"""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       :    List[str]									=			32



def 				__a       (			_lowercase							, _lowercase = 16						):







					"""simple docstring"""





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

					def tokenize_function(_lowercase						):
										# max_length=None => use the model max length (it's actually the default)
										lowerCamelCase__		:   List[str]  				=					tokenizer(examples['''sentence1''']							, examples['''sentence2''']							, truncation=_lowercase							, max_length=_lowercase						)
										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__		:   Tuple  				=					datasets.map(
										    _lowercase							, batched=_lowercase							, 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__		:   int  				=					tokenized_datasets.rename_column('''label'''							, '''labels'''						)

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

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

					# Instantiate dataloaders.
					lowerCamelCase__		:   List[Any]  				=					DataLoader(
					    tokenized_datasets['''train''']							, shuffle=_lowercase							, collate_fn=_lowercase							, batch_size=_lowercase						)
					lowerCamelCase__		:   int  				=					DataLoader(
					    tokenized_datasets['''validation''']							, shuffle=_lowercase							, collate_fn=_lowercase							, batch_size=_lowercase						)

					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[Any]									=			mocked_dataloaders  # noqa: F811



def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





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

					lowerCamelCase__		:   List[str]  				=					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(_lowercase						)						)
					def inner_training_loop(_lowercase						):
										# 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
										lowerCamelCase__		:   Optional[int]  				=					observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										lowerCamelCase__		:   Optional[int]  				=					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(_lowercase						)

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										lowerCamelCase__		:   str  				=					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							, return_dict=_lowercase						)

										# 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__		:   Union[str, Any]  				=					model.to(accelerator.device						)

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

										# Instantiate scheduler
										lowerCamelCase__		:   str  				=					get_linear_schedule_with_warmup(
										    optimizer=_lowercase							, num_warmup_steps=100							, num_training_steps=(len(_lowercase						) * 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.
										lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   List[Any]  				=					accelerator.prepare(
										    _lowercase							, _lowercase							, _lowercase							, _lowercase							, _lowercase						)

										# Now we train the model
										for epoch in range(_lowercase						):
															model.train()
															for step, batch in enumerate(_lowercase						):
																				# And perform gradient accumulation
																				with accelerator.accumulate(_lowercase						):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device						)
																									lowerCamelCase__		:   Dict  				=					model(**_lowercase						)
																									lowerCamelCase__		:   Union[str, Any]  				=					outputs.loss
																									accelerator.backward(_lowercase						)
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

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

															lowerCamelCase__		:   Optional[Any]  				=					metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(f"""epoch {epoch}:"""							, _lowercase						)

    # 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       (			):







					"""simple docstring"""





					lowerCamelCase__		:   List[Any]  				=					argparse.ArgumentParser(description='''Simple example of training script.'''						)
					parser.add_argument(
					    '''--mixed_precision'''							, type=_lowercase							, default=_lowercase							, 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__		:   str  				=					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
					lowerCamelCase__		:   Optional[int]  				=					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
					training_function(_lowercase							, _lowercase						)


if __name__ == "__main__":
					main()







"""simple docstring"""




from collections.abc import Callable

UpperCAmelCase       :    Optional[int]									=			Callable[[float], float]  # type alias for a real -> real function



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, ):







					"""simple docstring"""





					lowerCamelCase__		:   Tuple  				=					float(_lowercase						)
					while True:
										try:
															lowerCamelCase__		:   str  				=					prev_guess - function(_lowercase						) / derivative(_lowercase						)
										except ZeroDivisionError:
															raise ZeroDivisionError('''Could not find root'''						) from None
										if abs(prev_guess - next_guess						) < 10**-5:
															return next_guess
										lowerCamelCase__		:   List[str]  				=					next_guess



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return (x**3) - (2 * x) - 5



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))






