




'''simple docstring'''




import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

snake_case__      				=  32




def   snake_case__			(       lowerCamelCase__ :      List[Any]	,						lowerCamelCase__ :      List[str] = 1_6	)      ->      Any:
					A_       :   str       =							AutoTokenizer.from_pretrained('''bert-base-cased'''	)
					A_       :   Any       =							load_dataset('''glue'''	,						'''mrpc'''	)

					def tokenize_function(lowerCamelCase__ :      Union[str, Any]	):
										# max_length=None => use the model max length (it's actually the default)
										A_       :   Optional[int]       =							tokenizer(examples['''sentence1''']	,						examples['''sentence2''']	,						truncation=_snake_case	,						max_length=_snake_case	)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										A_       :   Optional[int]       =							datasets.map(
										    _snake_case	,						batched=_snake_case	,						remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,						)

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					A_       :   List[str]       =							tokenized_datasets.rename_column('''label'''	,						'''labels'''	)

					def collate_fn(lowerCamelCase__ :      Optional[Any]	):
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															A_       :   Optional[int]       =							1_6
										elif accelerator.mixed_precision != "no":
															A_       :   List[Any]       =							8
										else:
															A_       :   Tuple       =							None

										return tokenizer.pad(
										    _snake_case	,						padding='''longest'''	,						pad_to_multiple_of=_snake_case	,						return_tensors='''pt'''	,						)

					# Instantiate dataloaders.
					A_       :   Any       =							DataLoader(
					    tokenized_datasets['''train''']	,						shuffle=_snake_case	,						collate_fn=_snake_case	,						batch_size=_snake_case	)
					A_       :   Union[str, Any]       =							DataLoader(
					    tokenized_datasets['''validation''']	,						shuffle=_snake_case	,						collate_fn=_snake_case	,						batch_size=_snake_case	)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							snake_case__      				=  mocked_dataloaders  # noqa: F811




def   snake_case__			(       lowerCamelCase__ :      str	,						lowerCamelCase__ :      Optional[Any]	)      ->      Tuple:






					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,						_snake_case	) == "1":
										A_       :   List[str]       =							2
					# Initialize accelerator
					A_       :   Union[str, Any]       =							Accelerator(cpu=args.cpu	,						mixed_precision=args.mixed_precision	)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					A_       :   int       =							config['''lr''']
					A_       :   Optional[Any]       =							int(config['''num_epochs''']	)
					A_       :   str       =							int(config['''seed''']	)
					A_       :   str       =							int(config['''batch_size''']	)

					A_       :   Optional[int]       =							evaluate.load('''glue'''	,						'''mrpc'''	)

					# New Code #
					# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
					# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
					# half each time. From this, we can calculate the number of gradient accumulation steps needed
					# and modify the Accelerator object as a result
					@find_executable_batch_size(starting_batch_size=int(_snake_case	)	)
					def inner_training_loop(lowerCamelCase__ :      str	):
										# Since we need to modify the outside accelerator object, we need to bring it
										# to the local scope
										nonlocal accelerator

										# We can calculate the number of gradient accumulation steps based on the current
										# batch size vs the starting batch size
										A_       :   List[str]       =							observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										A_       :   str       =							num_gradient_accumulation_steps

										# Next we need to free all of the stored model references in the Accelerator each time
										accelerator.free_memory()

										# And set the seed so our results are reproducable each reset
										set_seed(_snake_case	)

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										A_       :   Tuple       =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,						return_dict=_snake_case	)

										# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
										# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
										# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
										A_       :   Dict       =							model.to(accelerator.device	)

										# Instantiate optimizer
										A_       :   int       =							AdamW(params=model.parameters()	,						lr=_snake_case	)
										A_       ,A_       :   Any       =							get_dataloaders(_snake_case	,						_snake_case	)

										# Instantiate scheduler
										A_       :   List[Any]       =							get_linear_schedule_with_warmup(
										    optimizer=_snake_case	,						num_warmup_steps=1_0_0	,						num_training_steps=(len(_snake_case	) * num_epochs)	,						)

										# Prepare everything
										# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
										# prepare method.
										A_       ,A_       ,A_       ,A_       ,A_       :   Optional[int]       =							accelerator.prepare(
										    _snake_case	,						_snake_case	,						_snake_case	,						_snake_case	,						_snake_case	)

										# Now we train the model
										for epoch in range(_snake_case	):
															model.train()
															for step, batch in enumerate(_snake_case	):
																				# And perform gradient accumulation
																				with accelerator.accumulate(_snake_case	):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device	)
																									A_       :   List[str]       =							model(**_snake_case	)
																									A_       :   Dict       =							outputs.loss
																									accelerator.backward(_snake_case	)
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

															model.eval()
															for step, batch in enumerate(_snake_case	):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device	)
																				with torch.no_grad():
																									A_       :   int       =							model(**_snake_case	)
																				A_       :   Union[str, Any]       =							outputs.logits.argmax(dim=-1	)
																				A_       ,A_       :   Tuple       =							accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
																				metric.add_batch(
																				    predictions=_snake_case	,						references=_snake_case	,						)

															A_       :   Optional[int]       =							metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(f'epoch {epoch}:'	,						_snake_case	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
					inner_training_loop()




def   snake_case__			(       )      ->      int:
					A_       :   str       =							argparse.ArgumentParser(description='''Simple example of training script.'''	)
					parser.add_argument(
					    '''--mixed_precision'''	,						type=_snake_case	,						default=_snake_case	,						choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,						help='''Whether to use mixed precision. Choose'''
					    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
					    '''and an Nvidia Ampere GPU.'''	,						)
					parser.add_argument('''--cpu'''	,						action='''store_true'''	,						help='''If passed, will train on the CPU.'''	)
					A_       :   Optional[int]       =							parser.parse_args()
					# New Code #
					# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
					A_       :   int       =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
					training_function(_snake_case	,						_snake_case	)


if __name__ == "__main__":
							main()











"""simple docstring"""







from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

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

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






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

            UpperCAmelCase							=       BASE_URL.format(_snake_case      ,       _snake_case							)
            UpperCAmelCase							=       get(_snake_case							)

            # Is the response ok?
            response.raise_for_status()

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

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

            # Fetch all the grids that contains the items.
            UpperCAmelCase							=       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.
                        UpperCAmelCase							=       grid.find("""p"""      ,       {"""class""": """list-title"""}							).text

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

  _UpperCamelCase     			=  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:
      _UpperCamelCase     			=  pharmacy_price["""pharmacy_name"""]
      _UpperCamelCase     			=  pharmacy_price["""price"""]

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



