




'''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
#
########################################################################

UpperCAmelCase_    :					str					      =						3_2







def      _UpperCamelCase	(_lowerCamelCase							:       Accelerator				,		_lowerCamelCase							:       int = 16		)->				Optional[int]:




     '''simple docstring'''





     __snake_case         =					AutoTokenizer.from_pretrained('''bert-base-cased'''		)
     __snake_case         =					load_dataset('''glue'''				,		'''mrpc'''		)

     def tokenize_function(_lowerCamelCase							:       Dict		):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case         =					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():
          __snake_case         =					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
     __snake_case         =					tokenized_datasets.rename_column('''label'''				,		'''labels'''		)

     def collate_fn(_lowerCamelCase							:       List[str]		):
          # 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(
              _lowerCamelCase				,		padding='''longest'''				,		pad_to_multiple_of=_lowerCamelCase				,		return_tensors='''pt'''				,		)

     # Instantiate dataloaders.
     __snake_case         =					DataLoader(
         tokenized_datasets['''train''']				,		shuffle=_lowerCamelCase				,		collate_fn=_lowerCamelCase				,		batch_size=_lowerCamelCase		)
     __snake_case         =					DataLoader(
         tokenized_datasets['''validation''']				,		shuffle=_lowerCamelCase				,		collate_fn=_lowerCamelCase				,		batch_size=_lowerCamelCase		)

     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[str]					      =						mocked_dataloaders  # noqa: F811







def      _UpperCamelCase	(_lowerCamelCase							:       Any				,		_lowerCamelCase							:       List[Any]		)->				Optional[int]:




     '''simple docstring'''





     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,		_lowerCamelCase		) == "1":
          __snake_case         =					2
     # Initialize accelerator
     __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'''		)

     # 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(_lowerCamelCase		)		)
     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
          __snake_case         =					observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          __snake_case         =					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(_lowerCamelCase		)

          # 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=_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).
          __snake_case         =					model.to(accelerator.device		)

          # Instantiate optimizer
          __snake_case         =					AdamW(params=model.parameters()				,		lr=_lowerCamelCase		)
          __snake_case   ,      __snake_case         =					get_dataloaders(_lowerCamelCase				,		_lowerCamelCase		)

          # Instantiate scheduler
          __snake_case         =					get_linear_schedule_with_warmup(
              optimizer=_lowerCamelCase				,		num_warmup_steps=1_00				,		num_training_steps=(len(_lowerCamelCase		) * 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.
          __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case         =					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		):
                    # And perform gradient accumulation
                    with accelerator.accumulate(_lowerCamelCase		):
                         # We could avoid this line since we set the accelerator with `device_placement=True`.
                         batch.to(accelerator.device		)
                         __snake_case         =					model(**_lowerCamelCase		)
                         __snake_case         =					outputs.loss
                         accelerator.backward(_lowerCamelCase		)
                         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():
                         __snake_case         =					model(**_lowerCamelCase		)
                    __snake_case         =					outputs.logits.argmax(dim=-1		)
                    __snake_case   ,      __snake_case         =					accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
                    metric.add_batch(
                        predictions=_lowerCamelCase				,		references=_lowerCamelCase				,		)

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

    # 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      _UpperCamelCase	()->				str:




     '''simple docstring'''





     __snake_case         =					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.'''		)
     __snake_case         =					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
     __snake_case         =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 2_56}
     training_function(_lowerCamelCase				,		_lowerCamelCase		)


if __name__ == "__main__":
     main()












'''simple docstring'''



def      _UpperCamelCase	(_lowerCamelCase							:       list				,		_lowerCamelCase							:       int		)->				int:




     '''simple docstring'''





     for index, item in enumerate(_lowerCamelCase		):
          if item == target:
               return index
     return -1







def      _UpperCamelCase	(_lowerCamelCase							:       list				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       int		)->				int:




     '''simple docstring'''





     if not (0 <= high < len(_lowerCamelCase		) and 0 <= low < len(_lowerCamelCase		)):
          raise Exception('''Invalid upper or lower bound!'''		)
     if high < low:
          return -1
     if sequence[low] == target:
          return low
     if sequence[high] == target:
          return high
     return rec_linear_search(_lowerCamelCase				,		low + 1				,		high - 1				,		_lowerCamelCase		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[Any]					      =						input('''Enter numbers separated by comma:\n''').strip()
     UpperCAmelCase_    :					Optional[int]					      =						[int(item.strip()) for item in user_input.split(''',''')]

     UpperCAmelCase_    :					List[str]					      =						int(input('''Enter a single number to be found in the list:\n''').strip())
     UpperCAmelCase_    :					Optional[int]					      =						linear_search(sequence, target)
     if result != -1:
          print(F"""linear_search({sequence}, {target}) = {result}""")
     else:
          print(F"""{target} was not found in {sequence}""")






