




import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


UpperCamelCase__              =						logging.get_logger(__name__)


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def __init__(				self ,       *__lowercase ,       **__lowercase					):







						'''simple docstring'''




						warnings.warn(
						    'The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
						    ' Please use ConvNextImageProcessor instead.' ,       __lowercase ,       )
						super().__init__(*__lowercase ,       **__lowercase					)





'''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 __snake_case(      nn.Module					):


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_	,       A_      )	->	Dict:
							super().__init__()
							lowerCAmelCase    =	mega_args
							lowerCAmelCase    =	nn.Embedding(A_	,       self.mega_args.encoder_embed_dim      )
							lowerCAmelCase    =	nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(A_      )]      )
							lowerCAmelCase    =	depth




						def 				__snake_case    (					self	,       A_	,       A_	,       A_=True	,       A_=0      )	->	List[Any]:

							# 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(A_      )

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


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_	,       A_      )	->	Optional[Any]:
							super().__init__()
							lowerCAmelCase    =	MegaLM(A_	,       A_	,       A_      )
							lowerCAmelCase    =	nn.Linear(mega_args.encoder_embed_dim	,       A_      )
							lowerCAmelCase    =	nn.Dropout(p=0.1      )




						def 				__snake_case    (					self	,       A_	,       A_	,       A_=True	,       A_=0      )	->	List[Any]:
							lowerCAmelCase    =	self.mega(A_	,       A_	,       A_	,       A_      )
							return self.mlm_head(self.dropout(A_      )      )







def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						List[str]							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							)			->						List[Any]:







	"""simple docstring"""
	with open(os.path.join(_SCREAMING_SNAKE_CASE							,		"""model_args.pkl"""							)							,		"""rb"""							) as f:
		lowerCAmelCase    =	pkl.load(_SCREAMING_SNAKE_CASE							)

	# load the original encoder
	lowerCAmelCase    =	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
	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=_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							,		)

	lowerCAmelCase    =	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
	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(_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():
		lowerCAmelCase    =	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
	lowerCAmelCase    =	torch.randint(0							,		hf_config.vocab_size							,		size=(4, 256)							)
	lowerCAmelCase    =	torch.ones_like(_SCREAMING_SNAKE_CASE							)
	# mask a few tokens to make sure masking is applied appropriately :)
	lowerCAmelCase    =	0

	# run forward passes
	lowerCAmelCase    =	original_mlm(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		batch_first=_SCREAMING_SNAKE_CASE							,		ignore_mask_value=0							)
	lowerCAmelCase    =	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
	lowerCAmelCase    =	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"""							)
		lowerCAmelCase    =	AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE							)
		tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE							)


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

				UpperCAmelCase			=    parser.parse_args()

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