






'''simple docstring'''

import argparse
import os

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, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

UpperCAmelCase__						:Union[str, Any]          =			16
UpperCAmelCase__						:Tuple          =			32
def     __lowercase  (_lowercase,						_lowercase = 16     )   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Optional[int]				   =				AutoTokenizer.from_pretrained("""bert-base-cased"""     )
     __lowerCamelCase						:       List[str]				   =				load_dataset("""glue""",						"""mrpc"""     )

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

     def collate_fn(_lowercase     ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __lowerCamelCase						:       int				   =				128 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						:       int				   =				16
          elif accelerator.mixed_precision != "no":
               __lowerCamelCase						:       Tuple				   =				8
          else:
               __lowerCamelCase						:       Any				   =				None

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

     # Instantiate dataloaders.
     __lowerCamelCase						:       Dict				   =				DataLoader(
         tokenized_datasets["""train"""],						shuffle=_lowercase,						collate_fn=_lowercase,						batch_size=_lowercase     )
     __lowerCamelCase						:       Tuple				   =				DataLoader(
         tokenized_datasets["""validation"""],						shuffle=_lowercase,						collate_fn=_lowercase,						batch_size=_lowercase     )

     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

   UpperCAmelCase__						:List[Any]          =			mocked_dataloaders  # noqa: F811
def     __lowercase  (_lowercase,						_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     # For testing only
     if os.environ.get("""TESTING_MOCKED_DATALOADERS""",						_lowercase     ) == "1":
          __lowerCamelCase						:       Optional[Any]				   =				2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          __lowerCamelCase						:       Tuple				   =				Accelerator(
              cpu=args.cpu,						mixed_precision=args.mixed_precision,						log_with="""all""",						project_dir=args.project_dir     )
     else:
          __lowerCamelCase						:       Any				   =				Accelerator(cpu=args.cpu,						mixed_precision=args.mixed_precision     )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __lowerCamelCase						:       Optional[Any]				   =				config["""lr"""]
     __lowerCamelCase						:       Optional[int]				   =				int(config["""num_epochs"""]     )
     __lowerCamelCase						:       Any				   =				int(config["""seed"""]     )
     __lowerCamelCase						:       str				   =				int(config["""batch_size"""]     )
     set_seed(_lowercase     )

     __lowerCamelCase   ,      __lowerCamelCase						:       Dict				   =				get_dataloaders(_lowercase,						_lowercase     )
     __lowerCamelCase						:       int				   =				evaluate.load("""glue""",						"""mrpc"""     )

     # If the batch size is too big we use gradient accumulation
     __lowerCamelCase						:       Any				   =				1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __lowerCamelCase						:       str				   =				batch_size // MAX_GPU_BATCH_SIZE
          __lowerCamelCase						:       Optional[int]				   =				MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __lowerCamelCase						:       int				   =				AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",						return_dict=_lowercase     )

     # 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).
     __lowerCamelCase						:       Optional[Any]				   =				model.to(accelerator.device     )

     # Instantiate optimizer
     __lowerCamelCase						:       Union[str, Any]				   =				AdamW(params=model.parameters(),						lr=_lowercase     )

     # Instantiate scheduler
     __lowerCamelCase						:       Dict				   =				get_linear_schedule_with_warmup(
         optimizer=_lowercase,						num_warmup_steps=100,						num_training_steps=(len(_lowercase     ) * num_epochs) // gradient_accumulation_steps,						)

     # 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.
     __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				accelerator.prepare(
         _lowercase,						_lowercase,						_lowercase,						_lowercase,						_lowercase     )

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          __lowerCamelCase						:       Optional[int]				   =				os.path.split(_lowercase     )[-1].split("""."""     )[0]
          accelerator.init_trackers(_lowercase,						_lowercase     )

     # Now we train the model
     for epoch in range(_lowercase     ):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               __lowerCamelCase						:       Optional[int]				   =				0
          for step, batch in enumerate(_lowercase     ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device     )
               __lowerCamelCase						:       Any				   =				model(**_lowercase     )
               __lowerCamelCase						:       List[Any]				   =				outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               __lowerCamelCase						:       Any				   =				loss / gradient_accumulation_steps
               accelerator.backward(_lowercase     )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

          __lowerCamelCase						:       List[Any]				   =				metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:",						_lowercase     )

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       """accuracy""": eval_metric["""accuracy"""],
                       """f1""": eval_metric["""f1"""],
                       """train_loss""": total_loss.item() / len(_lowercase     ),
                       """epoch""": epoch,
                   },						step=_lowercase,						)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()
def     __lowercase  ()   ->     Union[str, Any]:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				argparse.ArgumentParser(description="""Simple example of training script."""     )
     parser.add_argument(
         """--mixed_precision""",						type=_lowercase,						default=_lowercase,						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(
         """--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(
         """--project_dir""",						type=_lowercase,						default="""logs""",						help="""Location on where to store experiment tracking logs` and relevent project information""",						)
     __lowerCamelCase						:       str				   =				parser.parse_args()
     __lowerCamelCase						:       int				   =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(_lowercase,						_lowercase     )


if __name__ == "__main__":
   main()











'''simple docstring'''

from __future__ import annotations

from typing import Any
def     __lowercase  (_lowercase     )   ->     int:


     """simple docstring"""
     if not postfix_notation:
          return 0

     __lowerCamelCase						:       Optional[int]				   =				{"""+""", """-""", """*""", """/"""}
     __lowerCamelCase						:       list[Any]				   =				[]

     for token in postfix_notation:
          if token in operations:
               __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				stack.pop(), stack.pop()
               if token == "+":
                    stack.append(a + b     )
               elif token == "-":
                    stack.append(a - b     )
               elif token == "*":
                    stack.append(a * b     )
               else:
                    if a * b < 0 and a % b != 0:
                         stack.append(a // b + 1     )
                    else:
                         stack.append(a // b     )
          else:
               stack.append(int(_lowercase     )     )

     return stack.pop()


if __name__ == "__main__":
   import doctest

   doctest.testmod()



