
import argparse
import json
import os

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.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase__     :   List[str]	=							16
lowerCamelCase__     :   Any	=							32


def 							__A(lowerCAmelCase						,  lowerCAmelCase = 1_6						,  lowerCAmelCase = "bert-base-cased" )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	AutoTokenizer.from_pretrained(snake_case_ )
		_UpperCamelCase						    =	load_dataset("""glue"""						,  """mrpc""" )

		def tokenize_function(lowerCAmelCase ):
				# max_length=None => use the model max length (it's actually the default)
				_UpperCamelCase						    =	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
		_UpperCamelCase						    =	datasets.map(
		    snake_case_						,  batched=snake_case_						,  remove_columns=["""idx""", """sentence1""", """sentence2"""]						,  load_from_cache_file=snake_case_ )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		_UpperCamelCase						    =	tokenized_datasets.rename_column("""label"""						,  """labels""" )

		def collate_fn(lowerCAmelCase ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(snake_case_						,  padding="""max_length"""						,  max_length=1_2_8						,  return_tensors="""pt""" )
				return tokenizer.pad(snake_case_						,  padding="""longest"""						,  return_tensors="""pt""" )

		# Instantiate dataloaders.
		_UpperCamelCase						    =	DataLoader(
		    tokenized_datasets["""train"""]						,  shuffle=snake_case_						,  collate_fn=snake_case_						,  batch_size=snake_case_ )
		_UpperCamelCase						    =	DataLoader(
		    tokenized_datasets["""validation"""]						,  shuffle=snake_case_						,  collate_fn=snake_case_						,  batch_size=snake_case_ )

		return train_dataloader, eval_dataloader


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				Dict:

		"""simple docstring"""






		_UpperCamelCase						    =	Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_UpperCamelCase						    =	config["lr"]
		_UpperCamelCase						    =	int(config["""num_epochs"""] )
		_UpperCamelCase						    =	int(config["""seed"""] )
		_UpperCamelCase						    =	int(config["""batch_size"""] )
		_UpperCamelCase						    =	args.model_name_or_path

		set_seed(snake_case_ )
		_UpperCamelCase						    =	get_dataloaders(snake_case_						,  snake_case_						,  snake_case_ )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_UpperCamelCase						    =	AutoModelForSequenceClassification.from_pretrained(snake_case_						,  return_dict=snake_case_ )

		# Instantiate optimizer
		_UpperCamelCase						    =	(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_UpperCamelCase						    =	optimizer_cls(params=model.parameters()						,  lr=snake_case_ )

		if accelerator.state.deepspeed_plugin is not None:
				_UpperCamelCase						    =	accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				_UpperCamelCase						    =	1
		_UpperCamelCase						    =	(len(snake_case_ ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_UpperCamelCase						    =	get_linear_schedule_with_warmup(
				    optimizer=snake_case_						,  num_warmup_steps=0						,  num_training_steps=snake_case_						,  )
		else:
				_UpperCamelCase						    =	DummyScheduler(snake_case_						,  total_num_steps=snake_case_						,  warmup_num_steps=0 )

		# 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.
		_UpperCamelCase						    =	accelerator.prepare(
		    snake_case_						,  snake_case_						,  snake_case_						,  snake_case_						,  snake_case_ )

		# We need to keep track of how many total steps we have iterated over
		_UpperCamelCase						    =	0
		# We also need to keep track of the stating epoch so files are named properly
		_UpperCamelCase						    =	0

