





"""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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


A			      =	16
A			      =	32

def      __A   (      a_    :Accelerator ,							a_    :int = 16)		->		Any:
				__a			:    Dict      							=	AutoTokenizer.from_pretrained('''bert-base-cased''')
				__a			:    List[str]      							=	load_dataset('''glue''' ,							'''mrpc''')

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

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

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

				# Instantiate dataloaders.
				__a			:    Optional[Any]      							=	DataLoader(
				    tokenized_datasets['''train'''] ,							shuffle=a_ ,							collate_fn=a_ ,							batch_size=a_)
				__a			:    List[str]      							=	DataLoader(
				    tokenized_datasets['''validation'''] ,							shuffle=a_ ,							collate_fn=a_ ,							batch_size=a_)

				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

		A			      =	mocked_dataloaders  # noqa: F811

def      __A   (      a_    :Union[str, Any] ,							a_    :str)		->		Union[str, Any]:
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,							a_) == "1":
								__a			:    List[str]      							=	2
				# Initialize accelerator
				__a			:    Union[str, Any]      							=	Accelerator(cpu=args.cpu ,							mixed_precision=args.mixed_precision)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__a			:    Any      							=	config['''lr''']
				__a			:    Dict      							=	int(config['''num_epochs'''])
				__a			:    List[str]      							=	int(config['''seed'''])
				__a			:    Union[str, Any]      							=	int(config['''batch_size'''])

				__a			:    Tuple      							=	evaluate.load('''glue''' ,							'''mrpc''')

				# New Code #
				# We now can define an inner training loop function. It should take a batch size as the only parameter,
				# and build the dataloaders in there.
				# It also gets our decorator
				@find_executable_batch_size(starting_batch_size=a_)
				def inner_training_loop(a_    :List[str]):
								# And now just move everything below under this function
								# We need to bring in the Accelerator object from earlier
								nonlocal accelerator
								# And reset all of its attributes that could hold onto any memory:
								accelerator.free_memory()

								# Then we can declare the model, optimizer, and everything else:
								set_seed(a_)

								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								__a			:    Tuple      							=	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).
								__a			:    str      							=	model.to(accelerator.device)

								# Instantiate optimizer
								__a			:    int      							=	AdamW(params=model.parameters() ,							lr=a_)
								__a							,    __a			:    int      							=	get_dataloaders(a_ ,							a_)

								# Instantiate scheduler
								__a			:    Any      							=	get_linear_schedule_with_warmup(
								    optimizer=a_ ,							num_warmup_steps=1_00 ,							num_training_steps=(len(a_) * 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			:    str      							=	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)
																__a			:    str      							=	model(**a_)
																__a			:    Optional[Any]      							=	outputs.loss
																accelerator.backward(a_)
																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():
																				__a			:    List[str]      							=	model(**a_)
																__a			:    List[str]      							=	outputs.logits.argmax(dim=-1)
																__a							,    __a			:    List[str]      							=	accelerator.gather_for_metrics((predictions, batch['''labels''']))
																metric.add_batch(
																    predictions=a_ ,							references=a_ ,							)

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

    # 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      __A   (      )		->		str:
				__a			:    Tuple      							=	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.''')
				__a			:    Optional[Any]      							=	parser.parse_args()
				__a			:    int      							=	{'''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




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if len(__A) == 0:
          return False
     _a           =    len(__A) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint]    ,      __A)
     else:
          return binary_search(a_list[midpoint + 1 :]    ,      __A)


if __name__ == "__main__":
  lowercase_									=    input("Enter numbers separated by comma:\n").strip()
  lowercase_									=    [int(item.strip()) for item in user_input.split(",")]
  lowercase_									=    int(input("Enter the number to be found in the list:\n").strip())
  lowercase_									=    "" if binary_search(sequence, target) else "not "
  print(F"""{target} was {not_str}found in {sequence}""")



