




'''simple docstring'''





import argparse

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
#
# 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 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_						:      int       =						16
UpperCamelCase_						:      Optional[int]       =						32





def      __a     (       _UpperCamelCase:   Accelerator ,						_UpperCamelCase:   int = 16   )						->			int:

     """simple docstring"""




     _snake_case							=				AutoTokenizer.from_pretrained("bert-base-cased"   )
     _snake_case							=				load_dataset("glue" ,						"mrpc"   )

     def tokenize_function(_UpperCamelCase:   List[str]   ):
          # max_length=None => use the model max length (it's actually the default)
          _snake_case							=				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():
          _snake_case							=				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
     _snake_case							=				tokenized_datasets.rename_column("label" ,						"labels"   )

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

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

     # Instantiate dataloaders.
     _snake_case							=				DataLoader(
         tokenized_datasets["train"] ,						shuffle=__a ,						collate_fn=__a ,						batch_size=__a ,						drop_last=__a   )
     _snake_case							=				DataLoader(
         tokenized_datasets["validation"] ,						shuffle=__a ,						collate_fn=__a ,						batch_size=__a ,						drop_last=(accelerator.mixed_precision == "fp8") ,						)

     return train_dataloader, eval_dataloader





def      __a     (       _UpperCamelCase:   Dict ,						_UpperCamelCase:   str   )						->			Any:

     """simple docstring"""




     _snake_case							=				Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision   )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _snake_case							=				config['lr']
     _snake_case							=				int(config["num_epochs"]   )
     _snake_case							=				int(config["seed"]   )
     _snake_case							=				int(config["batch_size"]   )

     _snake_case							=				evaluate.load("glue" ,						"mrpc"   )

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

     set_seed(__a   )
     _snake_case							=				get_dataloaders(__a ,						__a   )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _snake_case							=				AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,						return_dict=__a   )

     # 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).
     _snake_case							=				model.to(accelerator.device   )
     # Instantiate optimizer
     _snake_case							=				AdamW(params=model.parameters() ,						lr=__a   )

     # Instantiate scheduler
     _snake_case							=				get_linear_schedule_with_warmup(
         optimizer=__a ,						num_warmup_steps=100 ,						num_training_steps=(len(__a   ) * 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.

     _snake_case							=				accelerator.prepare(
         __a ,						__a ,						__a ,						__a ,						__a   )

     # Now we train the model
     for epoch in range(__a   ):
          model.train()
          for step, batch in enumerate(__a   ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device   )
               _snake_case							=				model(**__a   )
               _snake_case							=				outputs.loss
               _snake_case							=				loss / gradient_accumulation_steps
               accelerator.backward(__a   )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          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():
                    _snake_case							=				model(**__a   )
               _snake_case							=				outputs.logits.argmax(dim=-1   )
               _snake_case							=				accelerator.gather_for_metrics((predictions, batch["labels"])   )
               metric.add_batch(
                   predictions=__a ,						references=__a ,						)

          _snake_case							=				metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F"""epoch {epoch}:""" ,						__a   )





def      __a     (       )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				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."   )
     _snake_case							=				parser.parse_args()
     _snake_case							=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(__a ,						__a   )


if __name__ == "__main__":
   main()








'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable





def      __a     (       _UpperCamelCase:   Callable[[int | float], int | float] ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int = 100 ,						)						->			float:

     """simple docstring"""




     _snake_case							=				x_start
     _snake_case							=				fnc(_UpperCamelCase   )
     _snake_case							=				0.0
     for _ in range(_UpperCamelCase   ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          _snake_case							=				(x_end - x_start) / steps + xa
          _snake_case							=				fnc(_UpperCamelCase   )
          area += abs(fxa + fxa   ) * (xa - xa) / 2
          # Increment step
          _snake_case							=				xa
          _snake_case							=				fxa
     return area


if __name__ == "__main__":





   def      __a     (       _UpperCamelCase:   Any   )						->			Optional[int]:

        """simple docstring"""




        return x**3 + x**2

   print('''f(x) = x^3 + x^2''')
   print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
   UpperCamelCase_						:      Optional[int]       =						10
   while i <= 100000:
      print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
      i *= 10


