

import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

A_	:			Optional[int]  	=      collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
A_	:			List[Any]  	=      'https://storage.googleapis.com/cvdf-datasets/mnist/'

def 						UpperCamelCase (lowercase_:			Dict )					->				Tuple:
	A__							:   Optional[int]					       =		numpy.dtype(numpy.uintaa ).newbyteorder(""">""" )
	return numpy.frombuffer(bytestream.read(4 )   ,				dtype=lowercase_ )[0]

@deprecated(lowercase_   ,				"""Please use tf.data to implement this functionality.""" )
def 						UpperCamelCase (lowercase_:			Tuple )					->				Tuple:
	print("""Extracting"""   ,				f.name )
	with gzip.GzipFile(fileobj=lowercase_ ) as bytestream:
		A__							:   Dict					       =		_readaa(lowercase_ )
		if magic != 2051:
			raise ValueError(
			    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name) )
		A__							:   Tuple					       =		_readaa(lowercase_ )
		A__							:   List[str]					       =		_readaa(lowercase_ )
		A__							:   str					       =		_readaa(lowercase_ )
		A__							:   Tuple					       =		bytestream.read(rows * cols * num_images )
		A__							:   List[str]					       =		numpy.frombuffer(lowercase_   ,				dtype=numpy.uinta )
		A__							:   str					       =		data.reshape(lowercase_   ,				lowercase_   ,				lowercase_   ,				1 )
		return data

@deprecated(lowercase_   ,				"""Please use tf.one_hot on tensors.""" )
def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			str )					->				Dict:
	A__							:   str					       =		labels_dense.shape[0]
	A__							:   str					       =		numpy.arange(lowercase_ ) * num_classes
	A__							:   int					       =		numpy.zeros((num_labels, num_classes) )
	A__							:   Any					       =		1
	return labels_one_hot

@deprecated(lowercase_   ,				"""Please use tf.data to implement this functionality.""" )
def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			str=False   ,				lowercase_:			Dict=10 )					->				Optional[Any]:
	print("""Extracting"""   ,				f.name )
	with gzip.GzipFile(fileobj=lowercase_ ) as bytestream:
		A__							:   Optional[int]					       =		_readaa(lowercase_ )
		if magic != 2049:
			raise ValueError(
			    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name) )
		A__							:   Any					       =		_readaa(lowercase_ )
		A__							:   Union[str, Any]					       =		bytestream.read(lowercase_ )
		A__							:   Optional[Any]					       =		numpy.frombuffer(lowercase_   ,				dtype=numpy.uinta )
		if one_hot:
			return _dense_to_one_hot(lowercase_   ,				lowercase_ )
		return labels



class    _a						:


					'''simple docstring'''






					@deprecated(
					    A__   ,	"""Please use alternatives such as official/mnist/_DataSet.py"""
					    """ from tensorflow/models."""   ,	)
					def __init__(			self   ,	A__   ,	A__   ,	A__=False   ,	A__=False   ,	A__=dtypes.floataa   ,	A__=True   ,	A__=None   ,	):
						A__       ,       A__							:   str					       =		random_seed.get_seed(A__      )
						# If op level seed is not set, use whatever graph level seed is returned
						numpy.random.seed(seeda if seed is None else seeda      )
						A__							:   str					       =		dtypes.as_dtype(A__      ).base_dtype
						if dtype not in (dtypes.uinta, dtypes.floataa):
							raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype      )
						if fake_data:
							A__							:   Union[str, Any]					       =		1_0000
							A__							:   str					       =		one_hot
						else:
							assert (
							    images.shape[0] == labels.shape[0]
							), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
							A__							:   List[str]					       =		images.shape[0]

							# Convert shape from [num examples, rows, columns, depth]
							# to [num examples, rows*columns] (assuming depth == 1)
							if reshape:
								assert images.shape[3] == 1
								A__							:   int					       =		images.reshape(
								    images.shape[0]   ,	images.shape[1] * images.shape[2]      )
							if dtype == dtypes.floataa:
								# Convert from [0, 255] -> [0.0, 1.0].
								A__							:   Optional[Any]					       =		images.astype(numpy.floataa      )
								A__							:   List[str]					       =		numpy.multiply(A__   ,	1.0 / 2_5_5.0      )
						A__							:   Union[str, Any]					       =		images
						A__							:   List[Any]					       =		labels
						A__							:   Dict					       =		0
						A__							:   Optional[Any]					       =		0






