
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  			=					1_6
__a  			=					3_2




def 				a						(							snake_case__:					Accelerator						,   snake_case__:					int = 16					):


				'''simple docstring'''
				lowercase_       =					AutoTokenizer.from_pretrained('''bert-base-cased'''					)
				lowercase_       =					load_dataset('''glue'''						,   '''mrpc'''					)

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

				def collate_fn(snake_case__:					Optional[Any]					):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowercase_       =					128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowercase_       =					16
								elif accelerator.mixed_precision != "no":
												lowercase_       =					8
								else:
												lowercase_       =					None

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

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

				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						(							snake_case__:					Optional[int]						,   snake_case__:					int					):


				'''simple docstring'''
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,   snake_case__					) == "1":
								lowercase_       =					2
				# Initialize accelerator
				lowercase_       =					Accelerator(cpu=args.cpu						,   mixed_precision=args.mixed_precision					)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowercase_       =					config['''lr''']
				lowercase_       =					int(config['''num_epochs''']					)
				lowercase_       =					int(config['''seed''']					)
				lowercase_       =					int(config['''batch_size''']					)

				lowercase_       =					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=snake_case__					)
				def inner_training_loop(snake_case__:					int					):
								# 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(snake_case__					)

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

								# 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).
								lowercase_       =					model.to(accelerator.device					)

								# Instantiate optimizer
								lowercase_       =					AdamW(params=model.parameters()						,   lr=snake_case__					)
								lowercase_  ,	lowercase_       =					get_dataloaders(snake_case__						,   snake_case__					)

								# Instantiate scheduler
								lowercase_       =					get_linear_schedule_with_warmup(
								    optimizer=snake_case__						,   num_warmup_steps=100						,   num_training_steps=(len(snake_case__					) * 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.
								lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					accelerator.prepare(
								    snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)

								# Now we train the model
								for epoch in range(snake_case__					):
												model.train()
												for step, batch in enumerate(snake_case__					):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device					)
																lowercase_       =					model(**snake_case__					)
																lowercase_       =					outputs.loss
																accelerator.backward(snake_case__					)
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

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

    # 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'''
				lowercase_       =					argparse.ArgumentParser(description='''Simple example of training script.'''					)
				parser.add_argument(
				    '''--mixed_precision'''						,   type=snake_case__						,   default=snake_case__						,   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.'''					)
				lowercase_       =					parser.parse_args()
				lowercase_       =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(snake_case__						,   snake_case__					)


if __name__ == "__main__":
						main()






from __future__ import annotations

from collections.abc import MutableSequence
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			MutableSequence[float]       )       ->     None:
					if len(SCREAMING_SNAKE_CASE_       ) != degree + 1:
									raise ValueError(
									    '''The number of coefficients should be equal to the degree + 1.'''       )

					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					degree
	def __add__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:

					if self.degree > polynomial_a.degree:
									lowercase_       =					self.coefficients[:]
									for i in range(polynomial_a.degree + 1       ):
													coefficients[i] += polynomial_a.coefficients[i]
									return Polynomial(self.degree       ,							SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					polynomial_a.coefficients[:]
									for i in range(self.degree + 1       ):
													coefficients[i] += self.coefficients[i]
									return Polynomial(polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def __sub__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					return self + polynomial_a * Polynomial(0       ,							[-1]       )
	def __neg__(       self  :			int       )       ->     Polynomial:
					return Polynomial(self.degree       ,							[-c for c in self.coefficients]       )
	def __mul__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + polynomial_a.degree + 1)
					for i in range(self.degree + 1       ):
									for j in range(polynomial_a.degree + 1       ):
													coefficients[i + j] += (
													    self.coefficients[i] * polynomial_a.coefficients[j]
													)

					return Polynomial(self.degree + polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			int | float       )       ->     int | float:
					lowercase_       =					0
					for i in range(self.degree + 1       ):
									result += self.coefficients[i] * (substitution**i)
					return result
	def __str__(       self  :			Tuple       )       ->     str:
					lowercase_       =					''''''
					for i in range(self.degree       ,							-1       ,							-1       ):
									if self.coefficients[i] == 0:
													continue
									elif self.coefficients[i] > 0:
													if polynomial:
																	polynomial += " + "
									else:
													polynomial += " - "

									if i == 0:
													polynomial += str(abs(self.coefficients[i]       )       )
									elif i == 1:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x"
									else:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x^" + str(SCREAMING_SNAKE_CASE_       )

					return polynomial
	def __repr__(       self  :			Optional[Any]       )       ->     str:
					return self.__str__()
	def 						_lowercase  (       self  :			int       )       ->     Polynomial:
					lowercase_       =					[0] * self.degree
					for i in range(self.degree       ):
									lowercase_       =					self.coefficients[i + 1] * (i + 1)
					return Polynomial(self.degree - 1       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int | float = 0       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + 2)
					lowercase_       =					constant
					for i in range(self.degree + 1       ):
									lowercase_       =					self.coefficients[i] / (i + 1)
					return Polynomial(self.degree + 1       ,							SCREAMING_SNAKE_CASE_       )
	def __eq__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									return False

					if self.degree != polynomial_a.degree:
									return False

					for i in range(self.degree + 1       ):
									if self.coefficients[i] != polynomial_a.coefficients[i]:
													return False

					return True




	def __ne__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					return not self.__eq__(SCREAMING_SNAKE_CASE_       )




