



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

__SCREAMING_SNAKE_CASE :      List[str]             =32



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Accelerator							,							lowerCamelCase__   :      int = 16				):


			'''simple docstring'''







			A:      Optional[int]														=   AutoTokenizer.from_pretrained("""bert-base-cased"""				)
			A:      Tuple														=   load_dataset("""glue"""							,							"""mrpc"""				)

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

			def collate_fn(lowerCamelCase__   :      List[Any]				):
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									A:      Optional[Any]														=   16
						elif accelerator.mixed_precision != "no":
									A:      int														=   8
						else:
									A:      int														=   None

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

			# Instantiate dataloaders.
			A:      Union[str, Any]														=   DataLoader(
			    tokenized_datasets["""train"""]							,							shuffle=lowerCamelCase__							,							collate_fn=lowerCamelCase__							,							batch_size=lowerCamelCase__				)
			A:      str														=   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

					__SCREAMING_SNAKE_CASE :      Tuple             =mocked_dataloaders  # noqa: F811



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      List[str]				):


			'''simple docstring'''







			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,							lowerCamelCase__				) == "1":
						A:      Union[str, Any]														=   2
			# Initialize accelerator
			A:      Tuple														=   Accelerator(cpu=args.cpu							,							mixed_precision=args.mixed_precision				)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			A:      Union[str, Any]														=   config["""lr"""]
			A:      Optional[Any]														=   int(config["""num_epochs"""]				)
			A:      Tuple														=   int(config["""seed"""]				)
			A:      Union[str, Any]														=   int(config["""batch_size"""]				)

			A:      Optional[Any]														=   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
						A:      str														=   observed_batch_size // batch_size

						# And then set it in the Accelerator directly:
						A:      Tuple														=   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)
						A:      Optional[Any]														=   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).
						A:      Optional[Any]														=   model.to(accelerator.device				)

						# Instantiate optimizer
						A:      Optional[Any]														=   AdamW(params=model.parameters()							,							lr=lowerCamelCase__				)
						A							,		A:      List[Any]														=   get_dataloaders(lowerCamelCase__							,							lowerCamelCase__				)

						# Instantiate scheduler
						A:      int														=   get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase__							,							num_warmup_steps=100							,							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.
						A							,		A							,		A							,		A							,		A:      Any														=   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				)
															A:      List[str]														=   model(**lowerCamelCase__				)
															A:      Any														=   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():
															A:      List[str]														=   model(**lowerCamelCase__				)
												A:      Union[str, Any]														=   outputs.logits.argmax(dim=-1				)
												A							,		A:      str														=   accelerator.gather_for_metrics((predictions, batch["""labels"""])				)
												metric.add_batch(
												    predictions=lowerCamelCase__							,							references=lowerCamelCase__							,							)

									A:      Union[str, Any]														=   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 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







			A:      str														=   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."""				)
			A:      Optional[int]														=   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
			A:      Optional[int]														=   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 256}
			training_function(lowerCamelCase__							,							lowerCamelCase__				)


if __name__ == "__main__":
					main()










'''simple docstring'''
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







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



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      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__":
					__SCREAMING_SNAKE_CASE :      List[str]             =input('Enter numbers separated by comma:\n').strip()
					__SCREAMING_SNAKE_CASE :      str             =[int(item.strip()) for item in user_input.split(',')]

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





