


import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
    from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
    A				:   List[Any]		= True
    from torch.cuda.amp import autocast


A				:   int		= logging.getLogger(__name__)







@dataclass
class       _lowercase			:


       """simple docstring"""




       A__ 						=      field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Whether to freeze the feature extractor layers of the model."})
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Whether to log verbose messages or not."}							,  )



def 				lowercase_		(		_A      :   ModelArguments   , _A      :   TrainingArguments						):

      """simple docstring"""




      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   , datefmt="%m/%d/%Y %H:%M:%S"   , handlers=[logging.StreamHandler(sys.stdout						)]   , )
      lowerCamelCase__		:    Any   =  logging.WARNING
      if model_args.verbose_logging:
            lowerCamelCase__		:    int   =  logging.DEBUG
      elif trainer_utils.is_main_process(training_args.local_rank						):
            lowerCamelCase__		:    int   =  logging.INFO
      logger.setLevel(_A						)







@dataclass
class       _lowercase			:


       """simple docstring"""




       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "The name of the dataset to use (via the datasets library)."})
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
       A__ 						=      field(
           default="train"							,  metadata={
               "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
           }							,  )
       A__ 						=      field(
           default="validation"							,  metadata={
               "help": (
                   "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
               )
           }							,  )
       A__ 						=      field(
           default="text"							,  metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"}							,  )
       A__ 						=      field(
           default="file"							,  metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."}							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."}							,  )
       A__ 						=      field(
           default="librispeech"							,  metadata={
               "help": (
                   "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"
                   " 'buckwalter'."
               )
           }							,  )
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "Overwrite the cached preprocessed datasets or not."})
       A__ 						=      field(
           default=lowercase__							,  metadata={"help": "The number of processes to use for the preprocessing."}							,  )







@dataclass
class       _lowercase			:


       """simple docstring"""




       A__ 						=      False
       A__ 						=      None
       A__ 						=      "|"
       A__ 						=      field(default_factory=lowercase__)
       A__ 						=      field(default_factory=lowercase__)
       A__ 						=      None





       @classmethod
       def 						lowerCAmelCase					(       cls							:      Optional[Any] ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             if name == "librispeech":
                   return cls()
             if name == "timit":
                   return cls(
                       do_lower_case=__lowerCamelCase ,				translation_table=str.maketrans({"-": " "}	) ,				)
             if name == "buckwalter":
                   lowerCamelCase__		:    List[Any]   =  {
                       "-": " ",  # sometimes used to represent pauses
                       "^": "v",  # fixing "tha" in arabic_speech_corpus dataset
                   }
                   return cls(
                       vocab_file=pathlib.Path(__lowerCamelCase	).parent.joinpath("vocab/buckwalter.json"	) ,				word_delimiter_token="/" ,				translation_table=str.maketrans(__lowerCamelCase	) ,				words_to_remove={"sil"} ,				untransliterator=arabic.buckwalter.untransliterate ,				)
             raise ValueError(f"Unsupported orthography: '{name}'."	)





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             if len(self.translation_table	) > 0:
                   lowerCamelCase__		:    str   =  text.translate(self.translation_table	)
             if len(self.words_to_remove	) == 0:
                   lowerCamelCase__		:    Optional[int]   =  " ".join(text.split()	)  # clean up whitespaces
             else:
                   lowerCamelCase__		:    Optional[Any]   =  " ".join(w for w in text.split() if w not in self.words_to_remove	)  # and clean up whilespaces
             return text





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


             '''simple docstring'''



             lowerCamelCase__		:    str   =  WavaVecaFeatureExtractor.from_pretrained(
                 model_args.model_name_or_path ,				cache_dir=model_args.cache_dir	)
             if self.vocab_file:
                   lowerCamelCase__		:    str   =  WavaVecaCTCTokenizer(
                       self.vocab_file ,				cache_dir=model_args.cache_dir ,				do_lower_case=self.do_lower_case ,				word_delimiter_token=self.word_delimiter_token ,				)
             else:
                   lowerCamelCase__		:    str   =  WavaVecaCTCTokenizer.from_pretrained(
                       model_args.model_name_or_path ,				cache_dir=model_args.cache_dir ,				do_lower_case=self.do_lower_case ,				word_delimiter_token=self.word_delimiter_token ,				)
             return WavaVecaProcessor(__lowerCamelCase ,				__lowerCamelCase	)







@dataclass
class       _lowercase			:


       """simple docstring"""




