
'''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			:       str      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       Optional[Any]							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       List[str]      ,							__magic_name__			:       Any      ,							__magic_name__			:       Any=True      ,							__magic_name__			:       int=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(a_							)

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

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






class    A     (	nn.Module    ):





	def __init__(					self			:       str      ,							__magic_name__			:       List[Any]      ,							__magic_name__			:       Any      ,							__magic_name__			:       str							):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__								=							MegaLM(a_      ,							a_      ,							a_							)
				lowerCAmelCase__								=							nn.Linear(mega_args.encoder_embed_dim      ,							a_							)
				lowerCAmelCase__								=							nn.Dropout(p=0.1							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       str      ,							__magic_name__			:       int=True      ,							__magic_name__			:       int=0							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.mega(a_      ,							a_      ,							a_      ,							a_							)
				return self.mlm_head(self.dropout(a_							)							)







def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     List[Any]      )					->       int:







			'''simple docstring'''



			with open(os.path.join(_UpperCamelCase						,	"model_args.pkl"      )						,	"rb"      ) as f:
						lowerCAmelCase__								=							pkl.load(_UpperCamelCase      )

			# load the original encoder
			lowerCAmelCase__								=							OriginalMegaForMaskedLM(**_UpperCamelCase      ).eval()

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

			lowerCAmelCase__								=							MegaForMaskedLM(_UpperCamelCase      ).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(_UpperCamelCase      ) != 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(_UpperCamelCase      )

			# 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(_UpperCamelCase      )      )

			# load the MLM head weights directly
			print(
			    "HF Mega MLM layer:"						,	hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(_UpperCamelCase						,	"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_56)      )
			lowerCAmelCase__								=							torch.ones_like(_UpperCamelCase      )
			# mask a few tokens to make sure masking is applied appropriately :)
			lowerCAmelCase__								=							0

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

			if success:
						print("Yay!"      )
						hf_mlm.save_pretrained(_UpperCamelCase      )
			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(_UpperCamelCase      )
						tokenizer.save_pretrained(_UpperCamelCase      )


if __name__ == "__main__":
		UpperCAmelCase__   : Dict					=		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__   : List[Any]					=		parser.parse_args()

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







'''simple docstring'''

from __future__ import annotations







def   A     (      UpperCamelCase_					:     int      )					->       list[int]:







			'''simple docstring'''



			assert (
			    isinstance(UpperCamelCase_						,	UpperCamelCase_      ) and mask > 0
			), F"""mask needs to be positive integer, your input {mask}"""
			lowerCAmelCase__								=							[]
			lowerCAmelCase__								=							mask

			while submask:
						all_submasks.append(UpperCamelCase_      )
						lowerCAmelCase__								=							(submask - 1) & mask

			return all_submasks


if __name__ == "__main__":
		import doctest

		doctest.testmod()





