






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


A_						:						Optional[Any]						      =					16
A_						:						Tuple						      =					32

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__ = 1_6      ,       UpperCAmelCase__ = "bert-base-cased"    )			->							Optional[int]:
  UpperCamelCase_:   Union[str, Any]        =							AutoTokenizer.from_pretrained(UpperCAmelCase__    )
  UpperCamelCase_:   int        =							load_dataset('glue'      ,       'mrpc'    )

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

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

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  UpperCamelCase_:   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.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(UpperCAmelCase__      ,       padding='max_length'      ,       max_length=1_2_8      ,       return_tensors='pt'    )
    return tokenizer.pad(UpperCAmelCase__      ,       padding='longest'      ,       return_tensors='pt'    )

  # Instantiate dataloaders.
  UpperCamelCase_:   Optional[int]        =							DataLoader(
      tokenized_datasets['train']      ,       shuffle=UpperCAmelCase__      ,       collate_fn=UpperCAmelCase__      ,       batch_size=UpperCAmelCase__    )
  UpperCamelCase_:   List[Any]        =							DataLoader(
      tokenized_datasets['validation']      ,       shuffle=UpperCAmelCase__      ,       collate_fn=UpperCAmelCase__      ,       batch_size=UpperCAmelCase__    )

  return train_dataloader, eval_dataloader

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

  UpperCamelCase_:   Any        =							metric.compute()
  return eval_metric["accuracy"]

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							str:
  # Initialize accelerator
  UpperCamelCase_:   Tuple        =							Accelerator()

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

  set_seed(UpperCAmelCase__    )
  UpperCamelCase_ ,UpperCamelCase_:   List[str]        =							get_dataloaders(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

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

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

  if accelerator.state.deepspeed_plugin is not None:
    UpperCamelCase_:   Dict        =							accelerator.state.deepspeed_plugin.deepspeed_config[
        'gradient_accumulation_steps'
    ]
  else:
    UpperCamelCase_:   List[Any]        =							1
  UpperCamelCase_:   List[Any]        =							(len(UpperCAmelCase__    ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    UpperCamelCase_:   Union[str, Any]        =							get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase__      ,       num_warmup_steps=0      ,       num_training_steps=UpperCAmelCase__      ,       )
  else:
    UpperCamelCase_:   Union[str, Any]        =							DummyScheduler(UpperCAmelCase__      ,       total_num_steps=UpperCAmelCase__      ,       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.
  UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Optional[int]        =							accelerator.prepare(
      UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

  # We need to keep track of how many total steps we have iterated over
  UpperCamelCase_:   Optional[Any]        =							0
  # We also need to keep track of the stating epoch so files are named properly
  UpperCamelCase_:   str        =							0
  UpperCamelCase_:   int        =							evaluate.load('glue'      ,       'mrpc'    )
  UpperCamelCase_:   int        =							num_epochs

  if args.partial_train_epoch is not None:
    UpperCamelCase_:   Union[str, Any]        =							args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint    )
    UpperCamelCase_:   Union[str, Any]        =							args.resume_from_checkpoint.split('epoch_'    )[1]
    UpperCamelCase_:   Tuple        =							''
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    UpperCamelCase_:   Optional[int]        =							int(UpperCAmelCase__    ) + 1
    UpperCamelCase_:   str        =							evaluation_loop(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
    accelerator.print('resumed checkpoint performance:'      ,       UpperCAmelCase__    )
    accelerator.print('resumed checkpoint\'s scheduler\'s lr:'      ,       lr_scheduler.get_lr()[0]    )
    accelerator.print('resumed optimizers\'s lr:'      ,       optimizer.param_groups[0]['lr']    )
    with open(os.path.join(args.output_dir      ,       F'''state_{starting_epoch-1}.json'''    )      ,       'r'    ) as f:
      UpperCamelCase_:   Dict        =							json.load(UpperCAmelCase__    )
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  UpperCamelCase_:   Any        =							{}
  for epoch in range(UpperCAmelCase__      ,       UpperCAmelCase__    ):
    model.train()
    for step, batch in enumerate(UpperCAmelCase__    ):
      UpperCamelCase_:   str        =							model(**UpperCAmelCase__    )
      UpperCamelCase_:   List[str]        =							outputs.loss
      UpperCamelCase_:   str        =							loss / gradient_accumulation_steps
      accelerator.backward(UpperCAmelCase__    )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    UpperCamelCase_:   Tuple        =							F'''epoch_{epoch}'''
    UpperCamelCase_:   Union[str, Any]        =							os.path.join(args.output_dir      ,       UpperCAmelCase__    )
    accelerator.save_state(UpperCAmelCase__    )
    UpperCamelCase_:   List[Any]        =							evaluation_loop(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
    UpperCamelCase_:   Optional[Any]        =							accuracy
    UpperCamelCase_:   List[str]        =							lr_scheduler.get_lr()[0]
    UpperCamelCase_:   Optional[Any]        =							optimizer.param_groups[0]['lr']
    UpperCamelCase_:   Dict        =							epoch
    UpperCamelCase_:   Optional[int]        =							overall_step
    accelerator.print(F'''epoch {epoch}:'''      ,       UpperCAmelCase__    )

