
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


lowercase_ 				=			16
lowercase_ 				=			32


def 							a__					(							snake_case       ,			snake_case = 16       ,			snake_case = "bert-base-cased"							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	List[str]					=		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_							)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		load_dataset('''glue'''       ,			'''mrpc'''							)

  def tokenize_function(snake_case							):
    # max_length=None => use the model max length (it's actually the default)
    __SCREAMING_SNAKE_CASE      :	Tuple					=		tokenizer(examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=SCREAMING_SNAKE_CASE_       ,			max_length=SCREAMING_SNAKE_CASE_							)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  __SCREAMING_SNAKE_CASE      :	List[str]					=		datasets.map(
      SCREAMING_SNAKE_CASE_       ,			batched=SCREAMING_SNAKE_CASE_       ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,			load_from_cache_file=SCREAMING_SNAKE_CASE_							)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  __SCREAMING_SNAKE_CASE      :	str					=		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.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(SCREAMING_SNAKE_CASE_       ,			padding='''max_length'''       ,			max_length=128       ,			return_tensors='''pt'''							)
    return tokenizer.pad(SCREAMING_SNAKE_CASE_       ,			padding='''longest'''       ,			return_tensors='''pt'''							)

  # Instantiate dataloaders.
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		DataLoader(
      tokenized_datasets['''train''']       ,			shuffle=SCREAMING_SNAKE_CASE_       ,			collate_fn=SCREAMING_SNAKE_CASE_       ,			batch_size=SCREAMING_SNAKE_CASE_							)
  __SCREAMING_SNAKE_CASE      :	List[str]					=		DataLoader(
      tokenized_datasets['''validation''']       ,			shuffle=SCREAMING_SNAKE_CASE_       ,			collate_fn=SCREAMING_SNAKE_CASE_       ,			batch_size=SCREAMING_SNAKE_CASE_							)

  return train_dataloader, eval_dataloader


def 							a__					(							snake_case       ,			snake_case							):





  """simple docstring"""
  # Initialize accelerator
  __SCREAMING_SNAKE_CASE      :	List[str]					=		Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __SCREAMING_SNAKE_CASE      :	int					=		config['''lr''']
  __SCREAMING_SNAKE_CASE      :	Dict					=		int(config['''num_epochs''']							)
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		int(config['''seed''']							)
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		int(config['''batch_size''']							)
  __SCREAMING_SNAKE_CASE      :	Dict					=		args.model_name_or_path

  set_seed(SCREAMING_SNAKE_CASE_							)
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		get_dataloaders(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_							)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  __SCREAMING_SNAKE_CASE      :	str					=		AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_       ,			return_dict=SCREAMING_SNAKE_CASE_							)

  # Instantiate optimizer
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  __SCREAMING_SNAKE_CASE      :	List[str]					=		optimizer_cls(params=model.parameters()       ,			lr=SCREAMING_SNAKE_CASE_							)

  if accelerator.state.deepspeed_plugin is not None:
    __SCREAMING_SNAKE_CASE      :	List[str]					=		accelerator.state.deepspeed_plugin.deepspeed_config[
        '''gradient_accumulation_steps'''
    ]
  else:
    __SCREAMING_SNAKE_CASE      :	int					=		1
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(len(SCREAMING_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
  ):
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		get_linear_schedule_with_warmup(
        optimizer=SCREAMING_SNAKE_CASE_       ,			num_warmup_steps=0       ,			num_training_steps=SCREAMING_SNAKE_CASE_       ,			)
  else:
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		DummyScheduler(SCREAMING_SNAKE_CASE_       ,			total_num_steps=SCREAMING_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.
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[int]					=		accelerator.prepare(
      SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_							)

  # We need to keep track of how many total steps we have iterated over
  __SCREAMING_SNAKE_CASE      :	str					=		0
  # We also need to keep track of the stating epoch so files are named properly
  __SCREAMING_SNAKE_CASE      :	List[str]					=		0

  # Now we train the model
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		evaluate.load('''glue'''       ,			'''mrpc'''							)
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		0
  __SCREAMING_SNAKE_CASE      :	str					=		{}
  for epoch in range(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_							):
    model.train()
    for step, batch in enumerate(SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE      :	Tuple					=		model(**SCREAMING_SNAKE_CASE_							)
      __SCREAMING_SNAKE_CASE      :	List[Any]					=		outputs.loss
      __SCREAMING_SNAKE_CASE      :	Any					=		loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE_							)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

    model.eval()
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		0
    for step, batch in enumerate(SCREAMING_SNAKE_CASE_							):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device							)
      with torch.no_grad():
        __SCREAMING_SNAKE_CASE      :	List[Any]					=		model(**SCREAMING_SNAKE_CASE_							)
      __SCREAMING_SNAKE_CASE      :	Any					=		outputs.logits.argmax(dim=-1							)
      # It is slightly faster to call this once, than multiple times
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[int]					=		accelerator.gather(
          (predictions, batch['''labels'''])							)  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
        if step == len(SCREAMING_SNAKE_CASE_							) - 1:
          __SCREAMING_SNAKE_CASE      :	str					=		predictions[: len(eval_dataloader.dataset							) - samples_seen]
          __SCREAMING_SNAKE_CASE      :	int					=		references[: len(eval_dataloader.dataset							) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=SCREAMING_SNAKE_CASE_       ,			references=SCREAMING_SNAKE_CASE_       ,			)

    __SCREAMING_SNAKE_CASE      :	int					=		metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''       ,			SCREAMING_SNAKE_CASE_							)
    __SCREAMING_SNAKE_CASE      :	str					=		eval_metric['''accuracy''']

    if best_performance < eval_metric["accuracy"]:
      __SCREAMING_SNAKE_CASE      :	int					=		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(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_							)


def 							a__					(							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''							)
  parser.add_argument(
      '''--model_name_or_path'''       ,			type=SCREAMING_SNAKE_CASE_       ,			default='''bert-base-cased'''       ,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,			required=SCREAMING_SNAKE_CASE_       ,			)
  parser.add_argument(
      '''--output_dir'''       ,			type=SCREAMING_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=SCREAMING_SNAKE_CASE_       ,			default=SCREAMING_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=SCREAMING_SNAKE_CASE_       ,			default=3       ,			help='''Number of train epochs.'''       ,			)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		parser.parse_args()
  __SCREAMING_SNAKE_CASE      :	Tuple					=		{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
  training_function(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
   main()

'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


__snake_case						  =		logging.get_logger(__name__)





class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       int   ,				*lowercase_      :       Tuple   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


    warnings.warn(
        """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
        """ Please use PoolFormerImageProcessor instead."""   ,				lowercase_   ,				)
    super().__init__(*lowercase_   ,				**lowercase_      )

