






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

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


if is_sentencepiece_available():
				from .tokenization_albert import AlbertTokenizer
else:
				A        =     None

A        =     logging.get_logger(__name__)
A        =     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A        =     {
    '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',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

A        =     {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

A        =     '▁'


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[Any]				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				Tuple				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Any				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Dict				    =						AlbertTokenizer


		def __init__(						self ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    UpperCamelCase__=True ,    UpperCamelCase__=True ,    UpperCamelCase__=False ,    UpperCamelCase__="[CLS]" ,    UpperCamelCase__="[SEP]" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="[SEP]" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__="[CLS]" ,    UpperCamelCase__="[MASK]" ,    **UpperCamelCase__ ,    ):
						# 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_   :	Union[str, Any]													=   (
						    AddedToken(UpperCamelCase__ ,    lstrip=UpperCamelCase__ ,    rstrip=UpperCamelCase__ ,    normalized=UpperCamelCase__       )
						    if isinstance(UpperCamelCase__ ,    UpperCamelCase__       )
						    else mask_token
						)

						super().__init__(
						    UpperCamelCase__ ,    tokenizer_file=UpperCamelCase__ ,    do_lower_case=UpperCamelCase__ ,    remove_space=UpperCamelCase__ ,    keep_accents=UpperCamelCase__ ,    bos_token=UpperCamelCase__ ,    eos_token=UpperCamelCase__ ,    unk_token=UpperCamelCase__ ,    sep_token=UpperCamelCase__ ,    pad_token=UpperCamelCase__ ,    cls_token=UpperCamelCase__ ,    mask_token=UpperCamelCase__ ,    **UpperCamelCase__ ,    )

						SCREAMING_SNAKE_CASE_   :	str													=   do_lower_case
						SCREAMING_SNAKE_CASE_   :	Tuple													=   remove_space
						SCREAMING_SNAKE_CASE_   :	Tuple													=   keep_accents
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   vocab_file
						SCREAMING_SNAKE_CASE_   :	Any													=   False if not self.vocab_file else True


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   [self.sep_token_id]
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   [self.sep_token_id]
						SCREAMING_SNAKE_CASE_   :	Tuple													=   [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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						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
						SCREAMING_SNAKE_CASE_   :	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,)







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A        =			input('Enter image url: ').strip()
				print(f"""Downloading image from {url} ...""")
				A        =			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A        =			soup.find('meta', {'property': 'og:image'})['content']
				A        =			requests.get(image_url).content
				A        =			f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(f"""Done. Image saved to disk as {file_name}.""")