					@property
					def   __A      (			self      ):
						return self._images






					@property
					def   __A      (			self      ):
						return self._labels






					@property
					def   __A      (			self      ):
						return self._num_examples






					@property
					def   __A      (			self      ):
						return self._epochs_completed






					def   __A      (			self   ,	A__   ,	A__=False   ,	A__=True      ):
						if fake_data:
							A__							:   Dict					       =		[1] * 784
							A__							:   int					       =		[1] + [0] * 9 if self.one_hot else 0
							return (
							    [fake_image for _ in range(A__      )],
							    [fake_label for _ in range(A__      )],
							)
						A__							:   Optional[Any]					       =		self._index_in_epoch
						# Shuffle for the first epoch
						if self._epochs_completed == 0 and start == 0 and shuffle:
							A__							:   Union[str, Any]					       =		numpy.arange(self._num_examples      )
							numpy.random.shuffle(A__      )
							A__							:   int					       =		self.images[perma]
							A__							:   Tuple					       =		self.labels[perma]
						# Go to the next epoch
						if start + batch_size > self._num_examples:
							# Finished epoch
							self._epochs_completed += 1
							# Get the rest examples in this epoch
							A__							:   List[Any]					       =		self._num_examples - start
							A__							:   str					       =		self._images[start : self._num_examples]
							A__							:   str					       =		self._labels[start : self._num_examples]
							# Shuffle the data
							if shuffle:
								A__							:   Optional[int]					       =		numpy.arange(self._num_examples      )
								numpy.random.shuffle(A__      )
								A__							:   List[Any]					       =		self.images[perm]
								A__							:   Optional[int]					       =		self.labels[perm]
							# Start next epoch
							A__							:   Dict					       =		0
							A__							:   Dict					       =		batch_size - rest_num_examples
							A__							:   Optional[Any]					       =		self._index_in_epoch
							A__							:   Optional[Any]					       =		self._images[start:end]
							A__							:   int					       =		self._labels[start:end]
							return (
							    numpy.concatenate((images_rest_part, images_new_part)   ,	axis=0      ),
							    numpy.concatenate((labels_rest_part, labels_new_part)   ,	axis=0      ),
							)
						else:
							self._index_in_epoch += batch_size
							A__							:   List[Any]					       =		self._index_in_epoch
							return self._images[start:end], self._labels[start:end]



@deprecated(lowercase_   ,				"""Please write your own downloading logic.""" )
def 						UpperCamelCase (lowercase_:			Optional[Any]   ,				lowercase_:			Tuple   ,				lowercase_:			List[str] )					->				List[Any]:
	if not gfile.Exists(lowercase_ ):
		gfile.MakeDirs(lowercase_ )
	A__							:   Dict					       =		os.path.join(lowercase_   ,				lowercase_ )
	if not gfile.Exists(lowercase_ ):
		urllib.request.urlretrieve(lowercase_   ,				lowercase_ )  # noqa: S310
		with gfile.GFile(lowercase_ ) as f:
			A__							:   Dict					       =		f.size()
		print("""Successfully downloaded"""   ,				lowercase_   ,				lowercase_   ,				"""bytes.""" )
	return filepath


@deprecated(
    lowercase_   ,				"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')""" )
def 						UpperCamelCase (lowercase_:			Union[str, Any]   ,				lowercase_:			Optional[int]=False   ,				lowercase_:			Dict=False   ,				lowercase_:			Dict=dtypes.floataa   ,				lowercase_:			List[Any]=True   ,				lowercase_:			Optional[Any]=5000   ,				lowercase_:			Optional[int]=None   ,				lowercase_:			Any=DEFAULT_SOURCE_URL   ,				)					->				Tuple:
	if fake_data:

		def fake():
			return _DataSet(
			    []   ,				[]   ,				fake_data=lowercase_   ,				one_hot=lowercase_   ,				dtype=lowercase_   ,				seed=lowercase_ )

