


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        __lowerCamelCase      (				nn.Module ):


				"""simple docstring"""







				def __init__(				self     :    List[Any]	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Optional[int]					):
								super().__init__()
								_lowerCAmelCase  						=mega_args
								_lowerCAmelCase  						=nn.Embedding(lowerCamelCase_	,							self.mega_args.encoder_embed_dim					)
								_lowerCAmelCase  						=nn.ModuleList([MegaEncoderLayer(self.mega_args					) for _ in range(lowerCamelCase_					)]					)
								_lowerCAmelCase  						=depth






				def        lowerCAmelCase__			(				self     :    Optional[Any]	,							lowerCamelCase_     :    int	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    List[str]=True	,							lowerCamelCase_     :    Union[str, Any]=0					):

								# Mega expects embeddings to be (time, batch, embedding size), but
								# Hugging Face returns tokens as (batch, time)
								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(lowerCamelCase_					)

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

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


class        __lowerCamelCase      (				nn.Module ):


				"""simple docstring"""







				def __init__(				self     :    List[str]	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Union[str, Any]	,							lowerCamelCase_     :    str					):
								super().__init__()
								_lowerCAmelCase  						=MegaLM(lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_					)
								_lowerCAmelCase  						=nn.Linear(mega_args.encoder_embed_dim	,							lowerCamelCase_					)
								_lowerCAmelCase  						=nn.Dropout(p=0.1					)






				def        lowerCAmelCase__			(				self     :    List[str]	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    Tuple=True	,							lowerCamelCase_     :    Tuple=0					):
								_lowerCAmelCase  						=self.mega(lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_					)
								return self.mlm_head(self.dropout(lowerCamelCase_					)					)

def 		snake_case_   (				lowercase__     :    str       ,     lowercase__     :    int       ,     lowercase__     :    List[Any]     ):






				'''simple docstring'''






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

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

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

				_lowerCAmelCase  						=MegaForMaskedLM(lowercase__     ).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(lowercase__     ) != 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(lowercase__     )

				# 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(lowercase__     )     )

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

				# run forward passes
				_lowerCAmelCase  						=original_mlm(lowercase__       ,     lowercase__       ,     batch_first=lowercase__       ,     ignore_mask_value=0     )
				_lowerCAmelCase  						=hf_mlm(lowercase__       ,     lowercase__     )[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(lowercase__       ,     lowercase__       ,     atol=1e-3     )

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


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE	:							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''',
		)

		__SCREAMING_SNAKE_CASE	:							str           =  parser.parse_args()

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PegasusForCausalLM''',
		    '''PegasusForConditionalGeneration''',
		    '''PegasusModel''',
		    '''PegasusPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''FlaxPegasusForConditionalGeneration''',
		    '''FlaxPegasusModel''',
		    '''FlaxPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