		# Now we train the model
		_UpperCamelCase						    =	evaluate.load("""glue"""						,  """mrpc""" )
		_UpperCamelCase						    =	0
		_UpperCamelCase						    =	{}
		for epoch in range(snake_case_						,  snake_case_ ):
				model.train()
				for step, batch in enumerate(snake_case_ ):
						_UpperCamelCase						    =	model(**snake_case_ )
						_UpperCamelCase						    =	outputs.loss
						_UpperCamelCase						    =	loss / gradient_accumulation_steps
						accelerator.backward(snake_case_ )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

				model.eval()
				_UpperCamelCase						    =	0
				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():
								_UpperCamelCase						    =	model(**snake_case_ )
						_UpperCamelCase						    =	outputs.logits.argmax(dim=-1 )
						# It is slightly faster to call this once, than multiple times
						_UpperCamelCase						    =	accelerator.gather(
						    (predictions, batch["""labels"""]) )  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
								if step == len(snake_case_ ) - 1:
										_UpperCamelCase						    =	predictions[: len(eval_dataloader.dataset ) - samples_seen]
										_UpperCamelCase						    =	references[: len(eval_dataloader.dataset ) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=snake_case_						,  references=snake_case_						,  )

				_UpperCamelCase						    =	metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F'epoch {epoch}:'						,  snake_case_ )
				_UpperCamelCase						    =	eval_metric["accuracy"]

				if best_performance < eval_metric["accuracy"]:
						_UpperCamelCase						    =	eval_metric["accuracy"]

		if args.performance_lower_bound is not None:
				assert (
				    args.performance_lower_bound <= best_performance
				), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir						,  """all_results.json""" )						,  """w""" ) as f:
						json.dump(snake_case_						,  snake_case_ )


def 							__A()		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
		parser.add_argument(
		    """--model_name_or_path"""						,  type=snake_case_						,  default="""bert-base-cased"""						,  help="""Path to pretrained model or model identifier from huggingface.co/models."""						,  required=snake_case_						,  )
		parser.add_argument(
		    """--output_dir"""						,  type=snake_case_						,  default="""."""						,  help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""						,  )
		parser.add_argument(
		    """--performance_lower_bound"""						,  type=snake_case_						,  default=snake_case_						,  help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""						,  )
		parser.add_argument(
		    """--num_epochs"""						,  type=snake_case_						,  default=3						,  help="""Number of train epochs."""						,  )
		_UpperCamelCase						    =	parser.parse_args()
		_UpperCamelCase						    =	{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
		training_function(snake_case_						,  snake_case_ )


if __name__ == "__main__":
		main()





from math import isclose, sqrt


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float, float]:

		"""simple docstring"""






		_UpperCamelCase						    =	point_y / 4 / point_x
		_UpperCamelCase						    =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
		_UpperCamelCase						    =	(1 - normal_gradient * normal_gradient) / (
		    1 + normal_gradient * normal_gradient
		)
		_UpperCamelCase						    =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

		# to find the next point, solve the simultaeneous equations:
		# y^2 + 4x^2 = 100
		# y - b = m * (x - a)
		# ==> A x^2 + B x + C = 0
		_UpperCamelCase						    =	outgoing_gradient**2 + 4
		_UpperCamelCase						    =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
		_UpperCamelCase						    =	(point_y - outgoing_gradient * point_x) ** 2 - 1_0_0

		_UpperCamelCase						    =	(
		    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)
		_UpperCamelCase						    =	(
		    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)

		# two solutions, one of which is our input point
		_UpperCamelCase						    =	x_minus if isclose(lowerCAmelCase						,  lowerCAmelCase ) else x_plus
		_UpperCamelCase						    =	point_y + outgoing_gradient * (next_x - point_x)

		return next_x, next_y, outgoing_gradient


def 							__A(lowerCAmelCase = 1.4						,  lowerCAmelCase = -9.6 )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	0
		_UpperCamelCase						    =	first_x_coord
		_UpperCamelCase						    =	first_y_coord
		_UpperCamelCase						    =	(10.1 - point_y) / (0.0 - point_x)

		while not (-0.01 <= point_x <= 0.01 and point_y > 0):
				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	next_point(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
				num_reflections += 1

		return num_reflections


if __name__ == "__main__":
		print(F"""{solution() = }""")