		A__							:   Optional[int]					       =		fake()
		A__							:   Tuple					       =		fake()
		A__							:   Tuple					       =		fake()
		return _Datasets(train=lowercase_   ,				validation=lowercase_   ,				test=lowercase_ )

	if not source_url:  # empty string check
		A__							:   Optional[Any]					       =		DEFAULT_SOURCE_URL

	A__							:   Optional[int]					       =		"""train-images-idx3-ubyte.gz"""
	A__							:   Optional[Any]					       =		"""train-labels-idx1-ubyte.gz"""
	A__							:   int					       =		"""t10k-images-idx3-ubyte.gz"""
	A__							:   int					       =		"""t10k-labels-idx1-ubyte.gz"""

	A__							:   Union[str, Any]					       =		_maybe_download(
	    lowercase_   ,				lowercase_   ,				source_url + train_images_file )
	with gfile.Open(lowercase_   ,				"""rb""" ) as f:
		A__							:   Optional[int]					       =		_extract_images(lowercase_ )

	A__							:   Optional[Any]					       =		_maybe_download(
	    lowercase_   ,				lowercase_   ,				source_url + train_labels_file )
	with gfile.Open(lowercase_   ,				"""rb""" ) as f:
		A__							:   Optional[int]					       =		_extract_labels(lowercase_   ,				one_hot=lowercase_ )

	A__							:   Any					       =		_maybe_download(
	    lowercase_   ,				lowercase_   ,				source_url + test_images_file )
	with gfile.Open(lowercase_   ,				"""rb""" ) as f:
		A__							:   int					       =		_extract_images(lowercase_ )

	A__							:   Optional[int]					       =		_maybe_download(
	    lowercase_   ,				lowercase_   ,				source_url + test_labels_file )
	with gfile.Open(lowercase_   ,				"""rb""" ) as f:
		A__							:   Optional[int]					       =		_extract_labels(lowercase_   ,				one_hot=lowercase_ )

	if not 0 <= validation_size <= len(lowercase_ ):
		A__							:   int					       =		(
		    """Validation size should be between 0 and """
		    f"""{len(lowercase_ )}. Received: {validation_size}."""
		)
		raise ValueError(lowercase_ )

	A__							:   Tuple					       =		train_images[:validation_size]
	A__							:   Optional[int]					       =		train_labels[:validation_size]
	A__							:   int					       =		train_images[validation_size:]
	A__							:   Optional[int]					       =		train_labels[validation_size:]

	A__							:   Any					       =		{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

	A__							:   int					       =		_DataSet(lowercase_   ,				lowercase_   ,				**lowercase_ )
	A__							:   Optional[Any]					       =		_DataSet(lowercase_   ,				lowercase_   ,				**lowercase_ )
	A__							:   int					       =		_DataSet(lowercase_   ,				lowercase_   ,				**lowercase_ )

	return _Datasets(train=lowercase_   ,				validation=lowercase_   ,				test=lowercase_ )






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_	:			Optional[int]  	=      32

def 						UpperCamelCase (lowercase_:			Accelerator   ,				lowercase_:			int = 16   ,				lowercase_:			str = "bert-base-cased" )					->				List[str]:
	A__							:   int					       =		AutoTokenizer.from_pretrained(lowercase_ )
	A__							:   Union[str, Any]					       =		load_dataset("""glue"""   ,				"""mrpc""" )

	def tokenize_function(lowercase_:			Tuple ):
		# max_length=None => use the model max length (it's actually the default)
		A__							:   Optional[Any]					       =		tokenizer(examples["""sentence1"""]   ,				examples["""sentence2"""]   ,				truncation=lowercase_   ,				max_length=lowercase_ )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	A__							:   int					       =		datasets.map(
	    lowercase_   ,				batched=lowercase_   ,				remove_columns=["""idx""", """sentence1""", """sentence2"""]   ,				load_from_cache_file=lowercase_ )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A__							:   int					       =		tokenized_datasets.rename_column("""label"""   ,				"""labels""" )

	def collate_fn(lowercase_:			Optional[int] ):
		# 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(lowercase_   ,				padding="""max_length"""   ,				max_length=128   ,				return_tensors="""pt""" )
		return tokenizer.pad(lowercase_   ,				padding="""longest"""   ,				return_tensors="""pt""" )

