

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							:	Optional[int] , a							:	Optional[Any] , a							:	Optional[int] , a							:	List[str]		)							->    Any:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    mega_args
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    nn.Embedding(a , self.mega_args.encoder_embed_dim		)
				SCREAMING_SNAKE_CASE				: Any			       =    nn.ModuleList([MegaEncoderLayer(self.mega_args		) for _ in range(a		)]		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    depth






			def 							__UpperCamelCase	(     self							:	int , a							:	Any , a							:	Tuple , a							:	Dict=True , a							:	List[str]=0		)							->    Any:

				"""simple docstring"""

				if batch_first:
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    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				: Optional[int]			       =    1 - attention_mask

				# get token embeddings from IDs
				SCREAMING_SNAKE_CASE				: int			       =    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				: List[str]			       =    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							:	Union[str, Any] , a							:	List[Any] , a							:	Any , a							:	int		)							->    Tuple:

				"""simple docstring"""

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






			def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	str , a							:	str , a							:	List[str]=True , a							:	Any=0		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    self.mega(a , a , a , a		)
				return self.mlm_head(self.dropout(a		)		)

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	with open(os.path.join(_a							,     "model_args.pkl")							,     "rb") as f:
		SCREAMING_SNAKE_CASE				: List[str]			       =    pkl.load(_a)

	# load the original encoder
	SCREAMING_SNAKE_CASE				: int			       =    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
	SCREAMING_SNAKE_CASE				: int			       =    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							,     )

	SCREAMING_SNAKE_CASE				: List[Any]			       =    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
	SCREAMING_SNAKE_CASE				: int			       =    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				: str			       =    original_mlm.mega.encoders.state_dict()
	SCREAMING_SNAKE_CASE				: List[str]			       =    {}
	for module_name in original_state_dict.keys():
		SCREAMING_SNAKE_CASE				: 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:
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    module_name.replace("move.beta"							,     "ema_gate.ema_expansion_matrix")
			elif "mega_layer.beta" in module_name:
				SCREAMING_SNAKE_CASE				: Dict			       =    module_name.replace("beta"							,     "qk_bias")
			else:
				SCREAMING_SNAKE_CASE				: Tuple			       =    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				: Union[str, Any]			       =    module_name.replace("move.gamma"							,     "ema_gate.kernel_projection_matrix")
			elif "mega_layer.gamma" in module_name:
				SCREAMING_SNAKE_CASE				: List[str]			       =    module_name.replace("gamma"							,     "qk_weight")
			else:
				SCREAMING_SNAKE_CASE				: List[str]			       =    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				: List[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:
			SCREAMING_SNAKE_CASE				: 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:
			SCREAMING_SNAKE_CASE				: Optional[int]			       =    module_name.replace("move.omega"							,     "ema_gate.residual_weight")

		if new_module_name:
			SCREAMING_SNAKE_CASE				: str			       =    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():
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    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
	SCREAMING_SNAKE_CASE				: List[Any]			       =    torch.randint(0							,     hf_config.vocab_size							,     size=(4, 256))
	SCREAMING_SNAKE_CASE				: Any			       =    torch.ones_like(_a)
	# mask a few tokens to make sure masking is applied appropriately :)
	SCREAMING_SNAKE_CASE				: str			       =    0

	# run forward passes
	SCREAMING_SNAKE_CASE				: str			       =    original_mlm(_a							,     _a							,     batch_first=_a							,     ignore_mask_value=0)
	SCREAMING_SNAKE_CASE				: str			       =    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
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    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")
		SCREAMING_SNAKE_CASE				: Tuple			       =    AutoTokenizer.from_pretrained(_a)
		tokenizer.save_pretrained(_a)


if __name__ == "__main__":
			a_         =      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_         =      parser.parse_args()

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


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


a_         =      logging.get_logger(__name__)

a_         =      {
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          ='mpnet'
			def __init__(     self							:	Optional[int] , a							:	Dict=3_0527 , a							:	Optional[int]=768 , a							:	List[str]=12 , a							:	List[str]=12 , a							:	str=3072 , a							:	Optional[int]="gelu" , a							:	Tuple=0.1 , a							:	Optional[int]=0.1 , a							:	Optional[Any]=512 , a							:	List[Any]=0.02 , a							:	str=1e-12 , a							:	Optional[Any]=32 , a							:	int=1 , a							:	Union[str, Any]=0 , a							:	List[str]=2 , **a							:	Optional[int] , )							->    Union[str, Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE				: Tuple			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Any			       =    hidden_size
				SCREAMING_SNAKE_CASE				: int			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Any			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: Tuple			       =    hidden_act
				SCREAMING_SNAKE_CASE				: Tuple			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: List[str]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Dict			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Any			       =    layer_norm_eps
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    relative_attention_num_buckets