



'''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 checkpointing 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
#
########################################################################

UpperCAmelCase			=    16
UpperCAmelCase			=    32






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Accelerator							,		_SCREAMING_SNAKE_CASE     :						int = 16							)			->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	AutoTokenizer.from_pretrained("""bert-base-cased"""							)
	lowerCAmelCase    =	load_dataset("""glue"""							,		"""mrpc"""							)

	def tokenize_function(_SCREAMING_SNAKE_CASE     :						str							):
		# max_length=None => use the model max length (it's actually the default)
		lowerCAmelCase    =	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
	# starting with the main process first:
	with accelerator.main_process_first():
		lowerCAmelCase    =	datasets.map(
		    _SCREAMING_SNAKE_CASE							,		batched=_SCREAMING_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
	lowerCAmelCase    =	tokenized_datasets.rename_column("""label"""							,		"""labels"""							)

	def collate_fn(_SCREAMING_SNAKE_CASE     :						Optional[Any]							):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		lowerCAmelCase    =	128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			lowerCAmelCase    =	16
		elif accelerator.mixed_precision != "no":
			lowerCAmelCase    =	8
		else:
			lowerCAmelCase    =	None

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

	# Instantiate dataloaders.
	lowerCAmelCase    =	DataLoader(
	    tokenized_datasets["""train"""]							,		shuffle=_SCREAMING_SNAKE_CASE							,		collate_fn=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	DataLoader(
	    tokenized_datasets["""validation"""]							,		shuffle=_SCREAMING_SNAKE_CASE							,		collate_fn=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_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

				UpperCAmelCase			=    mocked_dataloaders  # noqa: F811






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str							,		_SCREAMING_SNAKE_CASE     :						List[str]							)			->						List[str]:







	"""simple docstring"""
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,		_SCREAMING_SNAKE_CASE							) == "1":
		lowerCAmelCase    =	2
	# Initialize accelerator
	lowerCAmelCase    =	Accelerator(cpu=args.cpu							,		mixed_precision=args.mixed_precision							)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCAmelCase    =	config["""lr"""]
	lowerCAmelCase    =	int(config["""num_epochs"""]							)
	lowerCAmelCase    =	int(config["""seed"""]							)
	lowerCAmelCase    =	int(config["""batch_size"""]							)

	# New Code #
	# Parse out whether we are saving every epoch or after a certain number of batches
	if hasattr(args.checkpointing_steps							,		"""isdigit"""							):
		if args.checkpointing_steps == "epoch":
			lowerCAmelCase    =	args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			lowerCAmelCase    =	int(args.checkpointing_steps							)
		else:
			raise ValueError(
			    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'							)
	else:
		lowerCAmelCase    =	None

	set_seed(_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase,   lowerCAmelCase    =	get_dataloaders(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	evaluate.load("""glue"""							,		"""mrpc"""							)

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

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCAmelCase    =	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,		return_dict=_SCREAMING_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).
	lowerCAmelCase    =	model.to(accelerator.device							)

	# Instantiate optimizer
	lowerCAmelCase    =	AdamW(params=model.parameters()							,		lr=_SCREAMING_SNAKE_CASE							)

	# Instantiate scheduler
	lowerCAmelCase    =	get_linear_schedule_with_warmup(
	    optimizer=_SCREAMING_SNAKE_CASE							,		num_warmup_steps=100							,		num_training_steps=(len(_SCREAMING_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.
	lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	accelerator.prepare(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	# New Code #
	# We need to keep track of how many total steps we have iterated over
	lowerCAmelCase    =	0
	# We also need to keep track of the stating epoch so files are named properly
	lowerCAmelCase    =	0

	# We need to load the checkpoint back in before training here with `load_state`
	# The total number of epochs is adjusted based on where the state is being loaded from,
	# as we assume continuation of the same training script
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'							)
			accelerator.load_state(args.resume_from_checkpoint							)
			lowerCAmelCase    =	os.path.basename(args.resume_from_checkpoint							)
		else:
			# Get the most recent checkpoint
			lowerCAmelCase    =	[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
			dirs.sort(key=os.path.getctime							)
			lowerCAmelCase    =	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		lowerCAmelCase    =	os.path.splitext(_SCREAMING_SNAKE_CASE							)[0]

		if "epoch" in training_difference:
			lowerCAmelCase    =	int(training_difference.replace("""epoch_"""							,		""""""							)							) + 1
			lowerCAmelCase    =	None
		else:
			lowerCAmelCase    =	int(training_difference.replace("""step_"""							,		""""""							)							)
			lowerCAmelCase    =	resume_step // len(_SCREAMING_SNAKE_CASE							)
			resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE							)

