


from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		str  ,			__lowerCamelCase:		Optional[Any]  ):







	'''simple docstring'''



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

		lowercase_			 = BASE_URL.format(lowerCAmelCase_  ,			lowerCAmelCase_  )
		lowercase_			 = get(lowerCAmelCase_  )

		# Is the response ok?
		response.raise_for_status()

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

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

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

				# Get price of the drug.
				lowercase_			 = 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
							SCREAMING_SNAKE_CASE__       			=				input("""Enter drug name: """).strip()
							SCREAMING_SNAKE_CASE__       			=				input("""Enter zip code: """).strip()

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

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






'''simple docstring'''







import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('SDP_ENABLED') or is_sagemaker_dp_enabled():
      lowercase__  :	Any            =      True
      lowercase__  :	Tuple            =      'p3dn.24xlarge'
      import smdistributed.dataparallel.tensorflow as sdp
else:
      lowercase__  :	Dict            =      False





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=None						):
    UpperCamelCase__	     =      tqdm(lowerCAmelCase_						)
    for i, batch in enumerate(lowerCAmelCase_						):
        with tf.GradientTape() as tape:
            UpperCamelCase__     ,	UpperCamelCase__	     =      batch
            UpperCamelCase__	     =      model(lowerCAmelCase_						)
            UpperCamelCase__	     =      loss(lowerCAmelCase_       ,	outputs.logits						)

        if SDP_ENABLED:
            UpperCamelCase__	     =      sdp.DistributedGradientTape(lowerCAmelCase_       ,	sparse_as_dense=lowerCAmelCase_						)

        UpperCamelCase__	     =      tape.gradient(lowerCAmelCase_       ,	model.trainable_variables						)
        opt.apply_gradients(zip(lowerCAmelCase_       ,	model.trainable_variables						)						)

        pbar.set_description(F'Loss: {loss_value:.4f}'						)

        if SDP_ENABLED and i == 0:
            sdp.broadcast_variables(model.variables       ,	root_rank=0						)
            sdp.broadcast_variables(opt.variables()       ,	root_rank=0						)

        if max_steps and i >= max_steps:
            break

    UpperCamelCase__	     =      {"loss": loss_value.numpy()}
    return train_results





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    # Load dataset
    UpperCamelCase__     ,	UpperCamelCase__	     =      load_dataset("imdb"       ,	split=["train", "test"]						)

    # Preprocess train dataset
    UpperCamelCase__	     =      train_dataset.map(
        lambda lowerCAmelCase_						: tokenizer(e["text"]       ,	truncation=lowerCAmelCase_       ,	padding="max_length"						)       ,	batched=lowerCAmelCase_						)
    train_dataset.set_format(type="tensorflow"       ,	columns=["input_ids", "attention_mask", "label"]						)

    UpperCamelCase__	     =      {
        x: train_dataset[x].to_tensor(default_value=0       ,	shape=[None, tokenizer.model_max_length]						)
        for x in ["input_ids", "attention_mask"]
    }
    UpperCamelCase__	     =      tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])						)

    # Preprocess test dataset
    UpperCamelCase__	     =      test_dataset.map(
        lambda lowerCAmelCase_						: tokenizer(e["text"]       ,	truncation=lowerCAmelCase_       ,	padding="max_length"						)       ,	batched=lowerCAmelCase_						)
    test_dataset.set_format(type="tensorflow"       ,	columns=["input_ids", "attention_mask", "label"]						)

    UpperCamelCase__	     =      {
        x: test_dataset[x].to_tensor(default_value=0       ,	shape=[None, tokenizer.model_max_length]						)
        for x in ["input_ids", "attention_mask"]
    }
    UpperCamelCase__	     =      tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])						)

    if SDP_ENABLED:
        UpperCamelCase__	     =      tf_train_dataset.shard(sdp.size()       ,	sdp.rank()						)
        UpperCamelCase__	     =      tf_test_dataset.shard(sdp.size()       ,	sdp.rank()						)
    UpperCamelCase__	     =      tf_train_dataset.batch(lowerCAmelCase_       ,	drop_remainder=lowerCAmelCase_						)
    UpperCamelCase__	     =      tf_test_dataset.batch(lowerCAmelCase_       ,	drop_remainder=lowerCAmelCase_						)

