



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


A__     				=	logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

A__     				=	{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
A__     				=	{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
A__     				=	sorted(arg_to_scheduler.keys())
A__     				=	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""





class  __lowerCAmelCase			(					pl.LightningModule	):




    def __init__(							self						,    _snake_case						,    _snake_case=None						,    _snake_case="base"						,    _snake_case=None						,    _snake_case=None						,    _snake_case=None						,    **_snake_case						,    ):



         """simple docstring"""





         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(_snake_case		)
         _lowerCAmelCase      =   0
         _lowerCAmelCase      =   Path(self.hparams.output_dir		)
         _lowerCAmelCase      =   self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
              _lowerCAmelCase      =   AutoConfig.from_pretrained(
                  self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path						,    **({"""num_labels""": num_labels} if num_labels is not None else {})						,    cache_dir=_snake_case						,    **_snake_case						,    )
         else:
              _lowerCAmelCase      =   config

         _lowerCAmelCase      =   ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
         for p in extra_model_params:
              if getattr(self.hparams						,    _snake_case						,    _snake_case		):
                   assert hasattr(self.config						,    _snake_case		), F'model config doesn\'t have a `{p}` attribute'
                   setattr(self.config						,    _snake_case						,    getattr(self.hparams						,    _snake_case		)		)

         if tokenizer is None:
              _lowerCAmelCase      =   AutoTokenizer.from_pretrained(
                  self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,    cache_dir=_snake_case						,    )
         else:
              _lowerCAmelCase      =   tokenizer
         _lowerCAmelCase      =   MODEL_MODES[mode]
         if model is None:
              _lowerCAmelCase      =   self.model_type.from_pretrained(
                  self.hparams.model_name_or_path						,    from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path		)						,    config=self.config						,    cache_dir=_snake_case						,    )
         else:
              _lowerCAmelCase      =   model




    def 							snake_case       (							self						,    *_snake_case						,    **_snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   self.model_type.from_pretrained(*_snake_case						,    **_snake_case		)




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   arg_to_scheduler[self.hparams.lr_scheduler]
         _lowerCAmelCase      =   get_schedule_func(
             self.opt						,    num_warmup_steps=self.hparams.warmup_steps						,    num_training_steps=self.total_steps()		)
         _lowerCAmelCase      =   {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
         return scheduler




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   self.model
         _lowerCAmelCase      =   ["""bias""", """LayerNorm.weight"""]
         _lowerCAmelCase      =   [
             {
                 """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
                 """weight_decay""": self.hparams.weight_decay,
             },
             {
                 """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)],
                 """weight_decay""": 0.0,
             },
         ]
         if self.hparams.adafactor:
              _lowerCAmelCase      =   Adafactor(
                  _snake_case						,    lr=self.hparams.learning_rate						,    scale_parameter=_snake_case						,    relative_step=_snake_case		)

         else:
              _lowerCAmelCase      =   AdamW(
                  _snake_case						,    lr=self.hparams.learning_rate						,    eps=self.hparams.adam_epsilon		)
         _lowerCAmelCase      =   optimizer

         _lowerCAmelCase      =   self.get_lr_scheduler()

         return [optimizer], [scheduler]




    def 							snake_case       (							self						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         return self.validation_step(_snake_case						,    _snake_case		)




    def 							snake_case       (							self						,    _snake_case		):



         """simple docstring"""





         return self.validation_end(_snake_case		)




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   max(1						,    self.hparams.gpus		)  # TODO: consider num_tpu_cores
         _lowerCAmelCase      =   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




    def 							snake_case       (							self						,    _snake_case		):



         """simple docstring"""





         if mode == "test":
              _lowerCAmelCase      =   len(self.test_dataloader().dataset		)
         else:
              _lowerCAmelCase      =   self.get_dataloader("""train"""						,    self.hparams.train_batch_size						,    shuffle=_snake_case		)
              _lowerCAmelCase      =   len(self.train_dataloader().dataset		)




    def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case = False		):



         """simple docstring"""





         raise NotImplementedError("""You must implement this for your task"""		)




    def 							snake_case       (							self		):



         """simple docstring"""





         return self.train_loader




    def 							snake_case       (							self		):



         """simple docstring"""





         return self.get_dataloader("""dev"""						,    self.hparams.eval_batch_size						,    shuffle=_snake_case		)




    def 							snake_case       (							self		):



