlarge-v1""": 5_1_2,
    """albert-xxlarge-v1""": 5_1_2,
    """albert-base-v2""": 5_1_2,
    """albert-large-v2""": 5_1_2,
    """albert-xlarge-v2""": 5_1_2,
    """albert-xxlarge-v2""": 5_1_2,
}

__A						:							str							=  """▁"""




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





							def __init__(					self       :	List[Any]       ,     a       :	Optional[int]       ,     a       :	Dict=True       ,     a       :	Tuple=True       ,     a       :	List[Any]=False       ,     a       :	Dict="[CLS]"       ,     a       :	List[Any]="[SEP]"       ,     a       :	Optional[int]="<unk>"       ,     a       :	List[str]="[SEP]"       ,     a       :	Union[str, Any]="<pad>"       ,     a       :	Optional[int]="[CLS]"       ,     a       :	Any="[MASK]"       ,     a       :	Optional[Dict[str, Any]] = None       ,     **a       :	Tuple       ,     )			->					None:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										SCREAMING_SNAKE_CASE           =					(
										    AddedToken(a       ,     lstrip=a       ,     rstrip=a       ,     normalized=a			)
										    if isinstance(a       ,     a			)
										    else mask_token
										)

										SCREAMING_SNAKE_CASE           =					{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=a       ,     remove_space=a       ,     keep_accents=a       ,     bos_token=a       ,     eos_token=a       ,     unk_token=a       ,     sep_token=a       ,     pad_token=a       ,     cls_token=a       ,     mask_token=a       ,     sp_model_kwargs=self.sp_model_kwargs       ,     **a       ,     )

										SCREAMING_SNAKE_CASE           =					do_lower_case
										SCREAMING_SNAKE_CASE           =					remove_space
										SCREAMING_SNAKE_CASE           =					keep_accents
										SCREAMING_SNAKE_CASE           =					vocab_file

										SCREAMING_SNAKE_CASE           =					spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(a			)





							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					int:
										return len(self.sp_model			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					{self.convert_ids_to_tokens(a			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab





							def __getstate__(					self       :	Union[str, Any]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					self.__dict__.copy()
										SCREAMING_SNAKE_CASE           =					None
										return state





							def __setstate__(					self       :	Union[str, Any]       ,     a       :	str			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					d

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

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





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Union[str, Any]			)			->					Optional[int]:
										if self.remove_space:
													SCREAMING_SNAKE_CASE           =					""" """.join(inputs.strip().split()			)
										else:
													SCREAMING_SNAKE_CASE           =					inputs
										SCREAMING_SNAKE_CASE           =					outputs.replace("""``"""       ,     """\""""			).replace("""''"""       ,     """\""""			)

										if not self.keep_accents:
													SCREAMING_SNAKE_CASE           =					unicodedata.normalize("""NFKD"""       ,     a			)
													SCREAMING_SNAKE_CASE           =					"""""".join([c for c in outputs if not unicodedata.combining(a			)]			)
										if self.do_lower_case:
													SCREAMING_SNAKE_CASE           =					outputs.lower()

										return outputs





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	str			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					self.preprocess_text(a			)
										SCREAMING_SNAKE_CASE           =					self.sp_model.encode(a       ,     out_type=a			)
										SCREAMING_SNAKE_CASE           =					[]
										for piece in pieces:
													if len(a			) > 1 and piece[-1] == str(""","""			) and piece[-2].isdigit():
																SCREAMING_SNAKE_CASE           =					self.sp_model.EncodeAsPieces(piece[:-1].replace(a       ,     """"""			)			)
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																			if len(cur_pieces[0]			) == 1:
																						SCREAMING_SNAKE_CASE           =					cur_pieces[1:]
																			else:
																						SCREAMING_SNAKE_CASE           =					cur_pieces[0][1:]
																cur_pieces.append(piece[-1]			)
																new_pieces.extend(a			)
													else:
																new_pieces.append(a			)

										return new_pieces





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	int			)			->					List[Any]:
										return self.sp_model.PieceToId(a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	List[Any]			)			->					Any:
										return self.sp_model.IdToPiece(a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	List[str]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					""""""
										SCREAMING_SNAKE_CASE           =					False
										for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																if not prev_is_special:
																			out_string += " "
																out_string += self.sp_model.decode(a			) + token
																SCREAMING_SNAKE_CASE           =					True
																SCREAMING_SNAKE_CASE           =					[]
													else:
																current_sub_tokens.append(a			)
																SCREAMING_SNAKE_CASE           =					False
										out_string += self.sp_model.decode(a			)
										return out_string.strip()





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[self.cls_token_id]
										if token_ids_a is None:
													return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None       ,     a       :	bool = False			)			->					List[int]:

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

										if token_ids_a is not None:
													return [1] + ([0] * len(a			)) + [1] + ([0] * len(a			)) + [1]
										return [1] + ([0] * len(a			)) + [1]





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[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			) * [0] + len(token_ids_a + sep			) * [1]







							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										if not os.path.isdir(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
													return
										SCREAMING_SNAKE_CASE           =					os.path.join(
										    a       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

										return (out_vocab_file,)












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

import requests
from bsa import BeautifulSoup

__A						:							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"""
}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE = "dhaka"					,	SCREAMING_SNAKE_CASE = 5   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					min(SCREAMING_SNAKE_CASE					,	50   )  # Prevent abuse!
			SCREAMING_SNAKE_CASE           =					{
			    """q""": query,
			    """tbm""": """isch""",
			    """hl""": """en""",
			    """ijn""": """0""",
			}

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

			SCREAMING_SNAKE_CASE           =					json.dumps(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					json.loads(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					re.findall(
			    r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""					,	SCREAMING_SNAKE_CASE					,	)
			if not matched_google_image_data:
						return 0

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

			SCREAMING_SNAKE_CASE           =					re.findall(
			    r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""					,	SCREAMING_SNAKE_CASE					,	)
			for index, fixed_full_res_image in enumerate(SCREAMING_SNAKE_CASE   ):
						if index >= max_images:
									return index
						SCREAMING_SNAKE_CASE           =					bytes(SCREAMING_SNAKE_CASE					,	"""ascii"""   ).decode(
						    """unicode-escape"""   )
						SCREAMING_SNAKE_CASE           =					bytes(SCREAMING_SNAKE_CASE					,	"""ascii"""   ).decode(
						    """unicode-escape"""   )
						SCREAMING_SNAKE_CASE           =					urllib.request.build_opener()
						SCREAMING_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""",
						    )
						]
						urllib.request.install_opener(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					f"""query_{query.replace(" "					,	"_"   )}"""
						if not os.path.exists(SCREAMING_SNAKE_CASE   ):
									os.makedirs(SCREAMING_SNAKE_CASE   )
						urllib.request.urlretrieve(  # noqa: S310
						    SCREAMING_SNAKE_CASE					,	f"""{path_name}/original_size_img_{index}.jpg"""   )
			return index


if __name__ == "__main__":
		try:
				__A						:							List[Any]							=  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