	# Instantiate dataloaders.
	A__							:   int					       =		DataLoader(
	    tokenized_datasets["""train"""]   ,				shuffle=lowercase_   ,				collate_fn=lowercase_   ,				batch_size=lowercase_ )
	A__							:   Optional[Any]					       =		DataLoader(
	    tokenized_datasets["""validation"""]   ,				shuffle=lowercase_   ,				collate_fn=lowercase_   ,				batch_size=lowercase_ )

	return train_dataloader, eval_dataloader

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

	A__							:   int					       =		metric.compute()
	return eval_metric["accuracy"]

def 						UpperCamelCase (lowercase_:			List[Any]   ,				lowercase_:			str )					->				List[str]:
	# Initialize accelerator
	A__							:   str					       =		Accelerator()

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

	set_seed(lowercase_ )
	A__       ,       A__							:   Optional[Any]					       =		get_dataloaders(lowercase_   ,				lowercase_   ,				lowercase_ )

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

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

	if accelerator.state.deepspeed_plugin is not None:
		A__							:   List[Any]					       =		accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		A__							:   Optional[int]					       =		1
	A__							:   Optional[int]					       =		(len(lowercase_ ) * num_epochs) // gradient_accumulation_steps

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

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

	if args.partial_train_epoch is not None:
		A__							:   Tuple					       =		args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint )
		A__							:   Dict					       =		args.resume_from_checkpoint.split("""epoch_""" )[1]
		A__							:   int					       =		""""""
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		A__							:   Any					       =		int(lowercase_ ) + 1
		A__							:   Any					       =		evaluation_loop(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )
		accelerator.print("""resumed checkpoint performance:"""   ,				lowercase_ )
		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:
			A__							:   int					       =		json.load(lowercase_ )
			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
	A__							:   Optional[Any]					       =		{}
	for epoch in range(lowercase_   ,				lowercase_ ):
		model.train()
		for step, batch in enumerate(lowercase_ ):
			A__							:   int					       =		model(**lowercase_ )
			A__							:   int					       =		outputs.loss
			A__							:   int					       =		loss / gradient_accumulation_steps
			accelerator.backward(lowercase_ )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		A__							:   Any					       =		f"""epoch_{epoch}"""
		A__							:   int					       =		os.path.join(args.output_dir   ,				lowercase_ )
		accelerator.save_state(lowercase_ )
		A__							:   List[Any]					       =		evaluation_loop(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )
		A__							:   Tuple					       =		accuracy
		A__							:   Optional[Any]					       =		lr_scheduler.get_lr()[0]
		A__							:   Tuple					       =		optimizer.param_groups[0]["""lr"""]
		A__							:   int					       =		epoch
		A__							:   int					       =		overall_step
		accelerator.print(f"""epoch {epoch}:"""   ,				lowercase_ )

		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(lowercase_   ,				lowercase_ )

def 						UpperCamelCase ()					->				int:
	A__							:   Optional[int]					       =		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
	parser.add_argument(
	    """--model_name_or_path"""   ,				type=lowercase_   ,				default="""bert-base-cased"""   ,				help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,				required=lowercase_   ,				)
	parser.add_argument(
	    """--output_dir"""   ,				type=lowercase_   ,				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=lowercase_   ,				default=lowercase_   ,				help="""If the training should continue from a checkpoint folder."""   ,				)
	parser.add_argument(
	    """--partial_train_epoch"""   ,				type=lowercase_   ,				default=lowercase_   ,				help="""If passed, the training will stop after this number of epochs."""   ,				)
	parser.add_argument(
	    """--num_epochs"""   ,				type=lowercase_   ,				default=2   ,				help="""Number of train epochs."""   ,				)
	A__							:   List[str]					       =		parser.parse_args()
	A__							:   List[str]					       =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

	training_function(lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	main()



