
"""simple docstring"""





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														=		16
_a														=		32



def 				lowerCamelCase__		(      __snake_case,					__snake_case = 16     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  AutoTokenizer.from_pretrained('''bert-base-cased'''     )
					_UpperCamelCase										=  load_dataset('''glue''',					'''mrpc'''     )

					def tokenize_function(__snake_case     ):
										# 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
					# starting with the main process first:
					with accelerator.main_process_first():
										_UpperCamelCase										=  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
					_UpperCamelCase										=  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.
										_UpperCamelCase										=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															_UpperCamelCase										=  16
										elif accelerator.mixed_precision != "no":
															_UpperCamelCase										=  8
										else:
															_UpperCamelCase										=  None

										return tokenizer.pad(
										    __snake_case,					padding='''longest''',					max_length=__snake_case,					pad_to_multiple_of=__snake_case,					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


# 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 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Union[str, Any]:


					"""simple docstring"""





					if os.environ.get('''TESTING_MOCKED_DATALOADERS''',					__snake_case     ) == "1":
										_UpperCamelCase										=  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:
										_UpperCamelCase										=  Accelerator(
										    cpu=args.cpu,					mixed_precision=args.mixed_precision,					log_with='''all''',					project_dir=args.project_dir     )
					else:
										_UpperCamelCase										=  Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision     )
					# 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''']     )
					set_seed(__snake_case     )

					_UpperCamelCase			,  _UpperCamelCase										=  get_dataloaders(__snake_case,					__snake_case     )
					_UpperCamelCase										=  evaluate.load('''glue''',					'''mrpc'''     )

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

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_UpperCamelCase										=  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).
					_UpperCamelCase										=  model.to(accelerator.device     )

					# Instantiate optimizer
					_UpperCamelCase										=  AdamW(params=model.parameters(),					lr=__snake_case     )

					# Instantiate scheduler
					_UpperCamelCase										=  get_linear_schedule_with_warmup(
					    optimizer=__snake_case,					num_warmup_steps=1_00,					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.
					_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  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:
										_UpperCamelCase										=  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:
															_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     )
															_UpperCamelCase										=  model(**__snake_case     )
															_UpperCamelCase										=  outputs.loss
															# New Code #
															if args.with_tracking:
																				total_loss += loss.detach().float()
															_UpperCamelCase										=  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():
																				_UpperCamelCase										=  model(**__snake_case     )
															_UpperCamelCase										=  outputs.logits.argmax(dim=-1     )
															_UpperCamelCase			,  _UpperCamelCase										=  accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
															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     )

										# 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 				lowerCamelCase__		(      )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  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''',					)
					_UpperCamelCase										=  parser.parse_args()
					_UpperCamelCase										=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__snake_case,					__snake_case     )


if __name__ == "__main__":
				main()








"""simple docstring"""





from __future__ import annotations



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					if len(__snake_case     ) <= 1 or n <= 1:
										return

					insert_next(__snake_case,					n - 1     )
					rec_insertion_sort(__snake_case,					n - 1     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					if index >= len(__snake_case     ) or collection[index - 1] <= collection[index]:
										return

					# Swaps adjacent elements since they are not in ascending order
					_UpperCamelCase			,  _UpperCamelCase										=  (
					    collection[index],
					    collection[index - 1],
					)

					insert_next(__snake_case,					index + 1     )


if __name__ == "__main__":
				_a														=		input("""Enter integers separated by spaces: """)
				_a														=		[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)