       A__ 						=      42
       A__ 						=      True
       A__ 						=      None
       A__ 						=      None
       A__ 						=      None
       A__ 						=      None





       def __call__(       self							:      str ,				__lowerCamelCase							:      List[Dict[str, Union[List[int], torch.Tensor]]]	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  [{"input_values": feature["input_values"]} for feature in features]
             lowerCamelCase__		:    Dict   =  [{"input_ids": feature["labels"]} for feature in features]

             lowerCamelCase__		:    str   =  self.processor.pad(
                 __lowerCamelCase ,				padding=self.padding ,				max_length=self.max_length ,				pad_to_multiple_of=self.pad_to_multiple_of ,				return_tensors="pt" ,				)
             lowerCamelCase__		:    Optional[int]   =  self.processor.pad(
                 labels=__lowerCamelCase ,				padding=self.padding ,				max_length=self.max_length_labels ,				pad_to_multiple_of=self.pad_to_multiple_of_labels ,				return_tensors="pt" ,				)

             # replace padding with -100 to ignore loss correctly
             lowerCamelCase__		:    str   =  labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1	) ,				-100	)

             lowerCamelCase__		:    int   =  labels

             return batch







class       _lowercase			(							lowercase__):


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      Tuple ,				__lowerCamelCase							:      nn.Module ,				__lowerCamelCase							:      Dict[str, Union[torch.Tensor, Any]]	):


             '''simple docstring'''



             model.train()
             lowerCamelCase__		:    Tuple   =  self._prepare_inputs(__lowerCamelCase	)

             if self.use_amp:
                   with autocast():
                         lowerCamelCase__		:    List[str]   =  self.compute_loss(__lowerCamelCase ,				__lowerCamelCase	)
             else:
                   lowerCamelCase__		:    List[Any]   =  self.compute_loss(__lowerCamelCase ,				__lowerCamelCase	)

             if self.args.n_gpu > 1:
                   if model.module.config.ctc_loss_reduction == "mean":
                         lowerCamelCase__		:    Any   =  loss.mean()
                   elif model.module.config.ctc_loss_reduction == "sum":
                         lowerCamelCase__		:    Union[str, Any]   =  loss.sum() / (inputs["labels"] >= 0).sum()
                   else:
                         raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"	)

             if self.args.gradient_accumulation_steps > 1:
                   lowerCamelCase__		:    Optional[Any]   =  loss / self.args.gradient_accumulation_steps

             if self.use_amp:
                   self.scaler.scale(__lowerCamelCase	).backward()
             elif self.use_apex:
                   with amp.scale_loss(__lowerCamelCase ,				self.optimizer	) as scaled_loss:
                         scaled_loss.backward()
             elif self.deepspeed:
                   self.deepspeed.backward(__lowerCamelCase	)
             else:
                   loss.backward()

             return loss.detach()



def 				lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:    Optional[Any]   =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

      lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__		:    int   =  parser.parse_args_into_dataclasses()
      configure_logger(_A   , _A						)

      lowerCamelCase__		:    Any   =  Orthography.from_name(data_args.orthography.lower()						)
      lowerCamelCase__		:    Tuple   =  orthography.create_processor(_A						)
      lowerCamelCase__		:    List[Any]   =  WavaVecaForCTC.from_pretrained(
          model_args.model_name_or_path   , cache_dir=model_args.cache_dir   , gradient_checkpointing=training_args.gradient_checkpointing   , vocab_size=len(processor.tokenizer						)   , )

      lowerCamelCase__		:    List[str]   =  datasets.load_dataset(
          data_args.dataset_name   , data_args.dataset_config_name   , split=data_args.train_split_name						)
      lowerCamelCase__		:    List[str]   =  datasets.load_dataset(
          data_args.dataset_name   , data_args.dataset_config_name   , split=data_args.validation_split_name						)

      lowerCamelCase__		:    Tuple   =  datasets.load_metric("wer"						)
      lowerCamelCase__		:    List[str]   =  processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
      lowerCamelCase__		:    Any   =  "".join(t for t in processor.tokenizer.get_vocab().keys() if len(_A						) == 1						)
      lowerCamelCase__		:    int   =  re.compile(  # remove characters not in vocabulary
          rF"[^\s{re.escape(_A						)}]"   , flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0   , )
      lowerCamelCase__		:    Any   =  []

      def prepare_example(_A      :   Optional[Any]						):  # TODO(elgeish) make use of multiprocessing?
            lowerCamelCase__   ,       lowerCamelCase__		:    List[Any]   =  librosa.load(example[data_args.speech_file_column]   , sr=_A						)
            if data_args.max_duration_in_seconds is not None:
                  lowerCamelCase__		:    Union[str, Any]   =  len(example["speech"]						) / example["sampling_rate"]
            # Normalize and clean up text; order matters!
            lowerCamelCase__		:    Any   =  orthography.preprocess_for_training(example[data_args.target_text_column]						)
            lowerCamelCase__		:    Any   =  vocabulary_text_cleaner.sub(""   , _A						)
            if updated_text != example[data_args.target_text_column]:
                  text_updates.append((example[data_args.target_text_column], updated_text)						)
                  lowerCamelCase__		:    Any   =  updated_text
            return example

