






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
#
########################################################################

A__      :     List[Any]       =					16
A__      :     Dict       =					32






def     a     (		lowerCamelCase_						,  lowerCamelCase_ = 16							):






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

				def tokenize_function(lowerCamelCase_							):
								# max_length=None => use the model max length (it's actually the default)
								lowercase__  					=     tokenizer(examples['''sentence1''']						,  examples['''sentence2''']						,  truncation=lowerCamelCase_						,  max_length=lowerCamelCase_							)
								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(
								    lowerCamelCase_						,  batched=lowerCamelCase_						,  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(lowerCamelCase_							):
								# 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(
								    lowerCamelCase_						,  padding='''longest'''						,  max_length=lowerCamelCase_						,  pad_to_multiple_of=lowerCamelCase_						,  return_tensors='''pt'''						,  )

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

				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__      :     Optional[int]       =					mocked_dataloaders  # noqa: F811






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,  lowerCamelCase_							) == "1":
								lowercase__  					=     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:
								lowercase__  					=     Accelerator(
								    cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  log_with='''all'''						,  project_dir=args.project_dir							)
				else:
								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''']							)
				set_seed(lowerCamelCase_							)

				lowercase__					,		lowercase__  					=     get_dataloaders(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     evaluate.load('''glue'''						,  '''mrpc'''							)

				# If the batch size is too big we use gradient accumulation
				lowercase__  					=     1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowercase__  					=     batch_size // MAX_GPU_BATCH_SIZE
								lowercase__  					=     MAX_GPU_BATCH_SIZE

				# 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=lowerCamelCase_							)

				# 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=lowerCamelCase_							)

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

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

				# Now we train the model
				for epoch in range(lowerCamelCase_							):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowercase__  					=     0
								for step, batch in enumerate(lowerCamelCase_							):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device							)
												lowercase__  					=     model(**lowerCamelCase_							)
												lowercase__  					=     outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowercase__  					=     loss / gradient_accumulation_steps
												accelerator.backward(lowerCamelCase_							)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

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

								# 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(lowerCamelCase_							),
												        '''epoch''': epoch,
												    }						,  step=lowerCamelCase_						,  )

    # 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     a     (		):






				'''simple docstring'''
				lowercase__  					=     argparse.ArgumentParser(description='''Simple example of training script.'''							)
				parser.add_argument(
				    '''--mixed_precision'''						,  type=lowerCamelCase_						,  default=lowerCamelCase_						,  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=lowerCamelCase_						,  default='''logs'''						,  help='''Location on where to store experiment tracking logs` and relevent project information'''						,  )
				lowercase__  					=     parser.parse_args()
				lowercase__  					=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCamelCase_						,  lowerCamelCase_							)


if __name__ == "__main__":
							main()












import sys
import turtle






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  ):






				'''simple docstring'''
				my_pen.up()
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.down()
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.goto(vertexa[0]						,  vertexa[1]							)

				if depth == 0:
								return

				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)
				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)
				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)


if __name__ == "__main__":
							if len(sys.argv) != 2:
														raise ValueError(
														    'Correct format for using this script: '
														    'python fractals.py <int:depth_for_fractal>'
														)
							A__      :     Dict       =					turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							A__      :     Union[str, Any]       =					[(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
							triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))




