




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							):




							'''simple docstring'''



							def __init__(       self							:						List[str]							,  a							:						int							,  a							:						Dict							,  a							:						List[Any]      )      ->       int:
										super().__init__()
										SCREAMING_SNAKE_CASE     =       mega_args
										SCREAMING_SNAKE_CASE     =       nn.Embedding(a							,  self.mega_args.encoder_embed_dim      )
										SCREAMING_SNAKE_CASE     =       nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(a      )]      )
										SCREAMING_SNAKE_CASE     =       depth







							def  _UpperCAmelCase  (       self							:						Optional[int]							,  a							:						Optional[int]							,  a							:						List[str]							,  a							:						List[str]=True							,  a							:						Dict=0      )      ->       Optional[int]:

										# Mega expects embeddings to be (time, batch, embedding size), but
										# Hugging Face returns tokens as (batch, time)
										if batch_first:
													SCREAMING_SNAKE_CASE     =       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:
													SCREAMING_SNAKE_CASE     =       1 - attention_mask

										# get token embeddings from IDs
										SCREAMING_SNAKE_CASE     =       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:
													SCREAMING_SNAKE_CASE     =       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       UpperCAmelCase_   ( nn.Module							):




							'''simple docstring'''



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







							def  _UpperCAmelCase  (       self							:						List[str]							,  a							:						Dict							,  a							:						Any							,  a							:						List[str]=True							,  a							:						int=0      )      ->       Optional[Any]:
										SCREAMING_SNAKE_CASE     =       self.mega(a							,  a							,  a							,  a      )
										return self.mlm_head(self.dropout(a      )      )





def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			with open(os.path.join(SCREAMING_SNAKE_CASE       ,						"""model_args.pkl""" )       ,						"""rb""" ) as f:
						SCREAMING_SNAKE_CASE     =       pkl.load(SCREAMING_SNAKE_CASE )

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

			# load its weights
			print(
			    """Original Mega encoder:"""       ,						original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(SCREAMING_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(SCREAMING_SNAKE_CASE       ,						"""mlm_head_weights.pt""" )       ,						map_location="""cpu""" ) )       ,						)

			# create a new config from the old one
			SCREAMING_SNAKE_CASE     =       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=SCREAMING_SNAKE_CASE       ,						norm_affine=SCREAMING_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=SCREAMING_SNAKE_CASE       ,						add_lm_hidden_dense_layer=SCREAMING_SNAKE_CASE       ,						)

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

						if new_module_name:
									SCREAMING_SNAKE_CASE     =       new_module_name

			if len(SCREAMING_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():
						SCREAMING_SNAKE_CASE     =       original_state_dict.pop(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE       ,						"""mlm_head_weights.pt""" )       ,						map_location="""cpu""" ) )       ,						)

			# test on a randomly generated input sequence
			SCREAMING_SNAKE_CASE     =       torch.randint(0       ,						hf_config.vocab_size       ,						size=(4, 2_56) )
			SCREAMING_SNAKE_CASE     =       torch.ones_like(SCREAMING_SNAKE_CASE )
			# mask a few tokens to make sure masking is applied appropriately :)
			SCREAMING_SNAKE_CASE     =       0

			# run forward passes
			SCREAMING_SNAKE_CASE     =       original_mlm(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						batch_first=SCREAMING_SNAKE_CASE       ,						ignore_mask_value=0 )
			SCREAMING_SNAKE_CASE     =       hf_mlm(SCREAMING_SNAKE_CASE       ,						SCREAMING_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
			SCREAMING_SNAKE_CASE     =       torch.allclose(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						atol=1E-3 )

			if success:
						print("""Yay!""" )
						hf_mlm.save_pretrained(SCREAMING_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""" )
						SCREAMING_SNAKE_CASE     =       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
						tokenizer.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				Tuple    						=	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""",
		)

		__A    :				Any    						=	parser.parse_args()

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












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A    :				List[str]    						=	logging.get_logger(__name__)

__A    :				List[Any]    						=	{
    """mnaylor/mega-base-wikitext""": """https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json""",
}




