






"""simple docstring"""




import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_snake_case          =   16
_snake_case          =   32

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__ = 1_6				):



  '''simple docstring'''
  _a  :       Dict             =	AutoTokenizer.from_pretrained("""bert-base-cased"""				)
  _a  :       str             =	load_dataset("""glue"""     ,   """mrpc"""				)

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

  def collate_fn(UpperCamelCase__				):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _a  :       Any             =	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":
      _a  :       List[Any]             =	1_6
    elif accelerator.mixed_precision != "no":
      _a  :       List[Any]             =	8
    else:
      _a  :       Union[str, Any]             =	None

    return tokenizer.pad(
        __a     ,   padding="""longest"""     ,   max_length=__a     ,   pad_to_multiple_of=__a     ,   return_tensors="""pt"""     ,   )

  # Instantiate dataloaders.
  _a  :       Any             =	DataLoader(
      tokenized_datasets["""train"""]     ,   shuffle=__a     ,   collate_fn=__a     ,   batch_size=__a     ,   drop_last=__a				)
  _a  :       List[str]             =	DataLoader(
      tokenized_datasets["""validation"""]     ,   shuffle=__a     ,   collate_fn=__a     ,   batch_size=__a     ,   drop_last=(accelerator.mixed_precision == """fp8""")     ,   )

  return train_dataloader, eval_dataloader

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Union[str, Any]             =	Accelerator(cpu=args.cpu     ,   mixed_precision=args.mixed_precision				)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _a  :       List[Any]             =	config["""lr"""]
  _a  :       Tuple             =	int(config["""num_epochs"""]				)
  _a  :       int             =	int(config["""seed"""]				)
  _a  :       Any             =	int(config["""batch_size"""]				)

  _a  :       str             =	evaluate.load("""glue"""     ,   """mrpc"""				)

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

  set_seed(__a				)
  _a      ,  _a  :       int             =	get_dataloaders(__a     ,   __a				)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _a  :       str             =	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""     ,   return_dict=__a				)

  # 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).
  _a  :       str             =	model.to(accelerator.device				)
  # Instantiate optimizer
  _a  :       List[str]             =	AdamW(params=model.parameters()     ,   lr=__a				)

  # Instantiate scheduler
  _a  :       Dict             =	get_linear_schedule_with_warmup(
      optimizer=__a     ,   num_warmup_steps=1_0_0     ,   num_training_steps=(len(__a				) * 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.

  _a      ,  _a      ,  _a      ,  _a      ,  _a  :       Optional[Any]             =	accelerator.prepare(
      __a     ,   __a     ,   __a     ,   __a     ,   __a				)

  # Now we train the model
  for epoch in range(__a				):
    model.train()
    for step, batch in enumerate(__a				):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device				)
      _a  :       Dict             =	model(**__a				)
      _a  :       Tuple             =	outputs.loss
      _a  :       Any             =	loss / gradient_accumulation_steps
      accelerator.backward(__a				)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

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

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       Tuple             =	argparse.ArgumentParser(description="""Simple example of training script."""				)
  parser.add_argument(
      """--mixed_precision"""     ,   type=__a     ,   default=__a     ,   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."""				)
  _a  :       Optional[int]             =	parser.parse_args()
  _a  :       Tuple             =	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
  training_function(__a     ,   __a				)


if __name__ == "__main__":
 main()








from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A    :					Dict =      Lock()







def    __lowerCamelCase					(							__a   :Dict    ,     __a   :List[str]    ,     __a   :Optional[int]    ,     __a   :Optional[int]    ,     __a   :Optional[Any]    ,     __a   :Optional[int]    ,     __a   :int    )		->				Dict:






		"""simple docstring"""

		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0    ,     1_0    ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(__a    )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						A__        =   rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						A__        =   min(__a    ,     __a    )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(__a    )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						A__        =   lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						A__        =   max(__a    ,     __a    )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(__a    )







def    __lowerCamelCase					(							__a   :List[str]    )		->				int:






		"""simple docstring"""

		A__        =   []
		A__        =   []
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe()    )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		A__        =   Pipe()
		A__        =   Pipe()
		process_array_.append(
		    Process(
		        target=__a    ,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])    ,     )    )
		A__        =   temp_rs
		A__        =   temp_rr

		for i in range(1    ,     len(__a    ) - 1    ):
				A__        =   Pipe()
				A__        =   Pipe()
				process_array_.append(
				    Process(
				        target=__a    ,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])    ,     )    )
				A__        =   temp_rs
				A__        =   temp_rr

		process_array_.append(
		    Process(
		        target=__a    ,     args=(
		            len(__a    ) - 1,
		            arr[len(__a    ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(__a    ) - 1],
		        )    ,     )    )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0    ,     len(__a    )    ):
				A__        =   result_pipe[p][0].recv()
				process_array_[p].join()
		return arr







def    __lowerCamelCase					(							)		->				str:






		"""simple docstring"""

		A__        =   list(range(1_0    ,     0    ,     -1    )    )
		print("""Initial List"""    )
		print(*__a    )
		A__        =   odd_even_transposition(__a    )
		print("""Sorted List\n"""    )
		print(*__a    )


if __name__ == "__main__":
					main()



