



"""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
#
########################################################################


lowerCAmelCase__              =							16
lowerCAmelCase__              =							32






def 			snake_case_    ( A_   :						Accelerator,							A_   :						int = 16		):


       '''simple docstring'''





       _lowerCamelCase		:							str            =					AutoTokenizer.from_pretrained('''bert-base-cased'''		)
       _lowerCamelCase		:							Optional[Any]            =					load_dataset('''glue''',							'''mrpc'''		)

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

       def collate_fn(A_   :						Optional[int]		):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _lowerCamelCase		:							List[Any]            =					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		:							List[str]            =					16
              elif accelerator.mixed_precision != "no":
                     _lowerCamelCase		:							Tuple            =					8
              else:
                     _lowerCamelCase		:							Tuple            =					None

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

       # Instantiate dataloaders.
       _lowerCamelCase		:							Union[str, Any]            =					DataLoader(
           tokenized_datasets['''train'''],							shuffle=__lowerCAmelCase,							collate_fn=__lowerCAmelCase,							batch_size=__lowerCAmelCase,							drop_last=__lowerCAmelCase		)
       _lowerCamelCase		:							Dict            =					DataLoader(
           tokenized_datasets['''validation'''],							shuffle=__lowerCAmelCase,							collate_fn=__lowerCAmelCase,							batch_size=__lowerCAmelCase,							drop_last=(accelerator.mixed_precision == '''fp8'''),							)

       return train_dataloader, eval_dataloader






def 			snake_case_    ( A_   :						List[str],							A_   :						Tuple		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					Accelerator(cpu=args.cpu,							mixed_precision=args.mixed_precision		)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _lowerCamelCase		:							List[Any]            =					config['''lr''']
       _lowerCamelCase		:							Union[str, Any]            =					int(config['''num_epochs''']		)
       _lowerCamelCase		:							Tuple            =					int(config['''seed''']		)
       _lowerCamelCase		:							int            =					int(config['''batch_size''']		)

       _lowerCamelCase		:							List[Any]            =					evaluate.load('''glue''',							'''mrpc'''		)

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

       set_seed(__lowerCAmelCase		)
       _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					get_dataloaders(__lowerCAmelCase,							__lowerCAmelCase		)
       # 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=__lowerCAmelCase		)

       # 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		:							List[str]            =					model.to(accelerator.device		)
       # Instantiate optimizer
       _lowerCamelCase		:							List[Any]            =					AdamW(params=model.parameters(),							lr=__lowerCAmelCase		)

       # Instantiate scheduler
       _lowerCamelCase		:							Union[str, Any]            =					get_linear_schedule_with_warmup(
           optimizer=__lowerCAmelCase,							num_warmup_steps=1_00,							num_training_steps=(len(__lowerCAmelCase		) * 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		:							int            =					accelerator.prepare(
           __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase		)

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

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

              _lowerCamelCase		:							Any            =					metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:''',							__lowerCAmelCase		)






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							Union[str, Any]            =					argparse.ArgumentParser(description='''Simple example of training script.'''		)
       parser.add_argument(
           '''--mixed_precision''',							type=__lowerCAmelCase,							default=__lowerCAmelCase,							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.'''		)
       _lowerCamelCase		:							int            =					parser.parse_args()
       _lowerCamelCase		:							Dict            =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(__lowerCAmelCase,							__lowerCAmelCase		)


if __name__ == "__main__":
   main()




from collections.abc import Callable






def   lowerCamelCase__							(							__lowerCAmelCase    :    Callable[[float], float]       ,				__lowerCAmelCase    :    float       ,				__lowerCAmelCase    :    float							):




			"""simple docstring"""


			lowerCAmelCase_         =      a
			lowerCAmelCase_         =      b
			if function(__lowerCAmelCase							) == 0:  # one of the a or b is a root for the function
						return a
			elif function(__lowerCAmelCase							) == 0:
						return b
			elif (
			    function(__lowerCAmelCase							) * function(__lowerCAmelCase							) > 0
			):  # if none of these are root and they are both positive or negative,
						# then this algorithm can't find the root
						raise ValueError("could not find root in given interval."							)
			else:
						lowerCAmelCase_         =      start + (end - start) / 2.0
						while abs(start - mid							) > 10**-7:  # until precisely equals to 10^-7
									if function(__lowerCAmelCase							) == 0:
												return mid
									elif function(__lowerCAmelCase							) * function(__lowerCAmelCase							) < 0:
												lowerCAmelCase_         =      mid
									else:
												lowerCAmelCase_         =      mid
									lowerCAmelCase_         =      start + (end - start) / 2.0
						return mid






def   lowerCamelCase__							(							__lowerCAmelCase    :    float							):




			"""simple docstring"""


			return x**3 - 2 * x - 5


if __name__ == "__main__":
			print(bisection(f, 1, 10_00))

			import doctest

			doctest.testmod()



