




'''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     UpperCAmelCase_     (nn.Module     ):







						"""simple docstring"""


						def __init__(     self      :       List[Any]							, _lowerCamelCase      :       Tuple							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       Tuple    ):





											"""simple docstring"""







											super().__init__()
											A_ :     Dict =							mega_args
											A_ :     str =							nn.Embedding(__SCREAMING_SNAKE_CASE							, self.mega_args.encoder_embed_dim    )
											A_ :     Any =							nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(__SCREAMING_SNAKE_CASE    )]    )
											A_ :     Optional[Any] =							depth







						def       _a    (     self      :       int							, _lowerCamelCase      :       Optional[Any]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       str=True							, _lowerCamelCase      :       Dict=0    ):





											"""simple docstring"""







											if batch_first:
																A_ :     str =							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:
																A_ :     Union[str, Any] =							1 - attention_mask

											# get token embeddings from IDs
											A_ :     str =							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:
																A_ :     int =							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     UpperCAmelCase_     (nn.Module     ):







						"""simple docstring"""


						def __init__(     self      :       Union[str, Any]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       int							, _lowerCamelCase      :       Optional[int]    ):





											"""simple docstring"""







											super().__init__()
											A_ :     Optional[Any] =							MegaLM(__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE    )
											A_ :     str =							nn.Linear(mega_args.encoder_embed_dim							, __SCREAMING_SNAKE_CASE    )
											A_ :     Tuple =							nn.Dropout(p=0.1    )







						def       _a    (     self      :       Dict							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       int							, _lowerCamelCase      :       List[str]=True							, _lowerCamelCase      :       Union[str, Any]=0    ):





											"""simple docstring"""







											A_ :     Any =							self.mega(__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE    )
											return self.mlm_head(self.dropout(__SCREAMING_SNAKE_CASE    )    )






def 						snake_case__    ( lowerCamelCase__				:   Any			,		lowerCamelCase__				:   Any			,		lowerCamelCase__				:   List[str]			)   ->   Optional[Any]:
					with open(os.path.join(snake_case__			,		'''model_args.pkl'''			)			,		'''rb'''			) as f:
										A_ :     int =							pkl.load(snake_case__			)

					# load the original encoder
					A_ :     Tuple =							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
					A_ :     Any =							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__			,		)

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

										if new_module_name:
															A_ :     Dict =							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():
										A_ :     Optional[Any] =							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
					A_ :     str =							torch.randint(0			,		hf_config.vocab_size			,		size=(4, 2_5_6)			)
					A_ :     int =							torch.ones_like(snake_case__			)
					# mask a few tokens to make sure masking is applied appropriately :)
					A_ :     Any =							0

					# run forward passes
					A_ :     Union[str, Any] =							original_mlm(snake_case__			,		snake_case__			,		batch_first=snake_case__			,		ignore_mask_value=0			)
					A_ :     Optional[Any] =							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
					A_ :     Tuple =							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'''			)
										A_ :     str =							AutoTokenizer.from_pretrained(snake_case__			)
										tokenizer.save_pretrained(snake_case__			)


if __name__ == "__main__":
							snake_case__          =						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""",
							)

							snake_case__          =						parser.parse_args()

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









'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__          =						logging.get_logger(__name__)

snake_case__          =						{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					'qdqbert'







						def __init__(     self      :       Union[str, Any]							, _lowerCamelCase      :       List[Any]=30522							, _lowerCamelCase      :       str=768							, _lowerCamelCase      :       Optional[Any]=12							, _lowerCamelCase      :       str=12							, _lowerCamelCase      :       List[Any]=3072							, _lowerCamelCase      :       Union[str, Any]="gelu"							, _lowerCamelCase      :       Dict=0.1							, _lowerCamelCase      :       Union[str, Any]=0.1							, _lowerCamelCase      :       Optional[int]=512							, _lowerCamelCase      :       Dict=2							, _lowerCamelCase      :       Optional[Any]=0.02							, _lowerCamelCase      :       List[Any]=1E-12							, _lowerCamelCase      :       Tuple=True							, _lowerCamelCase      :       Tuple=1							, _lowerCamelCase      :       Optional[Any]=0							, _lowerCamelCase      :       Optional[int]=2							, **_lowerCamelCase      :       List[str]							, ):





											"""simple docstring"""







											super().__init__(pad_token_id=_lowerCamelCase							, bos_token_id=_lowerCamelCase							, eos_token_id=_lowerCamelCase							, **_lowerCamelCase    )

											A_ :     Optional[Any] =							vocab_size
											A_ :     str =							max_position_embeddings
											A_ :     List[str] =							hidden_size
											A_ :     str =							num_hidden_layers
											A_ :     List[str] =							num_attention_heads
											A_ :     List[str] =							intermediate_size
											A_ :     int =							hidden_act
											A_ :     Tuple =							hidden_dropout_prob
											A_ :     Optional[Any] =							attention_probs_dropout_prob
											A_ :     Dict =							initializer_range
											A_ :     Optional[int] =							type_vocab_size
											A_ :     Any =							layer_norm_eps
											A_ :     Optional[int] =							use_cache



