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					):
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Dict:
  								super().__init__()
  								UpperCamelCase__         =	mega_args
  								UpperCamelCase__         =	nn.Embedding(__lowerCAmelCase    ,      self.mega_args.encoder_embed_dim       )
  								UpperCamelCase__         =	nn.ModuleList([MegaEncoderLayer(self.mega_args       ) for _ in range(__lowerCAmelCase       )]       )
  								UpperCamelCase__         =	depth
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase=True    ,      __lowerCAmelCase=0       )						->  Optional[int]:
  
  								# Mega expects embeddings to be (time, batch, embedding size), but
  								# Hugging Face returns tokens as (batch, time)
  								if batch_first:
  											UpperCamelCase__         =	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:
  											UpperCamelCase__         =	1 - attention_mask
  
  								# get token embeddings from IDs
  								UpperCamelCase__         =	self.embedding_layer(__lowerCAmelCase       )
  
  								# pass through the Mega layers
  								# input is (time, batch, encoder dim) and output is the same
  								for encoder in self.encoders:
  											UpperCamelCase__         =	encoder(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  								# return according to the shape specified
  								if batch_first:
  											# (T, B, H) --> (B, T, H)
  											return torch.transpose(__lowerCAmelCase    ,      0    ,      1       )
  								else:
  											return embeds
  
  
  
  
  class   _UpperCAmelCase	(   nn.Module					):
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Any:
  								super().__init__()
  								UpperCamelCase__         =	MegaLM(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  								UpperCamelCase__         =	nn.Linear(mega_args.encoder_embed_dim    ,      __lowerCAmelCase       )
  								UpperCamelCase__         =	nn.Dropout(p=0.1       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase=True    ,      __lowerCAmelCase=0       )						->  Optional[int]:
  								UpperCamelCase__         =	self.mega(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
  								return self.mlm_head(self.dropout(__lowerCAmelCase       )       )
  
  
  
  def     a						(      _lowercase   :     Any ,						_lowercase   :     Optional[Any] ,						_lowercase   :     List[str]       )     ->Dict:
  			with open(os.path.join(__lowerCamelCase ,						"""model_args.pkl"""       ) ,						"""rb"""       ) as f:
  						UpperCamelCase__         =	pkl.load(__lowerCamelCase       )
  
  			# load the original encoder
  			UpperCamelCase__         =	OriginalMegaForMaskedLM(**__lowerCamelCase       ).eval()
  
  			# load its weights
  			print(
  			    """Original Mega encoder:""" ,						original_mlm.mega.load_state_dict(
  			        torch.load(os.path.join(__lowerCamelCase ,						"""encoder_weights.pt"""       ) ,						map_location="""cpu"""       )       ) ,						)
  			print(
  			    """Original Mega MLM layer:""" ,						original_mlm.mlm_head.load_state_dict(
  			        torch.load(os.path.join(__lowerCamelCase ,						"""mlm_head_weights.pt"""       ) ,						map_location="""cpu"""       )       ) ,						)
  
  			# create a new config from the old one
  			UpperCamelCase__         =	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=__lowerCamelCase ,						norm_affine=__lowerCamelCase ,						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=__lowerCamelCase ,						add_lm_hidden_dense_layer=__lowerCamelCase ,						)
  
  			UpperCamelCase__         =	MegaForMaskedLM(__lowerCamelCase       ).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
  			UpperCamelCase__         =	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
  			UpperCamelCase__         =	original_mlm.mega.encoders.state_dict()
  			UpperCamelCase__         =	{}
  			for module_name in original_state_dict.keys():
  						UpperCamelCase__         =	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:
  												UpperCamelCase__         =	module_name.replace("""move.beta""" ,						"""ema_gate.ema_expansion_matrix"""       )
  									elif "mega_layer.beta" in module_name:
  												UpperCamelCase__         =	module_name.replace("""beta""" ,						"""qk_bias"""       )
  									else:
  												UpperCamelCase__         =	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:
  												UpperCamelCase__         =	module_name.replace("""move.gamma""" ,						"""ema_gate.kernel_projection_matrix"""       )
  									elif "mega_layer.gamma" in module_name:
  												UpperCamelCase__         =	module_name.replace("""gamma""" ,						"""qk_weight"""       )
  									else:
  												UpperCamelCase__         =	module_name.replace("""gamma""" ,						"""g_param"""       )
          # alpha is used in EMA and positional bias; renaming to improve readability
  						elif "move.alpha" in module_name:
  									UpperCamelCase__         =	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:
  									UpperCamelCase__         =	module_name.replace("""move.delta""" ,						"""ema_gate.damping_factor"""       )
  						# omega is only used in EMA; renaming to improve readability
  						elif "omega" in module_name:
  									UpperCamelCase__         =	module_name.replace("""move.omega""" ,						"""ema_gate.residual_weight"""       )
  
  						if new_module_name:
  									UpperCamelCase__         =	new_module_name
  
  			if len(__lowerCamelCase       ) != 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():
  						UpperCamelCase__         =	original_state_dict.pop(__lowerCamelCase       )
  
  			# 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(__lowerCamelCase       )       )
  
  			# load the MLM head weights directly
  			print(
  			    """HF Mega MLM layer:""" ,						hf_mlm.mlm_head.load_state_dict(
  			        torch.load(os.path.join(__lowerCamelCase ,						"""mlm_head_weights.pt"""       ) ,						map_location="""cpu"""       )       ) ,						)
  
  			# test on a randomly generated input sequence
  			UpperCamelCase__         =	torch.randint(0 ,						hf_config.vocab_size ,						size=(4, 2_5_6)       )
  			UpperCamelCase__         =	torch.ones_like(__lowerCamelCase       )
  			# mask a few tokens to make sure masking is applied appropriately :)
  			UpperCamelCase__         =	0
  
  			# run forward passes
  			UpperCamelCase__         =	original_mlm(__lowerCamelCase ,						__lowerCamelCase ,						batch_first=__lowerCamelCase ,						ignore_mask_value=0       )
  			UpperCamelCase__         =	hf_mlm(__lowerCamelCase ,						__lowerCamelCase       )[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
  			UpperCamelCase__         =	torch.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3       )
  
  			if success:
  						print("""Yay!"""       )
  						hf_mlm.save_pretrained(__lowerCamelCase       )
  			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"""       )
  						UpperCamelCase__         =	AutoTokenizer.from_pretrained(__lowerCamelCase       )
  						tokenizer.save_pretrained(__lowerCamelCase       )
  
  
  if __name__ == "__main__":
  				__lowerCAmelCase     =			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",
  				)
  
  				__lowerCAmelCase     =			parser.parse_args()
  
  				convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def     a						(      _lowercase   :     Optional[int]       )     ->List[str]:
  		UpperCamelCase__         =	len(_lowercase       )
  		for i in range(length - 1       ):
  				UpperCamelCase__         =	False
  				for j in range(length - 1 - i       ):
  						if collection[j] > collection[j + 1]:
  								UpperCamelCase__         =	True
  								UpperCamelCase__  ,			UpperCamelCase__         =	collection[j + 1], collection[j]
  				if not swapped:
  						break  # Stop iteration if the collection is sorted.
  		return collection
  
  
  if __name__ == "__main__":
  				import doctest
  				import time
  
  				doctest.testmod()
  
  				__lowerCAmelCase     =			input("Enter numbers separated by a comma:").strip()
  				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
  				__lowerCAmelCase     =			time.process_time()
  				print(*bubble_sort(unsorted), sep=",")
  				print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  
  
  