





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    :					Tuple =      logging.get_logger(__name__)

A    :					Dict =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    '''canine'''






			def __init__(			self   :    Dict							,	__lowerCAmelCase   :    int=7_68							,	__lowerCAmelCase   :    Optional[int]=12							,	__lowerCAmelCase   :    Optional[Any]=12							,	__lowerCAmelCase   :    int=30_72							,	__lowerCAmelCase   :    Union[str, Any]="gelu"							,	__lowerCAmelCase   :    Any=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Optional[int]=1_63_84							,	__lowerCAmelCase   :    Optional[Any]=16							,	__lowerCAmelCase   :    Any=0.0_2							,	__lowerCAmelCase   :    List[Any]=1e-12							,	__lowerCAmelCase   :    Optional[int]=0							,	__lowerCAmelCase   :    Optional[Any]=0XE000							,	__lowerCAmelCase   :    int=0XE001							,	__lowerCAmelCase   :    List[Any]=4							,	__lowerCAmelCase   :    Union[str, Any]=4							,	__lowerCAmelCase   :    Union[str, Any]=8							,	__lowerCAmelCase   :    Any=1_63_84							,	__lowerCAmelCase   :    List[Any]=1_28							,	**__lowerCAmelCase   :    int							,	)	->	List[str]:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase   )

					A__        =   max_position_embeddings
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   intermediate_size
					A__        =   hidden_act
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   initializer_range
					A__        =   type_vocab_size
					A__        =   layer_norm_eps

					# Character config:
					A__        =   downsampling_rate
					A__        =   upsampling_kernel_size
					A__        =   num_hash_functions
					A__        =   num_hash_buckets
					A__        =   local_transformer_stride










from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
					A    :					List[str] =      input('''Enter image url: ''').strip()
					print(F'''Downloading image from {url} ...''')
					A    :					Any =      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    :					List[Any] =      soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
					A    :					Dict =      requests.get(image_url).content
					A    :					Tuple =      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}.''')



