


"""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


A				:   int		= logging.get_logger(__name__)

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







class       _lowercase			(							A__):


       """simple docstring"""




       A__ 						=      RetriBertConfig
       A__ 						=      None
       A__ 						=      "retribert"





       def 						lowerCAmelCase					(       self							:      Union[str, Any] ,				__lowerCamelCase							:      Optional[int]	):


             '''simple docstring'''



             if isinstance(snake_case_ ,				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(snake_case_ ,				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(snake_case_ ,				nn.LayerNorm	):
                   module.bias.data.zero_()
                   module.weight.data.fill_(1.0	)



A				:   Any		= 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."							,  A__							,  )
class       _lowercase			(							A__):


       """simple docstring"""




       def __init__(       self							:      List[Any] ,				__lowerCamelCase							:      Tuple	):


             '''simple docstring'''



             super().__init__(snake_case_	)
             lowerCamelCase__		:    Union[str, Any]   =  config.projection_dim

             lowerCamelCase__		:    Any   =  BertModel(snake_case_	)
             lowerCamelCase__		:    Optional[int]   =  None if config.share_encoders else BertModel(snake_case_	)
             lowerCamelCase__		:    Dict   =  nn.Dropout(config.hidden_dropout_prob	)
             lowerCamelCase__		:    Any   =  nn.Linear(config.hidden_size ,				config.projection_dim ,				bias=snake_case_	)
             lowerCamelCase__		:    Any   =  nn.Linear(config.hidden_size ,				config.projection_dim ,				bias=snake_case_	)

             lowerCamelCase__		:    Optional[Any]   =  nn.CrossEntropyLoss(reduction="mean"	)

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





       def 						lowerCAmelCase					(       self							:      int ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Optional[int] ,				__lowerCamelCase							:      Any=-1 ,				):


             '''simple docstring'''



             if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
                   return sent_encoder(snake_case_ ,				attention_mask=snake_case_	)[1]
             else:
                   # prepare implicit variables
                   lowerCamelCase__		:    Any   =  input_ids.device
                   lowerCamelCase__		:    List[str]   =  input_ids.size()
                   lowerCamelCase__		:    Union[str, Any]   =  torch.zeros(snake_case_ ,				dtype=torch.long ,				device=snake_case_	)
                   lowerCamelCase__		:    List[str]   =  [None] * sent_encoder.config.num_hidden_layers
                   lowerCamelCase__		:    Tuple   =  sent_encoder.get_extended_attention_mask(
                       snake_case_ ,				snake_case_	)

                   # define function for checkpointing
                   def partial_encode(*__lowerCamelCase							:      int	):
                         lowerCamelCase__		:    str   =  sent_encoder.encoder(
                             inputs[0] ,				attention_mask=inputs[1] ,				head_mask=snake_case_ ,				)
                         lowerCamelCase__		:    List[Any]   =  encoder_outputs[0]
                         lowerCamelCase__		:    Any   =  sent_encoder.pooler(snake_case_	)
                         return pooled_output

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





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Any=None ,				__lowerCamelCase							:      int=-1 ,				):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  self.embed_sentences_checkpointed(
                 snake_case_ ,				snake_case_ ,				self.bert_query ,				snake_case_ ,				)
             return self.project_query(snake_case_	)





       def 						lowerCAmelCase					(       self							:      Optional[Any] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      List[Any]=None ,				__lowerCamelCase							:      int=-1 ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  self.embed_sentences_checkpointed(
                 snake_case_ ,				snake_case_ ,				self.bert_query if self.bert_doc is None else self.bert_doc ,				snake_case_ ,				)
             return self.project_doc(snake_case_	)





       def 						lowerCAmelCase					(       self							:      Tuple ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      Optional[Any] = -1 ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  input_ids_query.device
             lowerCamelCase__		:    List[Any]   =  self.embed_questions(snake_case_ ,				snake_case_ ,				snake_case_	)
             lowerCamelCase__		:    str   =  self.embed_answers(snake_case_ ,				snake_case_ ,				snake_case_	)
             lowerCamelCase__		:    Tuple   =  torch.mm(snake_case_ ,				a_reps.t()	)
             lowerCamelCase__		:    Optional[Any]   =  self.ce_loss(snake_case_ ,				torch.arange(compare_scores.shape[1]	).to(snake_case_	)	)
             lowerCamelCase__		:    str   =  self.ce_loss(compare_scores.t() ,				torch.arange(compare_scores.shape[0]	).to(snake_case_	)	)
             lowerCamelCase__		:    Optional[int]   =  (loss_qa + loss_aq) / 2
             return loss






from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A				:   List[str]		= "https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true"

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      try:
            # Has user provided both inputs?
            if not drug_name or not zip_code:
                  return None

            lowerCamelCase__		:    List[str]   =  BASE_URL.format(_A   , _A						)
            lowerCamelCase__		:    int   =  get(_A						)

            # Is the response ok?
            response.raise_for_status()

            # Scrape the data using bs4
            lowerCamelCase__		:    Dict   =  BeautifulSoup(response.text   , "html.parser"						)

            # This list will store the name and price.
            lowerCamelCase__		:    int   =  []

            # Fetch all the grids that contains the items.
            lowerCamelCase__		:    List[str]   =  soup.find_all("div"   , {"class": "grid-x pharmCard"}						)
            if grid_list and len(_A						) > 0:
                  for grid in grid_list:
                        # Get the pharmacy price.
                        lowerCamelCase__		:    Optional[int]   =  grid.find("p"   , {"class": "list-title"}						).text

                        # Get price of the drug.
                        lowerCamelCase__		:    Union[str, Any]   =  grid.find("span"   , {"p", "price price-large"}						).text

                        pharmacy_price_list.append(
                            {
                                "pharmacy_name": pharmacy_name,
                                "price": price,
                            }						)

            return pharmacy_price_list

      except (HTTPError, exceptions.RequestException, ValueError):
            return None


if __name__ == "__main__":
    # Enter a drug name and a zip code
    A				:   Any		= input("Enter drug name: ").strip()
    A				:   Union[str, Any]		= input("Enter zip code: ").strip()

    A				:   list | None		= fetch_pharmacy_and_price_list(
        drug_name, zip_code
    )

    if pharmacy_price_list:
        print(f'\nSearch results for {drug_name} at location {zip_code}:')
        for pharmacy_price in pharmacy_price_list:
            A				:   Dict		= pharmacy_price["pharmacy_name"]
            A				:   Optional[int]		= pharmacy_price["price"]

            print(f'Pharmacy: {name} Price: {price}')
    else:
        print(f'No results found for {drug_name}')


