code: 
  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 					_UpperCamelCase (      nn.Module	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
  													super().__init__()
  													_a     =			mega_args
  													_a     =			nn.Embedding(__SCREAMING_SNAKE_CASE		,				self.mega_args.encoder_embed_dim		)
  													_a     =			nn.ModuleList([MegaEncoderLayer(self.mega_args		) for _ in range(__SCREAMING_SNAKE_CASE		)]		)
  													_a     =			depth
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				int=True		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=0		):
  
  													# Mega expects embeddings to be (time, batch, embedding size), but
  													# Hugging Face returns tokens as (batch, time)
  													if batch_first:
  																			_a     =			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:
  																			_a     =			1 - attention_mask
  
  													# get token embeddings from IDs
  													_a     =			self.embedding_layer(__SCREAMING_SNAKE_CASE		)
  
  													# pass through the Mega layers
  													# input is (time, batch, encoder dim) and output is the same
  													for encoder in self.encoders:
  																			_a     =			encoder(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		)
  
  													# return according to the shape specified
  													if batch_first:
  																			# (T, B, H) --> (B, T, H)
  																			return torch.transpose(__SCREAMING_SNAKE_CASE		,				0		,				1		)
  													else:
  																			return embeds
  
  
  
  
  class 					_UpperCamelCase (      nn.Module	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Any		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Tuple		):
  													super().__init__()
  													_a     =			MegaLM(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		)
  													_a     =			nn.Linear(mega_args.encoder_embed_dim		,				__SCREAMING_SNAKE_CASE		)
  													_a     =			nn.Dropout(p=0.1		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				int=True		,				SCREAMING_SNAKE_CASE_  :				int=0		):
  													_a     =			self.mega(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		)
  													return self.mlm_head(self.dropout(__SCREAMING_SNAKE_CASE		)		)
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Optional[Any]:
  						with open(os.path.join(snake_case__							,     'model_args.pkl'				)							,     'rb'				) as f:
  												_a     =			pkl.load(snake_case__				)
  
  						# load the original encoder
  						_a     =			OriginalMegaForMaskedLM(**snake_case__				).eval()
  
  						# load its weights
  						print(
  						    'Original Mega encoder:'							,     original_mlm.mega.load_state_dict(
  						        torch.load(os.path.join(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(snake_case__							,     'mlm_head_weights.pt'				)							,     map_location='cpu'				)				)							,     )
  
  						# create a new config from the old one
  						_a     =			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=snake_case__							,     norm_affine=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=snake_case__							,     add_lm_hidden_dense_layer=snake_case__							,     )
  
  						_a     =			MegaForMaskedLM(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
  						_a     =			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
  						_a     =			original_mlm.mega.encoders.state_dict()
  						_a     =			{}
  						for module_name in original_state_dict.keys():
  												_a     =			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:
  																								_a     =			module_name.replace('move.beta'							,     'ema_gate.ema_expansion_matrix'				)
  																		elif "mega_layer.beta" in module_name:
  																								_a     =			module_name.replace('beta'							,     'qk_bias'				)
  																		else:
  																								_a     =			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:
  																								_a     =			module_name.replace('move.gamma'							,     'ema_gate.kernel_projection_matrix'				)
  																		elif "mega_layer.gamma" in module_name:
  																								_a     =			module_name.replace('gamma'							,     'qk_weight'				)
  																		else:
  																								_a     =			module_name.replace('gamma'							,     'g_param'				)
          # alpha is used in EMA and positional bias; renaming to improve readability
  												elif "move.alpha" in module_name:
  																		_a     =			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:
  																		_a     =			module_name.replace('move.delta'							,     'ema_gate.damping_factor'				)
  												# omega is only used in EMA; renaming to improve readability
  												elif "omega" in module_name:
  																		_a     =			module_name.replace('move.omega'							,     'ema_gate.residual_weight'				)
  
  												if new_module_name:
  																		_a     =			new_module_name
  
  						if len(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():
  												_a     =			original_state_dict.pop(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(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(snake_case__							,     'mlm_head_weights.pt'				)							,     map_location='cpu'				)				)							,     )
  
  						# test on a randomly generated input sequence
  						_a     =			torch.randint(0							,     hf_config.vocab_size							,     size=(4, 256)				)
  						_a     =			torch.ones_like(snake_case__				)
  						# mask a few tokens to make sure masking is applied appropriately :)
  						_a     =			0
  
  						# run forward passes
  						_a     =			original_mlm(snake_case__							,     snake_case__							,     batch_first=snake_case__							,     ignore_mask_value=0				)
  						_a     =			hf_mlm(snake_case__							,     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
  						_a     =			torch.allclose(snake_case__							,     snake_case__							,     atol=1e-3				)
  
  						if success:
  												print('Yay!'				)
  												hf_mlm.save_pretrained(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'				)
  												_a     =			AutoTokenizer.from_pretrained(snake_case__				)
  												tokenizer.save_pretrained(snake_case__				)
  
  
  if __name__ == "__main__":
  		lowercase_								=    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',
  		)
  
  		lowercase_								=    parser.parse_args()
  
  		convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
  
  
  
style_context: 
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  bool:
  						# must be
  						_a     =			[list(_UpperCAmelCase				) for row in matrix]
  						if matrix and isinstance(_UpperCAmelCase							,     _UpperCAmelCase				):
  												if isinstance(matrix[0]							,     _UpperCAmelCase				):
  																		_a     =			0
  																		for row in matrix:
  																								if prev_len == 0:
  																														_a     =			len(_UpperCAmelCase				)
  																														_a     =			True
  																								else:
  																														_a     =			prev_len == len(_UpperCAmelCase				)
  												else:
  																		_a     =			True
  						else:
  												_a     =			False
  
  						return result
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  None:
  						if check_matrix(_UpperCAmelCase				) and len(_UpperCAmelCase				) > 0:
  												_a     =			[list(_UpperCAmelCase				) for row in a]
  												_a     =			len(_UpperCAmelCase				)
  												if isinstance(a[0]							,     _UpperCAmelCase				):
  																		_a     =			len(a[0]				)
  												else:
  																		for dat in a:
  																								print(_UpperCAmelCase				)
  																		return
  
  												# horizotal printing increasing
  												for i in range(0							,     _UpperCAmelCase				):
  																		print(a[0][i]				)
  												# vertical printing down
  												for i in range(1							,     _UpperCAmelCase				):
  																		print(a[i][mat_col - 1]				)
  												# horizotal printing decreasing
  												if mat_row > 1:
  																		for i in range(mat_col - 2							,     -1							,     -1				):
  																								print(a[mat_row - 1][i]				)
          # vertical printing up
  												for i in range(mat_row - 2							,     0							,     -1				):
  																		print(a[i][0]				)
  												_a     =			[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
  												if len(_UpperCAmelCase				) > 0:
  																		spiral_print_clockwise(_UpperCAmelCase				)
  												else:
  																		return
  						else:
  												print('Not a valid matrix'				)
  												return
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  list[int]:
  						if matrix:
  												return list(matrix.pop(0				)				) + spiral_traversal(list(zip(*_UpperCAmelCase				)				)[::-1]				)
  						else:
  												return []
  
  
  # driver code
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  		lowercase_								=    [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
  		spiral_print_clockwise(a)
  
  
  