


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):
					def __init__(	self					:	str  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Optional[int]     )       ->				Union[str, Any]:



											'''simple docstring'''





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







					def   a (	self					:	Any  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Tuple=True  ,			_lowerCAmelCase					:	List[str]=0     )       ->				Tuple:



											'''simple docstring'''





											if batch_first:
																	__magic_name__ 		=			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:
																	__magic_name__ 		=			1 - attention_mask

											# get token embeddings from IDs
											__magic_name__ 		=			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:
																	__magic_name__ 		=			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     __snake_case							(      nn.Module):
					def __init__(	self					:	int  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	List[str]     )       ->				Any:



											'''simple docstring'''





											super().__init__()
											__magic_name__ 		=			MegaLM(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			nn.Linear(mega_args.encoder_embed_dim  ,			_lowerCAmelCase     )
											__magic_name__ 		=			nn.Dropout(p=0.1     )







					def   a (	self					:	str  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Union[str, Any]=True  ,			_lowerCAmelCase					:	Union[str, Any]=0     )       ->				Optional[Any]:



											'''simple docstring'''





											__magic_name__ 		=			self.mega(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     )
											return self.mlm_head(self.dropout(_lowerCAmelCase     )     )







def 							UpperCamelCase_  (				__snake_case			:						Dict	,			__snake_case			:						Optional[Any]	,			__snake_case			:						Optional[Any]     )->      Optional[int]:




						'''simple docstring'''
						with open(os.path.join(__snake_case	,			'model_args.pkl'     )	,			'rb'     ) as f:
												__magic_name__ 		=			pkl.load(__snake_case     )

						# load the original encoder
						__magic_name__ 		=			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
						__magic_name__ 		=			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	,			)

						__magic_name__ 		=			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
						__magic_name__ 		=			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
						__magic_name__ 		=			original_mlm.mega.encoders.state_dict()
						__magic_name__ 		=			{}
						for module_name in original_state_dict.keys():
												__magic_name__ 		=			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:
																								__magic_name__ 		=			module_name.replace('move.beta'	,			'ema_gate.ema_expansion_matrix'     )
																		elif "mega_layer.beta" in module_name:
																								__magic_name__ 		=			module_name.replace('beta'	,			'qk_bias'     )
																		else:
																								__magic_name__ 		=			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:
																								__magic_name__ 		=			module_name.replace('move.gamma'	,			'ema_gate.kernel_projection_matrix'     )
																		elif "mega_layer.gamma" in module_name:
																								__magic_name__ 		=			module_name.replace('gamma'	,			'qk_weight'     )
																		else:
																								__magic_name__ 		=			module_name.replace('gamma'	,			'g_param'     )
        # alpha is used in EMA and positional bias; renaming to improve readability
												elif "move.alpha" in module_name:
																		__magic_name__ 		=			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:
																		__magic_name__ 		=			module_name.replace('move.delta'	,			'ema_gate.damping_factor'     )
												# omega is only used in EMA; renaming to improve readability
												elif "omega" in module_name:
																		__magic_name__ 		=			module_name.replace('move.omega'	,			'ema_gate.residual_weight'     )

												if new_module_name:
																		__magic_name__ 		=			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():
												__magic_name__ 		=			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
						__magic_name__ 		=			torch.randint(0	,			hf_config.vocab_size	,			size=(4, 2_56)     )
						__magic_name__ 		=			torch.ones_like(__snake_case     )
						# mask a few tokens to make sure masking is applied appropriately :)
						__magic_name__ 		=			0

						# run forward passes
						__magic_name__ 		=			original_mlm(__snake_case	,			__snake_case	,			batch_first=__snake_case	,			ignore_mask_value=0     )
						__magic_name__ 		=			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
						__magic_name__ 		=			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'     )
												__magic_name__ 		=			AutoTokenizer.from_pretrained(__snake_case     )
												tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
					UpperCamelCase :				List[Any]						    =       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 :				Optional[int]						    =       parser.parse_args()

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





def 							UpperCamelCase_  (				__snake_case			:						str     )->      int:




						'''simple docstring'''
						__magic_name__ 		=			__magic_name__ 		=			0
						for letter in data:
												__magic_name__ 		=			(hash_ << 4) + ord(__snake_case     )
												__magic_name__ 		=			hash_ & 0xf000_0000
												if x != 0:
																		hash_ ^= x >> 24
												hash_ &= ~x
						return hash_


if __name__ == "__main__":
					import doctest

					doctest.testmod()