    # Now we train the model
	for epoch in range(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
		model.train()
		# New Code #
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			lowerCAmelCase    =	accelerator.skip_first_batches(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			lowerCAmelCase    =	train_dataloader
		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							)
			lowerCAmelCase    =	model(**_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase    =	outputs.loss
			lowerCAmelCase    =	loss / gradient_accumulation_steps
			accelerator.backward(_SCREAMING_SNAKE_CASE							)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
			# New Code #
			overall_step += 1

			# New Code #
			# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
			# These are saved to folders named `step_{overall_step}`
			# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
			# If mixed precision was used, will also save a "scalar.bin" file
			if isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase    =	f'step_{overall_step}'
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						lowerCAmelCase    =	os.path.join(args.output_dir							,		_SCREAMING_SNAKE_CASE							)
					accelerator.save_state(_SCREAMING_SNAKE_CASE							)

		model.eval()
		for step, batch in enumerate(_SCREAMING_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():
				lowerCAmelCase    =	model(**_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase    =	outputs.logits.argmax(dim=-1							)
			lowerCAmelCase,   lowerCAmelCase    =	accelerator.gather_for_metrics((predictions, batch["""labels"""])							)
			metric.add_batch(
			    predictions=_SCREAMING_SNAKE_CASE							,		references=_SCREAMING_SNAKE_CASE							,		)

		lowerCAmelCase    =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,		_SCREAMING_SNAKE_CASE							)

		# New Code #
		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
		# These are saved to folders named `epoch_{epoch}`
		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
		# If mixed precision was used, will also save a "scalar.bin" file
		if checkpointing_steps == "epoch":
			lowerCAmelCase    =	f'epoch_{epoch}'
			if args.output_dir is not None:
				lowerCAmelCase    =	os.path.join(args.output_dir							,		_SCREAMING_SNAKE_CASE							)
			accelerator.save_state(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		(					)			->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	argparse.ArgumentParser(description="""Simple example of training script."""							)
	parser.add_argument(
	    """--mixed_precision"""							,		type=_SCREAMING_SNAKE_CASE							,		default=_SCREAMING_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(
	    """--checkpointing_steps"""							,		type=_SCREAMING_SNAKE_CASE							,		default=_SCREAMING_SNAKE_CASE							,		help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""							,		)
	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(
	    """--resume_from_checkpoint"""							,		type=_SCREAMING_SNAKE_CASE							,		default=_SCREAMING_SNAKE_CASE							,		help="""If the training should continue from a checkpoint folder."""							,		)
	lowerCAmelCase    =	parser.parse_args()
	lowerCAmelCase    =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
				main()




'''simple docstring'''

import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
				from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
				raise ImportError('You need to install the version of fairseq from the Mega repo!')

class __snake_case(      nn.Module					):


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_	,       A_      )	->	Dict:
							super().__init__()
							lowerCAmelCase    =	mega_args
							lowerCAmelCase    =	nn.Embedding(A_	,       self.mega_args.encoder_embed_dim      )
							lowerCAmelCase    =	nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(A_      )]      )
							lowerCAmelCase    =	depth




						def 				__snake_case    (					self	,       A_	,       A_	,       A_=True	,       A_=0      )	->	List[Any]:

							# Mega expects embeddings to be (time, batch, embedding size), but
							# Hugging Face returns tokens as (batch, time)
							if batch_first:
								lowerCAmelCase    =	input_ids.T

							# to make things more confusing, Mega expects the attention mask to
							# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
							# which is the opposite of what HF returns
							if ignore_mask_value == 0:
								lowerCAmelCase    =	1 - attention_mask

							# get token embeddings from IDs
							lowerCAmelCase    =	self.embedding_layer(A_      )

							# pass through the Mega layers
							# input is (time, batch, encoder dim) and output is the same
							for encoder in self.encoders:
								lowerCAmelCase    =	encoder(A_	,       A_      )

							# return according to the shape specified
							if batch_first:
								# (T, B, H) --> (B, T, H)
								return torch.transpose(A_	,       0	,       1      )
							else:
								return embeds







class __snake_case(      nn.Module					):