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
      lowercase__  :	Any            =      argparse.ArgumentParser()

      # Hyperparameters sent by the client are passed as command-line arguments to the script.
      parser.add_argument('--epochs', type=int, default=3)
      parser.add_argument('--per_device_train_batch_size', type=int, default=16)
      parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
      parser.add_argument('--model_name_or_path', type=str)
      parser.add_argument('--learning_rate', type=str, default=5e-5)
      parser.add_argument('--do_train', type=bool, default=True)
      parser.add_argument('--do_eval', type=bool, default=True)
      parser.add_argument('--output_dir', type=str)
      parser.add_argument('--max_steps', type=int, default=None)

      # Data, model, and output directories
      parser.add_argument('--output_data_dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
      parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
      parser.add_argument('--n_gpus', type=str, default=os.environ['SM_NUM_GPUS'])

      lowercase__       ,	lowercase__  :	Union[str, Any]            =      parser.parse_known_args()

      # Set up logging
      lowercase__  :	int            =      logging.getLogger(__name__)

      logging.basicConfig(
          level=logging.getLevelName('INFO'),
          handlers=[logging.StreamHandler(sys.stdout)],
          format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
      )

      if SDP_ENABLED:
            sdp.init()

            lowercase__  :	Union[str, Any]            =      tf.config.experimental.list_physical_devices('GPU')
            for gpu in gpus:
                  tf.config.experimental.set_memory_growth(gpu, True)
            if gpus:
                  tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], 'GPU')

    # Load model and tokenizer
      lowercase__  :	int            =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      lowercase__  :	Any            =      AutoTokenizer.from_pretrained(args.model_name_or_path)

      # get datasets
      lowercase__       ,	lowercase__  :	Dict            =      get_datasets(
          tokenizer=tokenizer,
          train_batch_size=args.per_device_train_batch_size,
          eval_batch_size=args.per_device_eval_batch_size,
      )

      # fine optimizer and loss
      lowercase__  :	Any            =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      lowercase__  :	Tuple            =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      lowercase__  :	Tuple            =      [tf.keras.metrics.SparseCategoricalAccuracy()]
      model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

      # Training
      if args.do_train:
            # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
            lowercase__  :	List[str]            =      time.time()
            lowercase__  :	Union[str, Any]            =      fit(
                model,
                loss,
                optimizer,
                tf_train_dataset,
                args.epochs,
                args.per_device_train_batch_size,
                max_steps=args.max_steps,
            )
            lowercase__  :	Dict            =      time.time() - start_train_time
            logger.info('*** Train ***')
            logger.info(F'''train_runtime = {end_train_time}''')

            lowercase__  :	List[str]            =      os.path.join(args.output_dir, 'train_results.txt')

            if not SDP_ENABLED or sdp.rank() == 0:
                  with open(output_eval_file, 'w') as writer:
                        logger.info('***** Train results *****')
                        logger.info(train_results)
                        for key, value in train_results.items():
                              logger.info(F'''  {key} = {value}''')
                              writer.write(F'''{key} = {value}\n''')

    # Evaluation
      if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
            lowercase__  :	Tuple            =      model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
            logger.info('*** Evaluate ***')

            lowercase__  :	Any            =      os.path.join(args.output_dir, 'eval_results.txt')

            with open(output_eval_file, 'w') as writer:
                  logger.info('***** Eval results *****')
                  logger.info(result)
                  for key, value in result.items():
                        logger.info(F'''  {key} = {value}''')
                        writer.write(F'''{key} = {value}\n''')

    # Save result
      if SDP_ENABLED:
            if sdp.rank() == 0:
                  model.save_pretrained(args.output_dir)
                  tokenizer.save_pretrained(args.output_dir)
      else:
            model.save_pretrained(args.output_dir)
            tokenizer.save_pretrained(args.output_dir)




