



import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


A__     				=	16
A__     				=	32


def 		_UpperCAmelCase    (				snake_case      ):





     """simple docstring"""

     return int(x / 2**20      )





class  __lowerCAmelCase			:




    def __enter__(							self		):



         """simple docstring"""





         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         _lowerCAmelCase      =   torch.cuda.memory_allocated()
         _lowerCAmelCase      =   psutil.Process()

         _lowerCAmelCase      =   self.cpu_mem_used()
         _lowerCAmelCase      =   True
         _lowerCAmelCase      =   threading.Thread(target=self.peak_monitor_func		)
         _lowerCAmelCase      =   True
         peak_monitor_thread.start()
         return self




    def 							snake_case       (							self		):



         """simple docstring"""





         return self.process.memory_info().rss




    def 							snake_case       (							self		):



         """simple docstring"""





         _lowerCAmelCase      =   -1

         while True:
              _lowerCAmelCase      =   max(self.cpu_mem_used()						,    self.cpu_peak		)

              # can't sleep or will not catch the peak right (this comment is here on purpose)
              # time.sleep(0.001) # 1msec

              if not self.peak_monitoring:
                   break




    def __exit__(							self						,    *_snake_case		):



         """simple docstring"""





         _lowerCAmelCase      =   False

         gc.collect()
         torch.cuda.empty_cache()
         _lowerCAmelCase      =   torch.cuda.memory_allocated()
         _lowerCAmelCase      =   torch.cuda.max_memory_allocated()
         _lowerCAmelCase      =   bamb(self.end - self.begin		)
         _lowerCAmelCase      =   bamb(self.peak - self.begin		)

         _lowerCAmelCase      =   self.cpu_mem_used()
         _lowerCAmelCase      =   bamb(self.cpu_end - self.cpu_begin		)
         _lowerCAmelCase      =   bamb(self.cpu_peak - self.cpu_begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

 A__     				=	mocked_dataloaders  # noqa: F811


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





     """simple docstring"""

     if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,						_A      ) == "1":
          _lowerCAmelCase      =   2

     # New Code #
     # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
     _lowerCAmelCase      =   FullyShardedDataParallelPlugin(
         state_dict_config=FullStateDictConfig(offload_to_cpu=_A     ,						ranka_only=_A      )     ,						optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_A     ,						ranka_only=_A      )     ,						)

     # Initialize accelerator
     if args.with_tracking:
          _lowerCAmelCase      =   Accelerator(
              cpu=args.cpu     ,						mixed_precision=args.mixed_precision     ,						log_with="""wandb"""     ,						project_dir=args.logging_dir     ,						fsdp_plugin=_A     ,						)
     else:
          _lowerCAmelCase      =   Accelerator(fsdp_plugin=_A      )
     accelerator.print(accelerator.distributed_type      )

     if hasattr(args.checkpointing_steps     ,						"""isdigit"""      ):
          if args.checkpointing_steps == "epoch":
               _lowerCAmelCase      =   args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               _lowerCAmelCase      =   int(args.checkpointing_steps      )
          else:
               raise ValueError(
                   F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'      )
     else:
          _lowerCAmelCase      =   None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _lowerCAmelCase      =   config["""lr"""]
     _lowerCAmelCase      =   int(config["""num_epochs"""]      )
     _lowerCAmelCase      =   int(config["""seed"""]      )
     _lowerCAmelCase      =   int(config["""batch_size"""]      )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          _lowerCAmelCase      =   vars(_A      )
          accelerator.init_trackers("""fsdp_glue_no_trainer"""     ,						_A      )

     _lowerCAmelCase      =   AutoTokenizer.from_pretrained(args.model_name_or_path      )
     _lowerCAmelCase      =   load_dataset("""glue"""     ,						"""mrpc"""      )
     _lowerCAmelCase      =   evaluate.load("""glue"""     ,						"""mrpc"""      )

     def tokenize_function(snake_case      ):
          # max_length=None => use the model max length (it's actually the default)
          _lowerCAmelCase      =   tokenizer(examples["""sentence1"""]     ,						examples["""sentence2"""]     ,						truncation=_A     ,						max_length=_A      )
          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():
          _lowerCAmelCase      =   datasets.map(
              _A     ,						batched=_A     ,						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
     _lowerCAmelCase      =   tokenized_datasets.rename_column("""label"""     ,						"""labels"""      )