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_	,       A_      )	->	Optional[Any]:
							super().__init__()
							lowerCAmelCase    =	MegaLM(A_	,       A_	,       A_      )
							lowerCAmelCase    =	nn.Linear(mega_args.encoder_embed_dim	,       A_      )
							lowerCAmelCase    =	nn.Dropout(p=0.1      )




						def 				__snake_case    (					self	,       A_	,       A_	,       A_=True	,       A_=0      )	->	List[Any]:
							lowerCAmelCase    =	self.mega(A_	,       A_	,       A_	,       A_      )
							return self.mlm_head(self.dropout(A_      )      )







def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						List[str]							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							)			->						List[Any]:







	"""simple docstring"""
	with open(os.path.join(_SCREAMING_SNAKE_CASE							,		"""model_args.pkl"""							)							,		"""rb"""							) as f:
		lowerCAmelCase    =	pkl.load(_SCREAMING_SNAKE_CASE							)

	# load the original encoder
	lowerCAmelCase    =	OriginalMegaForMaskedLM(**_SCREAMING_SNAKE_CASE							).eval()

	# load its weights
	print(
	    """Original Mega encoder:"""							,		original_mlm.mega.load_state_dict(
	        torch.load(os.path.join(_SCREAMING_SNAKE_CASE							,		"""encoder_weights.pt"""							)							,		map_location="""cpu"""							)							)							,		)
	print(
	    """Original Mega MLM layer:"""							,		original_mlm.mlm_head.load_state_dict(
	        torch.load(os.path.join(_SCREAMING_SNAKE_CASE							,		"""mlm_head_weights.pt"""							)							,		map_location="""cpu"""							)							)							,		)

	# create a new config from the old one
	lowerCAmelCase    =	MegaConfig(
	    num_hidden_layers=mega_original_args["""depth"""]							,		vocab_size=mega_original_args["""vocab_size"""]							,		hidden_size=mega_original_args["""mega_args"""].encoder_embed_dim							,		shared_representation_size=mega_original_args["""mega_args"""].encoder_z_dim							,		intermediate_size=mega_original_args["""mega_args"""].encoder_hidden_dim							,		ema_projection_size=mega_original_args["""mega_args"""].encoder_n_dim							,		dropout_prob=mega_original_args["""mega_args"""].dropout							,		attention_probs_dropout_prob=mega_original_args["""mega_args"""].attention_dropout							,		hidden_dropout_prob=mega_original_args["""mega_args"""].hidden_dropout							,		activation=mega_original_args["""mega_args"""].activation_fn							,		attention_activation=mega_original_args["""mega_args"""].attention_activation_fn							,		bidirectional=mega_original_args["""mega_args"""].bidirectional							,		use_chunking=mega_original_args["""mega_args"""].encoder_chunk_size > 0							,		chunk_size=mega_original_args["""mega_args"""].encoder_chunk_size							,		truncation=mega_original_args["""mega_args"""].truncation_length							,		normalization_type=mega_original_args["""mega_args"""].normalization_type							,		normalize_before_mega=_SCREAMING_SNAKE_CASE							,		norm_affine=_SCREAMING_SNAKE_CASE							,		use_feature_dropout=mega_original_args["""mega_args"""].feature_dropout							,		relative_positional_bias=mega_original_args["""mega_args"""].rel_pos_bias							,		max_positions=mega_original_args["""mega_args"""].max_source_positions							,		nffn_hidden_size=mega_original_args["""mega_args"""].encoder_ffn_embed_dim							,		normalize_before_ffn=mega_original_args["""mega_args"""].normalize_before							,		nffn_activation_dropout_prob=0.0							,		add_token_type_embeddings=_SCREAMING_SNAKE_CASE							,		add_lm_hidden_dense_layer=_SCREAMING_SNAKE_CASE							,		)

	lowerCAmelCase    =	MegaForMaskedLM(_SCREAMING_SNAKE_CASE							).eval()

	# the originl checkpoint just uses nn.Embedding for the word embeddings
	# we use a wrapper module for embeddings to add support for positional embeddings
	lowerCAmelCase    =	original_mlm.mega.embedding_layer.weight

	# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
	# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
	# also renaming previously confusing parameter names
	lowerCAmelCase    =	original_mlm.mega.encoders.state_dict()
	lowerCAmelCase    =	{}
	for module_name in original_state_dict.keys():
		lowerCAmelCase    =	None
		# have to handle gamma, beta, and alpha differently due to their use
		# in multiple modules within the original repository;
		# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
		# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
		if "beta" in module_name:
			# EMA sub-layers were always called "move" in the original repo
			if "move.beta" in module_name:
				lowerCAmelCase    =	module_name.replace("""move.beta"""							,		"""ema_gate.ema_expansion_matrix"""							)
			elif "mega_layer.beta" in module_name:
				lowerCAmelCase    =	module_name.replace("""beta"""							,		"""qk_bias"""							)
			else:
				lowerCAmelCase    =	module_name.replace("""beta"""							,		"""b_param"""							)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
		elif "gamma" in module_name:
			if "move.gamma" in module_name:
				lowerCAmelCase    =	module_name.replace("""move.gamma"""							,		"""ema_gate.kernel_projection_matrix"""							)
			elif "mega_layer.gamma" in module_name:
				lowerCAmelCase    =	module_name.replace("""gamma"""							,		"""qk_weight"""							)
			else:
				lowerCAmelCase    =	module_name.replace("""gamma"""							,		"""g_param"""							)
        # alpha is used in EMA and positional bias; renaming to improve readability
		elif "move.alpha" in module_name:
			lowerCAmelCase    =	module_name.replace("""move.alpha"""							,		"""ema_gate.decay_factor"""							)
		# delta is only used in EMA; renaming to improve readability
		elif "move.delta" in module_name:
			lowerCAmelCase    =	module_name.replace("""move.delta"""							,		"""ema_gate.damping_factor"""							)
		# omega is only used in EMA; renaming to improve readability
		elif "omega" in module_name:
			lowerCAmelCase    =	module_name.replace("""move.omega"""							,		"""ema_gate.residual_weight"""							)

		if new_module_name:
			lowerCAmelCase    =	new_module_name

	if len(_SCREAMING_SNAKE_CASE							) != 0:
		print(f'Renaming these keys: {updated_keys.keys()}'							)
	else:
		print("""No need to rename state dict entries"""							)
	for old, new in updated_keys.items():
		lowerCAmelCase    =	original_state_dict.pop(_SCREAMING_SNAKE_CASE							)

	# now attempt to load the state dictionary with updated names
	# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
	print("""HF Mega encoder:"""							,		hf_mlm.mega.layers.load_state_dict(_SCREAMING_SNAKE_CASE							)							)

	# load the MLM head weights directly
	print(
	    """HF Mega MLM layer:"""							,		hf_mlm.mlm_head.load_state_dict(
	        torch.load(os.path.join(_SCREAMING_SNAKE_CASE							,		"""mlm_head_weights.pt"""							)							,		map_location="""cpu"""							)							)							,		)

	# test on a randomly generated input sequence
	lowerCAmelCase    =	torch.randint(0							,		hf_config.vocab_size							,		size=(4, 256)							)
	lowerCAmelCase    =	torch.ones_like(_SCREAMING_SNAKE_CASE							)
	# mask a few tokens to make sure masking is applied appropriately :)
	lowerCAmelCase    =	0

	# run forward passes
	lowerCAmelCase    =	original_mlm(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		batch_first=_SCREAMING_SNAKE_CASE							,		ignore_mask_value=0							)
	lowerCAmelCase    =	hf_mlm(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)[0]

	# print shapes and diff
	print(f'original output {original_output.shape}'							)
	print(f'hf output {hf_output.shape}'							)
	print(f'max diff: {(original_output - hf_output).max()}'							)  # 0.0
	lowerCAmelCase    =	torch.allclose(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		atol=1E-3							)

	if success:
		print("""Yay!"""							)
		hf_mlm.save_pretrained(_SCREAMING_SNAKE_CASE							)
	else:
		raise RuntimeError(f'Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'							)

	if includes_tokenizer:
		print("""Transferring tokenizer"""							)
		lowerCAmelCase    =	AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE							)
		tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
				UpperCAmelCase			=    argparse.ArgumentParser()

				parser.add_argument(
				    '--pretrained_checkpoint_path',
				    default=None,
				    type=str,
				    required=True,
				    help='Point to the directory containing your model weights using the official Mega repo',
				)

				parser.add_argument(
				    '--output_path', default=None, type=str, required=True, help='Location to save the Hugging Face version'
				)

				parser.add_argument(
				    '--includes_tokenizer',
				    action='store_true',
				    help='Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo',
				)

				UpperCAmelCase			=    parser.parse_args()

				convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)