






import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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__      :			Optional[Any]							=		16
SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		32

def      __lowercase					( snake_case,  snake_case = 1_6 ):







				"""simple docstring"""






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

				def tokenize_function(snake_case ):
								# max_length=None => use the model max length (it's actually the default)
								__magic_name__		:Union[str, Any]        =      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():
								__magic_name__		:Dict        =      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
				__magic_name__		:Dict        =      tokenized_datasets.rename_column('''label''',  '''labels''' )

				def collate_fn(snake_case ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__magic_name__		:int        =      1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__magic_name__		:str        =      1_6
								elif accelerator.mixed_precision != "no":
												__magic_name__		:List[str]        =      8
								else:
												__magic_name__		:Tuple        =      None

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

				# Instantiate dataloaders.
				__magic_name__		:Optional[int]        =      DataLoader(
				    tokenized_datasets['''train'''],  shuffle=snake_case,  collate_fn=snake_case,  batch_size=snake_case )
				__magic_name__		:Optional[int]        =      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

					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		mocked_dataloaders  # noqa: F811

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if os.environ.get('''TESTING_MOCKED_DATALOADERS''',  snake_case ) == "1":
								__magic_name__		:Optional[Any]        =      2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								__magic_name__		:List[Any]        =      Accelerator(
								    cpu=args.cpu,  mixed_precision=args.mixed_precision,  log_with='''all''',  project_dir=args.project_dir )
				else:
								__magic_name__		:int        =      Accelerator(cpu=args.cpu,  mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__magic_name__		:str        =      config['''lr''']
				__magic_name__		:Union[str, Any]        =      int(config['''num_epochs'''] )
				__magic_name__		:int        =      int(config['''seed'''] )
				__magic_name__		:Tuple        =      int(config['''batch_size'''] )
				set_seed(snake_case )

				__magic_name__     ,				__magic_name__		:int        =      get_dataloaders(snake_case,  snake_case )
				__magic_name__		:List[str]        =      evaluate.load('''glue''',  '''mrpc''' )

				# If the batch size is too big we use gradient accumulation
				__magic_name__		:str        =      1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__magic_name__		:Union[str, Any]        =      batch_size // MAX_GPU_BATCH_SIZE
								__magic_name__		:Union[str, Any]        =      MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__magic_name__		:Union[str, Any]        =      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).
				__magic_name__		:List[str]        =      model.to(accelerator.device )

				# Instantiate optimizer
				__magic_name__		:Optional[Any]        =      AdamW(params=model.parameters(),  lr=snake_case )

				# Instantiate scheduler
				__magic_name__		:int        =      get_linear_schedule_with_warmup(
				    optimizer=snake_case,  num_warmup_steps=1_0_0,  num_training_steps=(len(snake_case ) * num_epochs) // gradient_accumulation_steps,  )

				# 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.
				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Any        =      accelerator.prepare(
				    snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								__magic_name__		:Union[str, Any]        =      os.path.split(snake_case )[-1].split('''.''' )[0]
								accelerator.init_trackers(snake_case,  snake_case )

				# Now we train the model
				for epoch in range(snake_case ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												__magic_name__		:Optional[Any]        =      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 )
												__magic_name__		:List[Any]        =      model(**snake_case )
												__magic_name__		:List[Any]        =      outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												__magic_name__		:Any        =      loss / gradient_accumulation_steps
												accelerator.backward(snake_case )
												if step % gradient_accumulation_steps == 0:
																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` (the default).
												batch.to(accelerator.device )
												with torch.no_grad():
																__magic_name__		:List[Any]        =      model(**snake_case )
												__magic_name__		:Optional[Any]        =      outputs.logits.argmax(dim=-1 )
												__magic_name__     ,				__magic_name__		:Any        =      accelerator.gather_for_metrics((predictions, batch['''labels''']) )
												metric.add_batch(
												    predictions=snake_case,  references=snake_case,  )

