




'''simple docstring'''





import math





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      0
    if n % 2 == 0:
        count += 1
        while n % 2 == 0:
            _a				:							str							       =      int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

    _a				:							Union[str, Any]							       =      3

    while i <= int(math.sqrt(UpperCAmelCase_						)						):
        if n % i == 0:
            count += 1
            while n % i == 0:
                _a				:							Union[str, Any]							       =      int(n / i						)
        _a				:							Optional[int]							       =      i + 2

    # this condition checks the prime
    # number n is greater than 2

    if n > 2:
        count += 1
    return count


if __name__ == "__main__":
  a		:	Tuple       =      5_1_2_4_2_1_8_3
  print(F"""The number of distinct prime factors is/are {exact_prime_factor_count(n)}""")
  print(F"""The value of log(log(n)) is {math.log(math.log(n)):.4f}""")







"""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 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





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




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

								# Mega expects embeddings to be (time, batch, embedding size), but
								# Hugging Face returns tokens as (batch, time)
								if batch_first:
											lowerCamelCase__						:						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:
											lowerCamelCase__						:						Union[str, Any]						      =1 - attention_mask

								# get token embeddings from IDs
								lowerCamelCase__						:						Any						      =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__						:						Union[str, Any]						      =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 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Tuple	)->   Dict:
								super().__init__()
								lowerCamelCase__						:						Dict						      =MegaLM(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								lowerCamelCase__						:						Optional[Any]						      =nn.Linear(mega_args.encoder_embed_dim,    lowerCamelCase	)
								lowerCamelCase__						:						List[Any]						      =nn.Dropout(p=0.1	)




					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    Optional[int],    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   int:
								lowerCamelCase__						:						Optional[int]						      =self.mega(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self.mlm_head(self.dropout(lowerCamelCase	)	)






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			with open(os.path.join(__lowerCamelCase				,						'''model_args.pkl'''      )				,						'''rb'''      ) as f:
						lowerCamelCase__						:						Any						      =pkl.load(__lowerCamelCase      )

			# load the original encoder
			lowerCamelCase__						:						Tuple						      =OriginalMegaForMaskedLM(**__lowerCamelCase      ).eval()

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

			# create a new config from the old one
			lowerCamelCase__						:						Union[str, 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=__lowerCamelCase				,						norm_affine=__lowerCamelCase				,						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=__lowerCamelCase				,						add_lm_hidden_dense_layer=__lowerCamelCase				,						)

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

						if new_module_name:
									lowerCamelCase__						:						Union[str, Any]						      =new_module_name

			if len(__lowerCamelCase      ) != 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__						:						Optional[int]						      =original_state_dict.pop(__lowerCamelCase      )

			# 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(__lowerCamelCase      )      )

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

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

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

			if success:
						print('''Yay!'''      )
						hf_mlm.save_pretrained(__lowerCamelCase      )
			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__						:						Any						      =AutoTokenizer.from_pretrained(__lowerCamelCase      )
						tokenizer.save_pretrained(__lowerCamelCase      )


if __name__ == "__main__":
		_lowercase					:					Dict 		=		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					:					Dict 		=		parser.parse_args()

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