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir      ,       F'''state_{epoch}.json'''    )      ,       'w'    ) as f:
        json.dump(UpperCAmelCase__      ,       UpperCAmelCase__    )

def     snake_case   ()			->							Optional[Any]:
  UpperCamelCase_:   Optional[int]        =							argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
  parser.add_argument(
      '--model_name_or_path'      ,       type=UpperCAmelCase__      ,       default='bert-base-cased'      ,       help='Path to pretrained model or model identifier from huggingface.co/models.'      ,       required=UpperCAmelCase__      ,       )
  parser.add_argument(
      '--output_dir'      ,       type=UpperCAmelCase__      ,       default='.'      ,       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'      ,       )
  parser.add_argument(
      '--resume_from_checkpoint'      ,       type=UpperCAmelCase__      ,       default=UpperCAmelCase__      ,       help='If the training should continue from a checkpoint folder.'      ,       )
  parser.add_argument(
      '--partial_train_epoch'      ,       type=UpperCAmelCase__      ,       default=UpperCAmelCase__      ,       help='If passed, the training will stop after this number of epochs.'      ,       )
  parser.add_argument(
      '--num_epochs'      ,       type=UpperCAmelCase__      ,       default=2      ,       help='Number of train epochs.'      ,       )
  UpperCamelCase_:   str        =							parser.parse_args()
  UpperCamelCase_:   Dict        =							{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

  training_function(UpperCAmelCase__      ,       UpperCAmelCase__    )


if __name__ == "__main__":
   main()


import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	return input_array.reshape((input_array.size, 1)      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= np.nan
	for i in range(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	Tuple									= features[:, labels == i]
		UpperCAmelCase__:	int									= data.mean(1      )
		# Centralize the data of class i
		UpperCAmelCase__:	Optional[Any]									= data - column_reshape(SCREAMING_SNAKE_CASE      )
		if i > 0:
			# If covariance_sum is not None
			covariance_sum += np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      )
		else:
			# If covariance_sum is np.nan (i.e. first loop)
			UpperCAmelCase__:	Dict									= np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      )

	return covariance_sum / features.shape[1]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= features.mean(1      )
	UpperCAmelCase__:	Optional[Any]									= np.nan
	for i in range(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	List[Any]									= features[:, labels == i]
		UpperCAmelCase__:	Optional[Any]									= data.shape[1]
		UpperCAmelCase__:	List[Any]									= data.mean(1      )
		if i > 0:
			# If covariance_sum is not None
			covariance_sum += device_data * np.dot(
			    column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )	,(column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )).T	,)
		else:
			# If covariance_sum is np.nan (i.e. first loop)
			UpperCAmelCase__:	Tuple									= device_data * np.dot(
			    column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )	,(column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )).T	,)

	return covariance_sum / features.shape[1]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):

	# Check if the features have been loaded
	if features.any():
		UpperCAmelCase__:	Tuple									= features.mean(1      )
		# Center the dataset
		UpperCAmelCase__:	List[Any]									= features - np.reshape(SCREAMING_SNAKE_CASE	,(data_mean.size, 1)      )
		UpperCAmelCase__:	Optional[int]									= np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      ) / features.shape[1]
		UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= np.linalg.eigh(SCREAMING_SNAKE_CASE      )
		# Take all the columns in the reverse order (-1), and then takes only the first
		UpperCAmelCase__:	Optional[Any]									= eigenvectors[:, ::-1][:, 0:dimensions]
		# Project the database on the new space
		UpperCAmelCase__:	Tuple									= np.dot(filtered_eigenvectors.T	,SCREAMING_SNAKE_CASE      )
		logging.info("Principal Component Analysis computed"      )

		return projected_data
	else:
		logging.basicConfig(level=logging.ERROR	,format="%(message)s"	,force=SCREAMING_SNAKE_CASE      )
		logging.error("Dataset empty"      )
		raise AssertionError



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	assert classes > dimensions

	# Check if features have been already loaded
	if features.any:
		UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= eigh(
		    covariance_between_classes(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )	,covariance_within_classes(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )	,)
		UpperCAmelCase__:	int									= eigenvectors[:, ::-1][:, :dimensions]
		UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Optional[Any]									= np.linalg.svd(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Optional[Any]									= svd_matrix[:, 0:dimensions]
		UpperCAmelCase__:	Optional[int]									= np.dot(filtered_svd_matrix.T	,SCREAMING_SNAKE_CASE      )
		logging.info("Linear Discriminant Analysis computed"      )

		return projected_data
	else:
		logging.basicConfig(level=logging.ERROR	,format="%(message)s"	,force=SCREAMING_SNAKE_CASE      )
		logging.error("Dataset empty"      )
		raise AssertionError



def 			_A	(      ):
	# Create dummy dataset with 2 classes and 3 features
	UpperCAmelCase__:	List[str]									= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]      )
	UpperCAmelCase__:	Optional[int]									= np.array([0, 0, 0, 1, 1]      )
	UpperCAmelCase__:	List[Any]									= 2
	UpperCAmelCase__:	Union[str, Any]									= 2

	# Assert that the function raises an AssertionError if dimensions > classes
	with pytest.raises(SCREAMING_SNAKE_CASE      ) as error_info:
		UpperCAmelCase__:	List[Any]									= linear_discriminant_analysis(
		    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		if isinstance(SCREAMING_SNAKE_CASE	,np.ndarray      ):
			raise AssertionError(
			    "Did not raise AssertionError for dimensions > classes"      )
		assert error_info.type is AssertionError



def 			_A	(      ):
	UpperCAmelCase__:	Optional[Any]									= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]      )
	UpperCAmelCase__:	Optional[int]									= 2
	UpperCAmelCase__:	Union[str, Any]									= np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]      )

	with pytest.raises(SCREAMING_SNAKE_CASE      ) as error_info:
		UpperCAmelCase__:	Any									= principal_component_analysis(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		if not np.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
			raise AssertionError
		assert error_info.type is AssertionError


if __name__ == "__main__":
					import doctest

					doctest.testmod()