         """simple docstring"""





         return self.get_dataloader("""test"""						,    self.hparams.eval_batch_size						,    shuffle=_snake_case		)




    def 							snake_case       (							self						,    _snake_case		):



         """simple docstring"""





         return os.path.join(
             self.hparams.data_dir						,    """cached_{}_{}_{}""".format(
                 _snake_case						,    list(filter(_snake_case						,    self.hparams.model_name_or_path.split("""/"""		)		)		).pop()						,    str(self.hparams.max_seq_length		)						,    )						,    )




    @pl.utilities.rank_zero_only
    def 							snake_case       (							self						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   self.output_dir.joinpath("""best_tfmr"""		)
         _lowerCAmelCase      =   self.step_count
         self.model.save_pretrained(_snake_case		)
         self.tokenizer.save_pretrained(_snake_case		)




    @staticmethod
    def 							snake_case       (							_snake_case						,    _snake_case		):



         """simple docstring"""





         parser.add_argument(
             """--model_name_or_path"""						,    default=_snake_case						,    type=_snake_case						,    required=_snake_case						,    help="""Path to pretrained model or model identifier from huggingface.co/models"""						,    )
         parser.add_argument(
             """--config_name"""						,    default=""""""						,    type=_snake_case						,    help="""Pretrained config name or path if not the same as model_name"""		)
         parser.add_argument(
             """--tokenizer_name"""						,    default=_snake_case						,    type=_snake_case						,    help="""Pretrained tokenizer name or path if not the same as model_name"""						,    )
         parser.add_argument(
             """--cache_dir"""						,    default=""""""						,    type=_snake_case						,    help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""						,    )
         parser.add_argument(
             """--encoder_layerdrop"""						,    type=_snake_case						,    help="""Encoder layer dropout probability (Optional). Goes into model.config"""						,    )
         parser.add_argument(
             """--decoder_layerdrop"""						,    type=_snake_case						,    help="""Decoder layer dropout probability (Optional). Goes into model.config"""						,    )
         parser.add_argument(
             """--dropout"""						,    type=_snake_case						,    help="""Dropout probability (Optional). Goes into model.config"""						,    )
         parser.add_argument(
             """--attention_dropout"""						,    type=_snake_case						,    help="""Attention dropout probability (Optional). Goes into model.config"""						,    )
         parser.add_argument("""--learning_rate"""						,    default=5e-5						,    type=_snake_case						,    help="""The initial learning rate for Adam."""		)
         parser.add_argument(
             """--lr_scheduler"""						,    default="""linear"""						,    choices=_snake_case						,    metavar=_snake_case						,    type=_snake_case						,    help="""Learning rate scheduler"""						,    )
         parser.add_argument("""--weight_decay"""						,    default=0.0						,    type=_snake_case						,    help="""Weight decay if we apply some."""		)
         parser.add_argument("""--adam_epsilon"""						,    default=1e-8						,    type=_snake_case						,    help="""Epsilon for Adam optimizer."""		)
         parser.add_argument("""--warmup_steps"""						,    default=0						,    type=_snake_case						,    help="""Linear warmup over warmup_steps."""		)
         parser.add_argument("""--num_workers"""						,    default=4						,    type=_snake_case						,    help="""kwarg passed to DataLoader"""		)
         parser.add_argument("""--num_train_epochs"""						,    dest="""max_epochs"""						,    default=3						,    type=_snake_case		)
         parser.add_argument("""--train_batch_size"""						,    default=32						,    type=_snake_case		)
         parser.add_argument("""--eval_batch_size"""						,    default=32						,    type=_snake_case		)
         parser.add_argument("""--adafactor"""						,    action="""store_true"""		)





class  __lowerCAmelCase			(					pl.Callback	):




    def 							snake_case       (							self						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   trainer.lr_schedulers[0]["""scheduler"""]
         _lowerCAmelCase      =   {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
         pl_module.logger.log_metrics(_snake_case		)




    def 							snake_case       (							self						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         rank_zero_info("""***** Validation results *****"""		)
         _lowerCAmelCase      =   trainer.callback_metrics
         # Log results
         for key in sorted(_snake_case		):
              if key not in ["log", "progress_bar"]:
                   rank_zero_info("""{} = {}\n""".format(_snake_case						,    str(metrics[key]		)		)		)




    def 							snake_case       (							self						,    _snake_case						,    _snake_case		):



