
'''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 				lowerCAmelCase__     (						nn.Module  ):






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





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






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=True       ,							__SCREAMING_SNAKE_CASE=0	):


											"""simple docstring"""





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

											# get token embeddings from IDs
											lowercase_			:  Union[str, Any]									= 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:
																	lowercase_			:  List[Any]									= 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 				lowerCAmelCase__     (						nn.Module  ):






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											super().__init__()
											lowercase_			:  Dict									= MegaLM(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  int									= nn.Linear(mega_args.encoder_embed_dim       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  str									= nn.Dropout(p=0.1	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=True       ,							__SCREAMING_SNAKE_CASE=0	):


											"""simple docstring"""





											lowercase_			:  Tuple									= 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_	(       __SCREAMING_SNAKE_CASE      :				Tuple   ,   __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Tuple							):



						"""simple docstring"""





						with open(os.path.join(__SCREAMING_SNAKE_CASE   ,   '''model_args.pkl'''							)   ,   '''rb'''							) as f:
												lowercase_			:  List[Any]									= pkl.load(__SCREAMING_SNAKE_CASE							)

						# load the original encoder
						lowercase_			:  List[str]									= 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
						lowercase_			:  Optional[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=__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   ,   )

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

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

						# run forward passes
						lowercase_			:  Any									= original_mlm(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   batch_first=__SCREAMING_SNAKE_CASE   ,   ignore_mask_value=0							)
						lowercase_			:  Dict									= 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
						lowercase_			:  str									= 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'''							)
												lowercase_			:  List[Any]									= AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE							)
												tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							_lowercase				:						Any												= 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",
							)

							_lowercase				:						str												= parser.parse_args()

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





'''simple docstring'''







import math
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5   ,   int(math.sqrt(__SCREAMING_SNAKE_CASE							) + 1							)   ,   6							):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 2000000							):



						"""simple docstring"""





						return sum(num for num in range(3   ,   __SCREAMING_SNAKE_CASE   ,   2							) if is_prime(__SCREAMING_SNAKE_CASE							)							) + 2 if n > 2 else 0


if __name__ == "__main__":
							print(f"""{solution() = }""")



