


"""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       __a	(   nn.Module       ):







				def __init__(   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






					super().__init__()
					lowerCAmelCase_			       = mega_args
					lowerCAmelCase_			       = nn.Embedding(UpperCAmelCase			,  self.mega_args.encoder_embed_dim      )
					lowerCAmelCase_			       = nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(UpperCAmelCase      )]      )
					lowerCAmelCase_			       = depth







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=True			,  UpperCAmelCase=0      ):

					'''simple docstring'''






					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(UpperCAmelCase      )

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

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







class       __a	(   nn.Module       ):







				def __init__(   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






					super().__init__()
					lowerCAmelCase_			       = MegaLM(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
					lowerCAmelCase_			       = nn.Linear(mega_args.encoder_embed_dim			,  UpperCAmelCase      )
					lowerCAmelCase_			       = nn.Dropout(p=0.1      )







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=True			,  UpperCAmelCase=0      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.mega(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
					return self.mlm_head(self.dropout(UpperCAmelCase      )      )




def     UpperCAmelCase    (					_lowercase   :      Union[str, Any] ,      _lowercase   :      List[Any] ,      _lowercase   :      Optional[Any]  )	->   Optional[int]:


	"""simple docstring"""




	with open(os.path.join(_lowercase ,      '''model_args.pkl'''  ) ,      '''rb'''  ) as f:
		lowerCAmelCase_			       = pkl.load(_lowercase  )

	# load the original encoder
	lowerCAmelCase_			       = OriginalMegaForMaskedLM(**_lowercase  ).eval()

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

	lowerCAmelCase_			       = MegaForMaskedLM(_lowercase  ).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(_lowercase  ) != 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(_lowercase  )

	# 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(_lowercase  )  )

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

	# test on a randomly generated input sequence
	lowerCAmelCase_			       = torch.randint(0 ,      hf_config.vocab_size ,      size=(4, 2_5_6)  )
	lowerCAmelCase_			       = torch.ones_like(_lowercase  )
	# mask a few tokens to make sure masking is applied appropriately :)
	lowerCAmelCase_			       = 0

	# run forward passes
	lowerCAmelCase_			       = original_mlm(_lowercase ,      _lowercase ,      batch_first=_lowercase ,      ignore_mask_value=0  )
	lowerCAmelCase_			       = hf_mlm(_lowercase ,      _lowercase  )[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(_lowercase ,      _lowercase ,      atol=1E-3  )

	if success:
		print('''Yay!'''  )
		hf_mlm.save_pretrained(_lowercase  )
	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(_lowercase  )
		tokenizer.save_pretrained(_lowercase  )


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)



"""simple docstring"""



def     UpperCAmelCase    (					_lowercase   :      list[int]  )	->   int:


	"""simple docstring"""




	lowerCAmelCase_			       = len(_lowercase  )
	if n in (0, 1):
		return 0
	return sum(query * (n - i - 1) for i, query in enumerate(sorted(_lowercase  )  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()