     # If the batch size is too big we use gradient accumulation
     _lowerCAmelCase      =   1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          _lowerCAmelCase      =   batch_size // MAX_GPU_BATCH_SIZE
          _lowerCAmelCase      =   MAX_GPU_BATCH_SIZE

     def collate_fn(snake_case      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          _lowerCAmelCase      =   1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               _lowerCAmelCase      =   16
          elif accelerator.mixed_precision != "no":
               _lowerCAmelCase      =   8
          else:
               _lowerCAmelCase      =   None

          return tokenizer.pad(
              _A     ,						padding="""longest"""     ,						max_length=_A     ,						pad_to_multiple_of=_A     ,						return_tensors="""pt"""     ,						)

     # Instantiate dataloaders.
     _lowerCAmelCase      =   DataLoader(
         tokenized_datasets["""train"""]     ,						shuffle=_A     ,						collate_fn=_A     ,						batch_size=_A      )
     _lowerCAmelCase      =   DataLoader(
         tokenized_datasets["""validation"""]     ,						shuffle=_A     ,						collate_fn=_A     ,						batch_size=_A      )

     set_seed(_A      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _lowerCAmelCase      =   AutoModelForSequenceClassification.from_pretrained(
         args.model_name_or_path     ,						return_dict=_A     ,						low_cpu_mem_usage=_A      )

     # New Code #
     # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
     _lowerCAmelCase      =   accelerator.prepare(_A      )
     accelerator.print(_A      )

     # Instantiate optimizer
     # New Code #
     # For FSDP feature, at present it doesn't support multiple parameter groups,
     # so we need to create a single parameter group for the whole model
     _lowerCAmelCase      =   torch.optim.AdamW(params=model.parameters()     ,						lr=_A     ,						weight_decay=2E-4      )

     # Instantiate scheduler
     _lowerCAmelCase      =   get_linear_schedule_with_warmup(
         optimizer=_A     ,						num_warmup_steps=10     ,						num_training_steps=(len(_A      ) * num_epochs) // gradient_accumulation_steps     ,						)

     # New Code #
     # For FSDP feature, prepare everything except the model as we have already prepared the model
     # before creating the optimizer
     # 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.
     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase      =   accelerator.prepare(
         _A     ,						_A     ,						_A     ,						_A      )

     _lowerCAmelCase      =   0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'      )
               accelerator.load_state(args.resume_from_checkpoint      )
               _lowerCAmelCase      =   os.path.basename(args.resume_from_checkpoint      )
          else:
               # Get the most recent checkpoint
               _lowerCAmelCase      =   [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
               dirs.sort(key=os.path.getctime      )
               _lowerCAmelCase      =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          _lowerCAmelCase      =   os.path.splitext(_A      )[0]

          if "epoch" in training_difference:
               num_epochs -= int(training_difference.replace("""epoch_"""     ,						""""""      )      )
               _lowerCAmelCase      =   None
          else:
               _lowerCAmelCase      =   int(training_difference.replace("""step_"""     ,						""""""      )      )
               num_epochs -= resume_step // len(_A      )
               # If resuming by step, we also need to know exactly how far into the DataLoader we went
               _lowerCAmelCase      =   (num_epochs * len(_A      )) - resume_step