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  '''mega'''





							def __init__(       self							:						Any							,  a							:						List[str]=30_522							,  a							:						Dict=128							,  a							:						Dict=4							,  a							:						Union[str, Any]=256							,  a							:						Tuple=16							,  a							:						Any=True							,  a							:						Dict=64							,  a							:						Union[str, Any]=False							,  a							:						Tuple=-1							,  a							:						str=None							,  a							:						int=True							,  a							:						Optional[int]="scalenorm"							,  a							:						Optional[int]=True							,  a							:						Dict="silu"							,  a							:						Optional[Any]="softmax"							,  a							:						Any=0.1							,  a							:						List[Any]=0.1							,  a							:						Tuple=0.1							,  a							:						Optional[int]=False							,  a							:						List[str]=True							,  a							:						Optional[int]=256							,  a							:						List[str]=True							,  a							:						int=0.1							,  a							:						str=2_048							,  a							:						Tuple=False							,  a							:						Optional[int]=2							,  a							:						Optional[int]=0.02							,  a							:						Optional[int]=0.2							,  a							:						Union[str, Any]=0.02							,  a							:						str=1.0							,  a							:						Dict=1							,  a							:						Union[str, Any]=0							,  a							:						Dict=2							,  a							:						List[str]="rotary"							,  a							:						Union[str, Any]=None							,  a							:						List[Any]=True							,  a							:						Tuple=True							,  **a							:						Optional[int]							,  )      ->       Dict:
										super().__init__(pad_token_id=a							,  bos_token_id=a							,  eos_token_id=a							,  **a      )

										SCREAMING_SNAKE_CASE     =       vocab_size
										SCREAMING_SNAKE_CASE     =       hidden_size
										SCREAMING_SNAKE_CASE     =       num_hidden_layers
										SCREAMING_SNAKE_CASE     =       activation
										SCREAMING_SNAKE_CASE     =       attention_activation
										SCREAMING_SNAKE_CASE     =       intermediate_size
										SCREAMING_SNAKE_CASE     =       ema_projection_size
										SCREAMING_SNAKE_CASE     =       bidirectional
										SCREAMING_SNAKE_CASE     =       shared_representation_size
										SCREAMING_SNAKE_CASE     =       use_chunking
										SCREAMING_SNAKE_CASE     =       chunk_size
										SCREAMING_SNAKE_CASE     =       truncation
										SCREAMING_SNAKE_CASE     =       normalize_before_mega
										SCREAMING_SNAKE_CASE     =       normalization_type
										SCREAMING_SNAKE_CASE     =       norm_affine
										SCREAMING_SNAKE_CASE     =       dropout_prob
										SCREAMING_SNAKE_CASE     =       hidden_dropout_prob
										SCREAMING_SNAKE_CASE     =       attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE     =       use_feature_dropout
										SCREAMING_SNAKE_CASE     =       use_normalized_ffn
										SCREAMING_SNAKE_CASE     =       nffn_hidden_size
										SCREAMING_SNAKE_CASE     =       normalize_before_ffn
										SCREAMING_SNAKE_CASE     =       nffn_activation_dropout_prob
										SCREAMING_SNAKE_CASE     =       max_positions
										SCREAMING_SNAKE_CASE     =       add_token_type_embeddings
										SCREAMING_SNAKE_CASE     =       type_vocab_size
										SCREAMING_SNAKE_CASE     =       initializer_range
										SCREAMING_SNAKE_CASE     =       ema_delta_alpha_range
										SCREAMING_SNAKE_CASE     =       ema_beta_range
										SCREAMING_SNAKE_CASE     =       ema_gamma_omega_range
										SCREAMING_SNAKE_CASE     =       relative_positional_bias
										SCREAMING_SNAKE_CASE     =       use_cache
										SCREAMING_SNAKE_CASE     =       classifier_dropout
										SCREAMING_SNAKE_CASE     =       add_lm_hidden_dense_layer
										SCREAMING_SNAKE_CASE     =       1  # not used but required by Hugging Face





class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							@property
							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]      )