         """simple docstring"""





         rank_zero_info("""***** Test results *****"""		)
         _lowerCAmelCase      =   trainer.callback_metrics
         # Log and save results to file
         _lowerCAmelCase      =   os.path.join(pl_module.hparams.output_dir						,    """test_results.txt"""		)
         with open(_snake_case						,    """w"""		) as writer:
              for key in sorted(_snake_case		):
                   if key not in ["log", "progress_bar"]:
                        rank_zero_info("""{} = {}\n""".format(_snake_case						,    str(metrics[key]		)		)		)
                        writer.write("""{} = {}\n""".format(_snake_case						,    str(metrics[key]		)		)		)


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





     """simple docstring"""

     parser.add_argument(
         """--output_dir"""     ,						default=snake_case     ,						type=snake_case     ,						required=snake_case     ,						help="""The output directory where the model predictions and checkpoints will be written."""     ,						)
     parser.add_argument(
         """--fp16"""     ,						action="""store_true"""     ,						help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""     ,						)

     parser.add_argument(
         """--fp16_opt_level"""     ,						type=snake_case     ,						default="""O2"""     ,						help=(
             """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
             """See details at https://nvidia.github.io/apex/amp.html"""
         )     ,						)
     parser.add_argument("""--n_tpu_cores"""     ,						dest="""tpu_cores"""     ,						type=snake_case      )
     parser.add_argument("""--max_grad_norm"""     ,						dest="""gradient_clip_val"""     ,						default=1.0     ,						type=snake_case     ,						help="""Max gradient norm"""      )
     parser.add_argument("""--do_train"""     ,						action="""store_true"""     ,						help="""Whether to run training."""      )
     parser.add_argument("""--do_predict"""     ,						action="""store_true"""     ,						help="""Whether to run predictions on the test set."""      )
     parser.add_argument(
         """--gradient_accumulation_steps"""     ,						dest="""accumulate_grad_batches"""     ,						type=snake_case     ,						default=1     ,						help="""Number of updates steps to accumulate before performing a backward/update pass."""     ,						)
     parser.add_argument("""--seed"""     ,						type=snake_case     ,						default=42     ,						help="""random seed for initialization"""      )
     parser.add_argument(
         """--data_dir"""     ,						default=snake_case     ,						type=snake_case     ,						required=snake_case     ,						help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""     ,						)


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case=None     ,						snake_case=True     ,						snake_case=[]     ,						snake_case=None     ,						snake_case=None     ,						**snake_case     ,						):





     """simple docstring"""

     pl.seed_everything(args.seed      )

     # init model
     _lowerCAmelCase      =   Path(model.hparams.output_dir      )
     odir.mkdir(exist_ok=snake_case      )

     # add custom checkpoints
     if checkpoint_callback is None:
          _lowerCAmelCase      =   pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir     ,						prefix="""checkpoint"""     ,						monitor="""val_loss"""     ,						mode="""min"""     ,						save_top_k=1      )
     if early_stopping_callback:
          extra_callbacks.append(snake_case      )
     if logging_callback is None:
          _lowerCAmelCase      =   LoggingCallback()

     _lowerCAmelCase      =   {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _lowerCAmelCase      =   16
          _lowerCAmelCase      =   args.fpaa_opt_level

     if args.gpus > 1:
          _lowerCAmelCase      =   """ddp"""

     _lowerCAmelCase      =   args.accumulate_grad_batches
     _lowerCAmelCase      =   extra_train_kwargs.get("""accelerator"""     ,						snake_case      )
     _lowerCAmelCase      =   extra_train_kwargs.get("""profiler"""     ,						snake_case      )

     _lowerCAmelCase      =   pl.Trainer.from_argparse_args(
         snake_case     ,						weights_summary=snake_case     ,						callbacks=[logging_callback] + extra_callbacks     ,						logger=snake_case     ,						checkpoint_callback=snake_case     ,						**snake_case     ,						)

     if args.do_train:
          trainer.fit(snake_case      )

     return trainer








from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

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

										_lowerCAmelCase      =   BASE_URL.format(snake_case     ,						snake_case      )
										_lowerCAmelCase      =   get(snake_case      )

										# Is the response ok?
										response.raise_for_status()

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

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

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

																				# Get price of the drug.
																				_lowerCAmelCase      =   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__     				=	input("""Enter drug name: """).strip()
	A__     				=	input("""Enter zip code: """).strip()

	A__     				=	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__     				=	pharmacy_price["""pharmacy_name"""]
			A__     				=	pharmacy_price["""price"""]

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