          # Now we train the model
     for epoch in range(_A      ):
          # New Code #
          # context manager to track the peak memory usage during the training epoch
          with TorchTracemalloc() as tracemalloc:
               model.train()
               if args.with_tracking:
                    _lowerCAmelCase      =   0
               for step, batch in enumerate(_A      ):
                    # We need to skip steps until we reach the resumed step
                    if args.resume_from_checkpoint and epoch == 0:
                         if resume_step is not None and step < resume_step:
                              pass
                                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device      )
                    _lowerCAmelCase      =   model(**_A      )
                    _lowerCAmelCase      =   outputs.loss
                    _lowerCAmelCase      =   loss / gradient_accumulation_steps
                    # We keep track of the loss at each epoch
                    if args.with_tracking:
                         total_loss += loss.detach().float()
                    accelerator.backward(_A      )
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # accelerator.print(lr_scheduler.get_lr())

                    overall_step += 1

                    if isinstance(_A     ,						_A      ):
                         _lowerCAmelCase      =   F'step_{overall_step}'
                         if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                   _lowerCAmelCase      =   os.path.join(args.output_dir     ,						_A      )
                              accelerator.save_state(_A      )
                    # New Code #
                    # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin      )      )      )
          accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used      )      )
          accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked      )      )
          accelerator.print(
              """Total Peak Memory consumed during the train (max): {}""".format(
                  tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       """train_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin      ),
                   }     ,						step=_A     ,						)

          # New Code #
          # context manager to track the peak memory usage during the evaluation
          with TorchTracemalloc() as tracemalloc:
               model.eval()
               for step, batch in enumerate(_A      ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device      )
                    with torch.no_grad():
                         _lowerCAmelCase      =   model(**_A      )
                    _lowerCAmelCase      =   outputs.logits.argmax(dim=-1      )
                    _lowerCAmelCase     ,     _lowerCAmelCase      =   accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
                    metric.add_batch(
                        predictions=_A     ,						references=_A     ,						)

               _lowerCAmelCase      =   metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F'epoch {epoch}:'     ,						_A      )
               if args.with_tracking:
                    accelerator.log(
                        {
                            """accuracy""": eval_metric["""accuracy"""],
                            """f1""": eval_metric["""f1"""],
                            """train_loss""": total_loss.item() / len(_A      ),
                        }     ,						step=_A     ,						)

               if checkpointing_steps == "epoch":
                    _lowerCAmelCase      =   F'epoch_{epoch}'
                    if args.output_dir is not None:
                         _lowerCAmelCase      =   os.path.join(args.output_dir     ,						_A      )
                    accelerator.save_state(_A      )
                    # New Code #
                    # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print("""Memory before entering the eval : {}""".format(bamb(tracemalloc.begin      )      )      )
          accelerator.print("""Memory consumed at the end of the eval (end-begin): {}""".format(tracemalloc.used      )      )
          accelerator.print("""Peak Memory consumed during the eval (max-begin): {}""".format(tracemalloc.peaked      )      )
          accelerator.print(
              """Total Peak Memory consumed during the eval (max): {}""".format(tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       """eval_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin      ),
                   }     ,						step=_A     ,						)

     if args.with_tracking:
          accelerator.end_training()


def 		_UpperCAmelCase    (				):





     """simple docstring"""

     _lowerCAmelCase      =   argparse.ArgumentParser(description="""Simple example of training script."""      )
     parser.add_argument(
         """--mixed_precision"""     ,						type=_A     ,						default=_A     ,						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."""      )
     parser.add_argument(
         """--checkpointing_steps"""     ,						type=_A     ,						default=_A     ,						help="""Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."""     ,						)
     parser.add_argument(
         """--resume_from_checkpoint"""     ,						type=_A     ,						default=_A     ,						help="""If the training should continue from a checkpoint folder."""     ,						)
     parser.add_argument(
         """--with_tracking"""     ,						action="""store_true"""     ,						help="""Whether to load in all available experiment trackers from the environment and use them for logging."""     ,						)
     parser.add_argument(
         """--output_dir"""     ,						type=_A     ,						default="""."""     ,						help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""     ,						)
     parser.add_argument(
         """--logging_dir"""     ,						type=_A     ,						default="""logs"""     ,						help="""Location on where to store experiment tracking logs`"""     ,						)
     parser.add_argument(
         """--model_name_or_path"""     ,						type=_A     ,						help="""Path to pretrained model or model identifier from huggingface.co/models."""     ,						required=_A     ,						)
     _lowerCAmelCase      =   parser.parse_args()
     _lowerCAmelCase      =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(_A     ,						_A      )


if __name__ == "__main__":
 main()








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}")



