

'''simple docstring'''

import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			[
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   RetriBertConfig
				snake_case_           =   None
				snake_case_           =   '''retribert'''

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       List[str]:
					if isinstance(lowercase_,	nn.Linear   ):
						module.weight.data.normal_(mean=0.0,	std=self.config.initializer_range   )
						if module.bias is not None:
							module.bias.data.zero_()
					elif isinstance(lowercase_,	nn.Embedding   ):
						module.weight.data.normal_(mean=0.0,	std=self.config.initializer_range   )
						if module.padding_idx is not None:
							module.weight.data[module.padding_idx].zero_()
					elif isinstance(lowercase_,	nn.LayerNorm   ):
						module.bias.data.zero_()
						module.weight.data.fill_(1.0   )
lowerCAmelCase_						   =			r"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"







@add_start_docstrings(
    '''Bert Based model to embed queries or document for document retrieval.'''							,							__lowerCAmelCase							,							)
class lowerCamelCase      (						__lowerCAmelCase							):

				def __init__(					self,	lowercase_   )						->       None:
					super().__init__(lowercase_   )
					snake_case							 =				config.projection_dim

					snake_case							 =				BertModel(lowercase_   )
					snake_case							 =				None if config.share_encoders else BertModel(lowercase_   )
					snake_case							 =				nn.Dropout(config.hidden_dropout_prob   )
					snake_case							 =				nn.Linear(config.hidden_size,	config.projection_dim,	bias=lowercase_   )
					snake_case							 =				nn.Linear(config.hidden_size,	config.projection_dim,	bias=lowercase_   )

					snake_case							 =				nn.CrossEntropyLoss(reduction='mean'   )

					# Initialize weights and apply final processing
					self.post_init()

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_=-1,	)						->       Any:
					# reproduces BERT forward pass with checkpointing
					if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
						return sent_encoder(lowercase_,	attention_mask=lowercase_   )[1]
					else:
						# prepare implicit variables
						snake_case							 =				input_ids.device
						snake_case							 =				input_ids.size()
						snake_case							 =				torch.zeros(lowercase_,	dtype=torch.long,	device=lowercase_   )
						snake_case							 =				[None] * sent_encoder.config.num_hidden_layers
						snake_case							 =				sent_encoder.get_extended_attention_mask(
						    lowercase_,	lowercase_   )

						# define function for checkpointing
						def partial_encode(*lowercase_   ):
							snake_case							 =				sent_encoder.encoder(
							    inputs[0],	attention_mask=inputs[1],	head_mask=lowercase_,	)
							snake_case							 =				encoder_outputs[0]
							snake_case							 =				sent_encoder.pooler(lowercase_   )
							return pooled_output

						# run embedding layer on everything at once
						snake_case							 =				sent_encoder.embeddings(
						    input_ids=lowercase_,	position_ids=lowercase_,	token_type_ids=lowercase_,	inputs_embeds=lowercase_   )
						# run encoding and pooling on one mini-batch at a time
						snake_case							 =				[]
						for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
							snake_case							 =				embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
							snake_case							 =				extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
							snake_case							 =				checkpoint.checkpoint(lowercase_,	lowercase_,	lowercase_   )
							pooled_output_list.append(lowercase_   )
						return torch.cat(lowercase_,	dim=0   )

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_=None,	lowercase_=-1,	)						->       Dict:
					snake_case							 =				self.embed_sentences_checkpointed(
					    lowercase_,	lowercase_,	self.bert_query,	lowercase_,	)
					return self.project_query(lowercase_   )

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_=None,	lowercase_=-1,	)						->       Tuple:
					snake_case							 =				self.embed_sentences_checkpointed(
					    lowercase_,	lowercase_,	self.bert_query if self.bert_doc is None else self.bert_doc,	lowercase_,	)
					return self.project_doc(lowercase_   )






				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_,	lowercase_ = -1,	)						->       torch.FloatTensor:
					snake_case							 =				input_ids_query.device
					snake_case							 =				self.embed_questions(lowercase_,	lowercase_,	lowercase_   )
					snake_case							 =				self.embed_answers(lowercase_,	lowercase_,	lowercase_   )
					snake_case							 =				torch.mm(lowercase_,	a_reps.t()   )
					snake_case							 =				self.ce_loss(lowercase_,	torch.arange(compare_scores.shape[1]   ).to(lowercase_   )   )
					snake_case							 =				self.ce_loss(compare_scores.t(),	torch.arange(compare_scores.shape[0]   ).to(lowercase_   )   )
					snake_case							 =				(loss_qa + loss_aq) / 2
					return loss








'''simple docstring'''

import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCAmelCase_						   =			{"UserAgent": UserAgent().random}
lowerCAmelCase_						   =			"https://www.mywaifulist.moe/random"

def     __magic_name__       ( A					,   A							)			->		None:
	snake_case							 =				requests.get(A					,   headers=A							)
	with open(A					,   'wb'							) as file:
		file.write(image.content							)

def     __magic_name__       ( )			->		tuple[str, str, str]:
	snake_case							 =				BeautifulSoup(requests.get(A					,   headers=A							).text					,   'html.parser'							)
	snake_case							 =				soup.find('meta'					,   attrs={'property': 'og:title'}							).attrs['content']
	snake_case							 =				soup.find('meta'					,   attrs={'property': 'og:image'}							).attrs['content']
	snake_case							 =				soup.find('p'					,   id='description'							).get_text()
	snake_case , snake_case							 =				os.path.splitext(os.path.basename(A							)							)
	snake_case							 =				title.strip().replace(' '					,   '_'							)
	snake_case							 =				F'''{image_title}{image_extension}'''
	save_image(A					,   A							)
	return (title, description, image_title)


if __name__ == "__main__":
						lowerCAmelCase_   ,	lowerCAmelCase_   ,	lowerCAmelCase_						   =			random_anime_character()
						print(f"{title}\n\n{desc}\n\nImage saved : {image_title}")