								__magic_name__		:Dict        =      metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:''',  snake_case )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        '''accuracy''': eval_metric['''accuracy'''],
												        '''f1''': eval_metric['''f1'''],
												        '''train_loss''': total_loss.item() / len(snake_case ),
												        '''epoch''': epoch,
												    },  step=snake_case,  )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      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.''' )
				parser.add_argument(
				    '''--with_tracking''',  action='''store_true''',  help='''Whether to load in all available experiment trackers from the environment and use them for logging.''',  )
				parser.add_argument(
				    '''--project_dir''',  type=snake_case,  default='''logs''',  help='''Location on where to store experiment tracking logs` and relevent project information''',  )
				__magic_name__		:List[Any]        =      parser.parse_args()
				__magic_name__		:Any        =      {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
				training_function(snake_case,  snake_case )


if __name__ == "__main__":
					main()














import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Vector([1, 2, 3]     )
								self.assertEqual(x.component(0     )       ,					1     )
								self.assertEqual(x.component(2     )       ,					3     )
								__magic_name__		:Tuple        =      Vector()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      Vector([0, 0, 0, 0, 0, 1]     )
								self.assertEqual(str(__lowerCAmelCase     )       ,					'''(0,0,0,0,0,1)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      Vector([1, 2, 3, 4]     )
								self.assertEqual(len(__lowerCAmelCase     )       ,					4     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      Vector([1, 2]     )
								__magic_name__		:int        =      Vector([1, 2, 3, 4, 5]     )
								__magic_name__		:Any        =      Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]     )
								__magic_name__		:Optional[Any]        =      Vector([1, -1, 1, -1, 2, -3, 4, -5]     )
								self.assertAlmostEqual(x.euclidean_length()       ,					2.236       ,					3     )
								self.assertAlmostEqual(y.euclidean_length()       ,					7.416       ,					3     )
								self.assertEqual(z.euclidean_length()       ,					0     )
								self.assertAlmostEqual(w.euclidean_length()       ,					7.616       ,					3     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Vector([1, 2, 3]     )
								__magic_name__		:List[str]        =      Vector([1, 1, 1]     )
								self.assertEqual((x + y).component(0     )       ,					2     )
								self.assertEqual((x + y).component(1     )       ,					3     )
								self.assertEqual((x + y).component(2     )       ,					4     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 2, 3]     )
								__magic_name__		:Union[str, Any]        =      Vector([1, 1, 1]     )
								self.assertEqual((x - y).component(0     )       ,					0     )
								self.assertEqual((x - y).component(1     )       ,					1     )
								self.assertEqual((x - y).component(2     )       ,					2     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Vector([1, 2, 3]     )
								__magic_name__		:Optional[int]        =      Vector([2, -1, 4]     )  # for test of dot product
								__magic_name__		:List[Any]        =      Vector([1, -2, -1]     )
								self.assertEqual(str(x * 3.0     )       ,					'''(3.0,6.0,9.0)'''     )
								self.assertEqual((a * b)       ,					0     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(str(zero_vector(1_0     )     ).count('''0'''     )       ,					1_0     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(str(unit_basis_vector(3       ,					1     )     )       ,					'''(0,1,0)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Vector([1, 2, 3]     )
								__magic_name__		:List[Any]        =      Vector([1, 0, 1]     )
								self.assertEqual(str(axpy(2       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )     )       ,					'''(3,4,7)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 0, 0, 0, 0, 0]     )
								__magic_name__		:Optional[int]        =      x.copy()
								self.assertEqual(str(__lowerCAmelCase     )       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 0, 0]     )
								x.change_component(0       ,					0     )
								x.change_component(1       ,					1     )
								self.assertEqual(str(__lowerCAmelCase     )       ,					'''(0,1,0)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual('''|1,2,3|\n|2,4,5|\n|6,7,8|\n'''       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:List[str]        =      [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
								for x in range(a.height()     ):
												for y in range(a.width()     ):
																self.assertEqual(minors[x][y]       ,					a.minor(__lowerCAmelCase       ,					__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Any        =      [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
								for x in range(a.height()     ):
												for y in range(a.width()     ):
																self.assertEqual(cofactors[x][y]       ,					a.cofactor(__lowerCAmelCase       ,					__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual(-5       ,					a.determinant()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]       ,					3       ,					3     )
								__magic_name__		:Any        =      Vector([1, 2, 3]     )
								self.assertEqual('''(14,32,50)'''       ,					str(a * x     )     )
								self.assertEqual('''|2,4,6|\n|8,10,12|\n|14,16,18|\n'''       ,					str(a * 2     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								a.change_component(0       ,					2       ,					5     )
								self.assertEqual('''|1,2,5|\n|2,4,5|\n|6,7,8|\n'''       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual(7       ,					a.component(2       ,					1     )       ,					0.01     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Optional[Any]        =      Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]       ,					3       ,					3     )
								self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n'''       ,					str(a + b     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Union[str, Any]        =      Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]       ,					3       ,					3     )
								self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'''       ,					str(a - b     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(
								    '''|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'''       ,					str(square_zero_matrix(5     )     )       ,					)


if __name__ == "__main__":
					unittest.main()






