






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 							__a							(  __SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE						):





		'''simple docstring'''



		@register_to_config
		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = False ,    ):
						super().__init__()

						SCREAMING_SNAKE_CASE_   :	Tuple													=   nn.Embedding(_a ,    _a       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   nn.Embedding(_a ,    _a       )
						SCREAMING_SNAKE_CASE_   :	int													=   False

						SCREAMING_SNAKE_CASE_   :	Tuple													=   nn.Dropout(p=_a       )

						SCREAMING_SNAKE_CASE_   :	int													=   TaConfig(
						    vocab_size=_a ,    d_model=_a ,    num_heads=_a ,    d_kv=_a ,    d_ff=_a ,    dropout_rate=_a ,    feed_forward_proj=_a ,    is_decoder=_a ,    is_encoder_decoder=_a ,    )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   nn.ModuleList()
						for lyr_num in range(_a       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   TaBlock(_a       )
										self.encoders.append(_a       )

						SCREAMING_SNAKE_CASE_   :	str													=   TaLayerNorm(_a       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   nn.Dropout(p=_a       )






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.token_embedder(_a       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   encoder_input_tokens.shape[1]
						SCREAMING_SNAKE_CASE_   :	int													=   torch.arange(_a ,    device=encoder_input_tokens.device       )
						x += self.position_encoding(_a       )

						SCREAMING_SNAKE_CASE_   :	Any													=   self.dropout_pre(_a       )

						# inverted the attention mask
						SCREAMING_SNAKE_CASE_   :	str													=   encoder_input_tokens.size()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_extended_attention_mask(_a ,    _a       )

						for lyr in self.encoders:
										SCREAMING_SNAKE_CASE_   :	Dict													=   lyr(_a ,    _a       )[0]
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.layer_norm(_a       )

						return self.dropout_post(_a       ), encoder_inputs_mask







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}.""")