






'''simple docstring'''




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCamelCase_							      =      logging.getLogger(__name__)

@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,					)





@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()		)}		)
   snake_case					       =		field(
       metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}		)
   snake_case					       =		field(
       default=1_28							,					metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }							,					)
   snake_case					       =		field(
       default=snake_case_							,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def       __lowercase		(    __lowercase ) ->		Dict[str, torch.Tensor]:

      '''simple docstring'''


      _A      =			default_data_collator(__lowercase )
      _A      =			batch.pop("pairID"     ,						__lowercase )
      return batch

def       __lowercase		(    ) ->		Optional[Any]:

      '''simple docstring'''


      _A      =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
      _A  ,			_A  ,			_A      =			parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir )
          and os.listdir(training_args.output_dir )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                " --overwrite_output_dir to overcome." )

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,						datefmt="%m/%d/%Y %H:%M:%S"     ,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     ,						)
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"     ,						training_args.local_rank     ,						training_args.device     ,						training_args.n_gpu     ,						bool(training_args.local_rank != -1 )     ,						training_args.fpaa     ,						)
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank ):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s"     ,						__lowercase )

      # Set seed
      set_seed(training_args.seed )

      try:
            _A      =			hans_tasks_num_labels[data_args.task_name]
      except KeyError:
            raise ValueError("Task not found: %s" % (data_args.task_name) )

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      _A      =			AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,						num_labels=__lowercase     ,						finetuning_task=data_args.task_name     ,						cache_dir=model_args.cache_dir     ,						)
      _A      =			AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,						cache_dir=model_args.cache_dir     ,						)
      _A      =			AutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path     ,						from_tf=bool(".ckpt" in model_args.model_name_or_path )     ,						config=__lowercase     ,						cache_dir=model_args.cache_dir     ,						)

      # Get datasets
      _A      =			(
          HansDataset(
              data_dir=data_args.data_dir     ,						tokenizer=__lowercase     ,						task=data_args.task_name     ,						max_seq_length=data_args.max_seq_length     ,						overwrite_cache=data_args.overwrite_cache     ,						)
          if training_args.do_train
          else None
      )
      _A      =			(
          HansDataset(
              data_dir=data_args.data_dir     ,						tokenizer=__lowercase     ,						task=data_args.task_name     ,						max_seq_length=data_args.max_seq_length     ,						overwrite_cache=data_args.overwrite_cache     ,						evaluate=__lowercase     ,						)
          if training_args.do_eval
          else None
      )

      # Initialize our Trainer
      _A      =			Trainer(
          model=__lowercase     ,						args=__lowercase     ,						train_dataset=__lowercase     ,						eval_dataset=__lowercase     ,						data_collator=__lowercase     ,						)

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
      if training_args.do_eval:
            logger.info("*** Evaluate ***" )

            _A      =			trainer.predict(__lowercase )
            _A      =			output.predictions
            _A      =			np.argmax(__lowercase     ,						axis=1 )

            _A      =			[ex.pairID for ex in eval_dataset]
            _A      =			os.path.join(training_args.output_dir     ,						"hans_predictions.txt" )
            _A      =			eval_dataset.get_labels()
            if trainer.is_world_master():
                  with open(__lowercase     ,						"w" ) as writer:
                        writer.write("pairID,gold_label\n" )
                        for pid, pred in zip(__lowercase     ,						__lowercase ):
                              writer.write("ex" + str(__lowercase ) + "," + label_list[int(__lowercase )] + "\n" )

            trainer._log(output.metrics )

def       __lowercase		(    __lowercase ) ->		Tuple:

      '''simple docstring'''


      main()


if __name__ == "__main__":
    main()













'''simple docstring'''




from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

lowerCamelCase_							      =      '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''

def       __lowercase		(    __lowercase     ,						__lowercase ) ->		list | None:

      '''simple docstring'''


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

            _A      =			BASE_URL.format(__lowercase     ,						__lowercase )
            _A      =			get(__lowercase )

            # Is the response ok?
            response.raise_for_status()

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

            # This list will store the name and price.
            _A      =			[]

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

                        # Get price of the drug.
                        _A      =			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
    lowerCamelCase_							      =      input('''Enter drug name: ''').strip()
    lowerCamelCase_							      =      input('''Enter zip code: ''').strip()

    lowerCamelCase_							      =      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:
            lowerCamelCase_							      =      pharmacy_price['''pharmacy_name''']
            lowerCamelCase_							      =      pharmacy_price['''price''']

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