      lowerCamelCase__		:    List[Any]   =  train_dataset.map(_A   , remove_columns=[data_args.speech_file_column]						)
      lowerCamelCase__		:    List[str]   =  val_dataset.map(_A   , remove_columns=[data_args.speech_file_column]						)

      if data_args.max_duration_in_seconds is not None:

            def filter_by_max_duration(_A      :   List[str]						):
                  return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

            lowerCamelCase__		:    Union[str, Any]   =  len(_A						)
            lowerCamelCase__		:    str   =  len(_A						)
            lowerCamelCase__		:    Optional[int]   =  train_dataset.filter(_A   , remove_columns=["duration_in_seconds"]						)
            lowerCamelCase__		:    Dict   =  val_dataset.filter(_A   , remove_columns=["duration_in_seconds"]						)
            if len(_A						) > old_train_size:
                  logger.warning(
                      F"Filtered out {len(_A						) - old_train_size} train example(s) longer than"
                      F" {data_args.max_duration_in_seconds} second(s)."						)
            if len(_A						) > old_val_size:
                  logger.warning(
                      F"Filtered out {len(_A						) - old_val_size} validation example(s) longer than"
                      F" {data_args.max_duration_in_seconds} second(s)."						)
      logger.info(F"Split sizes: {len(_A						)} train and {len(_A						)} validation."						)

      logger.warning(F"Updated {len(_A						)} transcript(s) using '{data_args.orthography}' orthography rules."						)
      if logger.isEnabledFor(logging.DEBUG						):
            for original_text, updated_text in text_updates:
                  logger.debug(F"Updated text: \"{original_text}\" -> \"{updated_text}\""						)
      lowerCamelCase__		:    Optional[int]   =  None

      def prepare_dataset(_A      :   Optional[int]						):
            # check that all files have the correct sampling rate
            assert (
                len(set(batch["sampling_rate"]						)						) == 1
            ), F"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."

            lowerCamelCase__		:    List[str]   =  processor(
                audio=batch["speech"]   , text=batch[data_args.target_text_column]   , sampling_rate=batch["sampling_rate"][0]						)
            batch.update(_A						)
            return batch

      lowerCamelCase__		:    List[Any]   =  train_dataset.map(
          _A   , batch_size=training_args.per_device_train_batch_size   , batched=_A   , num_proc=data_args.preprocessing_num_workers   , )
      lowerCamelCase__		:    int   =  val_dataset.map(
          _A   , batch_size=training_args.per_device_train_batch_size   , batched=_A   , num_proc=data_args.preprocessing_num_workers   , )

      lowerCamelCase__		:    Dict   =  DataCollatorCTCWithPadding(processor=_A   , padding=_A						)

      def compute_metrics(_A      :   List[Any]						):
            lowerCamelCase__		:    Optional[int]   =  pred.predictions
            lowerCamelCase__		:    Dict   =  np.argmax(_A   , axis=-1						)

            lowerCamelCase__		:    Dict   =  processor.tokenizer.pad_token_id

            lowerCamelCase__		:    Any   =  processor.batch_decode(_A						)
            # we do not want to group tokens when computing the metrics
            lowerCamelCase__		:    Optional[Any]   =  processor.batch_decode(pred.label_ids   , group_tokens=_A						)
            if logger.isEnabledFor(logging.DEBUG						):
                  for reference, predicted in zip(_A   , _A						):
                        logger.debug(F"reference: \"{reference}\""						)
                        logger.debug(F"predicted: \"{predicted}\""						)
                        if orthography.untransliterator is not None:
                              logger.debug(F"reference (untransliterated): \"{orthography.untransliterator(_A						)}\""						)
                              logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(_A						)}\""						)

            lowerCamelCase__		:    Optional[int]   =  wer_metric.compute(predictions=_A   , references=_A						)

            return {"wer": wer}

      if model_args.freeze_feature_extractor:
            model.freeze_feature_extractor()

      lowerCamelCase__		:    List[str]   =  CTCTrainer(
          model=_A   , data_collator=_A   , args=_A   , compute_metrics=_A   , train_dataset=_A   , eval_dataset=_A   , tokenizer=processor.feature_extractor   , )

      trainer.train()


if __name__ == "__main__":
    main()






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}')


