



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     ):







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


						'''simple docstring'''







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







		def 					a_  (    self  :   Any       ,		UpperCAmelCase_  :   Tuple       ,		UpperCAmelCase_  :   Optional[Any]       ,		UpperCAmelCase_  :   Optional[Any]=True       ,		UpperCAmelCase_  :   int=0					)       ->		Dict:


						'''simple docstring'''







						if batch_first:
										_UpperCAmelCase						:  Any     					=	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						:  int     					=	1 - attention_mask

						# get token embeddings from IDs
						_UpperCAmelCase						:  str     					=	self.embedding_layer(UpperCAmelCase_					)

						# pass through the Mega layers
						# input is (time, batch, encoder dim) and output is the same
						for encoder in self.encoders:
										_UpperCAmelCase						:  List[Any]     					=	encoder(UpperCAmelCase_       ,		UpperCAmelCase_					)

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





class 							lowerCAmelCase_		(		nn.Module     ):







		def __init__(    self  :   Optional[Any]       ,		UpperCAmelCase_  :   List[str]       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   Dict					)       ->		List[Any]:


						'''simple docstring'''







						super().__init__()
						_UpperCAmelCase						:  int     					=	MegaLM(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  Dict     					=	nn.Linear(mega_args.encoder_embed_dim       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  List[str]     					=	nn.Dropout(p=0.1					)







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   List[Any]       ,		UpperCAmelCase_  :   Union[str, Any]       ,		UpperCAmelCase_  :   List[Any]=True       ,		UpperCAmelCase_  :   str=0					)       ->		Optional[int]:


						'''simple docstring'''







						_UpperCAmelCase						:  Optional[Any]     					=	self.mega(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					)
						return self.mlm_head(self.dropout(UpperCAmelCase_					)					)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				with open(os.path.join(_UpperCamelCase      ,					'''model_args.pkl'''			)      ,					'''rb'''			) as f:
								_UpperCAmelCase						:  List[Any]     					=	pkl.load(_UpperCamelCase			)

				# load the original encoder
				_UpperCAmelCase						:  List[str]     					=	OriginalMegaForMaskedLM(**_UpperCamelCase			).eval()

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

				# create a new config from the old one
				_UpperCAmelCase						:  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=_UpperCamelCase      ,					norm_affine=_UpperCamelCase      ,					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=_UpperCamelCase      ,					add_lm_hidden_dense_layer=_UpperCamelCase      ,					)

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

								if new_module_name:
												_UpperCAmelCase						:  str     					=	new_module_name

				if len(_UpperCamelCase			) != 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						:  int     					=	original_state_dict.pop(_UpperCamelCase			)

				# 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(_UpperCamelCase			)			)

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

				# test on a randomly generated input sequence
				_UpperCAmelCase						:  List[Any]     					=	torch.randint(0      ,					hf_config.vocab_size      ,					size=(4, 256)			)
				_UpperCAmelCase						:  List[Any]     					=	torch.ones_like(_UpperCamelCase			)
				# mask a few tokens to make sure masking is applied appropriately :)
				_UpperCAmelCase						:  Optional[int]     					=	0

				# run forward passes
				_UpperCAmelCase						:  Any     					=	original_mlm(_UpperCamelCase      ,					_UpperCamelCase      ,					batch_first=_UpperCamelCase      ,					ignore_mask_value=0			)
				_UpperCAmelCase						:  int     					=	hf_mlm(_UpperCamelCase      ,					_UpperCamelCase			)[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						:  Optional[Any]     					=	torch.allclose(_UpperCamelCase      ,					_UpperCamelCase      ,					atol=1e-3			)

				if success:
								print('''Yay!'''			)
								hf_mlm.save_pretrained(_UpperCamelCase			)
				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						:  List[Any]     					=	AutoTokenizer.from_pretrained(_UpperCamelCase			)
								tokenizer.save_pretrained(_UpperCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase__			:   Optional[int]      			=    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__			:   Dict      			=    parser.parse_args()

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





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


UpperCAmelCase__			:   str      			=    importlib.metadata.version('numpy')
UpperCAmelCase__			:   Optional[int]      			=    '.'.join([str(x) for x in sys.version_info[:3]])





class 							lowerCAmelCase_		(		lowercase_     ):







		def 					a_  (    self  :   Tuple					)       ->		Tuple:


						'''simple docstring'''







						require_version_core('''numpy<1000.4.5'''					)
						require_version_core('''numpy<1000.4'''					)
						require_version_core('''numpy<1000'''					)

						# le
						require_version_core('''numpy<=1000.4.5'''					)
						require_version_core(F'''numpy<={numpy_ver}'''					)

						# eq
						require_version_core(F'''numpy=={numpy_ver}'''					)

						# ne
						require_version_core('''numpy!=1000.4.5'''					)

						# ge
						require_version_core('''numpy>=1.0'''					)
						require_version_core('''numpy>=1.0.0'''					)
						require_version_core(F'''numpy>={numpy_ver}'''					)

						# gt
						require_version_core('''numpy>1.0.0'''					)

						# mix
						require_version_core('''numpy>1.0.0,<1000'''					)

						# requirement w/o version
						require_version_core('''numpy'''					)

						# unmet requirements due to version conflict
						for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''but found'''       ,		str(UpperCAmelCase_					)					)

        # unmet requirements due to missing module
						for req in ["numpipypie>1", "numpipypie2"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except importlib.metadata.PackageNotFoundError as e:
														self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn('''Try: pip install transformers -U'''       ,		str(UpperCAmelCase_					)					)

        # bogus requirements formats:
        # 1. whole thing
						for req in ["numpy??1.0.0", "numpy1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''requirement needs to be in the pip package format'''       ,		str(UpperCAmelCase_					)					)
        # 2. only operators
						for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ValueError as e:
														self.assertIn('''need one of '''       ,		str(UpperCAmelCase_					)					)







		def 					a_  (    self  :   Tuple					)       ->		List[Any]:


						'''simple docstring'''







						require_version('''python>=3.6.0'''					)

						# not matching requirements
						for req in ["python>9.9.9", "python<3.0.0"]:
										try:
														require_version_core(UpperCAmelCase_					)
										except ImportError as e:
														self.assertIn(F'''{req} is required'''       ,		str(UpperCAmelCase_					)					)
														self.assertIn(F'''but found python=={python_ver}'''       ,		str(UpperCAmelCase_					)					)
