






'''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  _SCREAMING_SNAKE_CASE				(		nn.Module						):


			'''simple docstring'''





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

									'''simple docstring'''







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







			def 					A   ( self				:    Optional[int]			,   lowercase				:    Union[str, Any]			,   lowercase				:    str			,   lowercase				:    str=True			,   lowercase				:    Optional[int]=0      )				->     int:

									'''simple docstring'''







									if batch_first:
															UpperCamelCase__       =			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:
															UpperCamelCase__       =			1 - attention_mask

									# get token embeddings from IDs
									UpperCamelCase__       =			self.embedding_layer(lowercase      )

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

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


			'''simple docstring'''





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

									'''simple docstring'''







									super().__init__()
									UpperCamelCase__       =			MegaLM(lowercase			,   lowercase			,   lowercase      )
									UpperCamelCase__       =			nn.Linear(mega_args.encoder_embed_dim			,   lowercase      )
									UpperCamelCase__       =			nn.Dropout(p=0.1      )







			def 					A   ( self				:    Any			,   lowercase				:    Optional[int]			,   lowercase				:    int			,   lowercase				:    List[str]=True			,   lowercase				:    str=0      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			self.mega(lowercase			,   lowercase			,   lowercase			,   lowercase      )
									return self.mlm_head(self.dropout(lowercase      )      )







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





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

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

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

						# create a new config from the old one
						UpperCamelCase__       =			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=_A    ,   norm_affine=_A    ,   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=_A    ,   add_lm_hidden_dense_layer=_A    ,   )

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

												if new_module_name:
																		UpperCamelCase__       =			new_module_name

						if len(_A       ) != 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():
												UpperCamelCase__       =			original_state_dict.pop(_A       )

						# 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(_A       )       )

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

						# test on a randomly generated input sequence
						UpperCamelCase__       =			torch.randint(0    ,   hf_config.vocab_size    ,   size=(4, 256)       )
						UpperCamelCase__       =			torch.ones_like(_A       )
						# mask a few tokens to make sure masking is applied appropriately :)
						UpperCamelCase__       =			0

						# run forward passes
						UpperCamelCase__       =			original_mlm(_A    ,   _A    ,   batch_first=_A    ,   ignore_mask_value=0       )
						UpperCamelCase__       =			hf_mlm(_A    ,   _A       )[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
						UpperCamelCase__       =			torch.allclose(_A    ,   _A    ,   atol=1e-3       )

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


if __name__ == "__main__":
					lowerCamelCase_					:			List[str]     						=				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''',
					)

					lowerCamelCase_					:			Optional[Any]     						=				parser.parse_args()

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








'''simple docstring'''


def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			len(_A       )

						# Set the gap value to be decreased by a factor of 1.3
						# after each iteration
						UpperCamelCase__       =			1.3

						# Continue sorting until the gap is 1
						while gap > 1:
												# Decrease the gap value
												UpperCamelCase__       =			int(gap / shrink       )

												# Sort the elements using insertion sort
												for i in range(_A    ,   len(_A       )       ):
																		UpperCamelCase__       =			collection[i]
																		UpperCamelCase__       =			i
																		while j >= gap and collection[j - gap] > temp:
																								UpperCamelCase__       =			collection[j - gap]
																								j -= gap
																		UpperCamelCase__       =			temp

						return collection


if __name__ == "__main__":
					import doctest

					doctest.testmod()
