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      lowercase_						(						nn.Module		):



	"""simple docstring"""





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




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=0       )		->Optional[int]:

								# 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(__SCREAMING_SNAKE_CASE       )

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



	"""simple docstring"""





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




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=0       )		->List[str]:
								lowerCAmelCase				=	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_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Optional[Any]:
							with open(os.path.join(snake_case__     ,						'''model_args.pkl'''							)     ,						'''rb'''							) as f:
														lowerCAmelCase				=	pkl.load(snake_case__							)

							# load the original encoder
							lowerCAmelCase				=	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
							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=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__     ,						)

							lowerCAmelCase				=	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
							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(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(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
							lowerCAmelCase				=	torch.randint(0     ,						hf_config.vocab_size     ,						size=(4, 2_5_6)							)
							lowerCAmelCase				=	torch.ones_like(snake_case__							)
							# mask a few tokens to make sure masking is applied appropriately :)
							lowerCAmelCase				=	0

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


if __name__ == "__main__":
	lowercase__    :       int						=			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__    :       Union[str, Any]						=			parser.parse_args()

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




from __future__ import annotations

from enum import Enum




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      str     =       24
	UpperCAmelCase_						:      Dict     =       21
	UpperCAmelCase_						:      List[str]     =       18
	UpperCAmelCase_						:      Optional[int]     =       15
	UpperCAmelCase_						:      str     =       12
	UpperCAmelCase_						:      Union[str, Any]     =       9
	UpperCAmelCase_						:      List[Any]     =       6
	UpperCAmelCase_						:      Any     =       3
	UpperCAmelCase_						:      List[str]     =       2
	UpperCAmelCase_						:      Any     =       1
	UpperCAmelCase_						:      Any     =       -1
	UpperCAmelCase_						:      Any     =       -2
	UpperCAmelCase_						:      Optional[int]     =       -3
	UpperCAmelCase_						:      List[Any]     =       -6
	UpperCAmelCase_						:      Optional[int]     =       -9
	UpperCAmelCase_						:      str     =       -12
	UpperCAmelCase_						:      Union[str, Any]     =       -15
	UpperCAmelCase_						:      List[str]     =       -18
	UpperCAmelCase_						:      str     =       -21
	UpperCAmelCase_						:      Dict     =       -24




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      List[str]     =       8
	UpperCAmelCase_						:      Union[str, Any]     =       7
	UpperCAmelCase_						:      int     =       6
	UpperCAmelCase_						:      int     =       5
	UpperCAmelCase_						:      Any     =       4
	UpperCAmelCase_						:      Dict     =       3
	UpperCAmelCase_						:      Tuple     =       2
	UpperCAmelCase_						:      Any     =       1

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						)  ->			float:
							if isinstance(snake_case__     ,						snake_case__							):
														lowerCAmelCase				=	SIUnit[known_prefix.lower()]
							if isinstance(snake_case__     ,						snake_case__							):
														lowerCAmelCase				=	SIUnit[unknown_prefix.lower()]
							lowerCAmelCase				=	known_amount * (
							    1_0 ** (known_prefix.value - unknown_prefix.value)
							)
							return unknown_amount

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						)  ->			float:
							if isinstance(snake_case__     ,						snake_case__							):
														lowerCAmelCase				=	BinaryUnit[known_prefix.lower()]
							if isinstance(snake_case__     ,						snake_case__							):
														lowerCAmelCase				=	BinaryUnit[unknown_prefix.lower()]
							lowerCAmelCase				=	known_amount * (
							    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
							)
							return unknown_amount


if __name__ == "__main__":
	import doctest

	doctest.testmod()



