




'''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     lowercase  ( nn.Module):






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






      def  A__(self			,	a__			,	a__			,	a__=True			,	a__=0       )							-> Optional[Any]:

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

         # get token embeddings from IDs
         SCREAMING_SNAKE_CASE__      :					str							    =			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:
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			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     lowercase  ( nn.Module):






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






      def  A__(self			,	a__			,	a__			,	a__=True			,	a__=0       )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			self.mega(a__			,	a__			,	a__			,	a__       )
         return self.mlm_head(self.dropout(a__       )       )


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case ):




   '''simple docstring'''

   with open(os.path.join(snake_case						,  '''model_args.pkl''' )						,  '''rb''' ) as f:
      SCREAMING_SNAKE_CASE__      :					Dict							    =			pkl.load(snake_case )

   # load the original encoder
   SCREAMING_SNAKE_CASE__      :					int							    =			OriginalMegaForMaskedLM(**snake_case ).eval()

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

   # create a new config from the old one
   SCREAMING_SNAKE_CASE__      :					str							    =			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=snake_case						,  norm_affine=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=snake_case						,  add_lm_hidden_dense_layer=snake_case						,  )

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

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

   if len(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():
      SCREAMING_SNAKE_CASE__      :					Any							    =			original_state_dict.pop(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(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(snake_case						,  '''mlm_head_weights.pt''' )						,  map_location='''cpu''' ) )						,  )

   # test on a randomly generated input sequence
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			torch.randint(0						,  hf_config.vocab_size						,  size=(4, 2_56) )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.ones_like(snake_case )
   # mask a few tokens to make sure masking is applied appropriately :)
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			0

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

   if success:
      print('''Yay!''' )
      hf_mlm.save_pretrained(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''' )
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			AutoTokenizer.from_pretrained(snake_case )
      tokenizer.save_pretrained(snake_case )


if __name__ == "__main__":
       lowercase__								=						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__								=						parser.parse_args()

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







'''simple docstring'''





import math
from decimal import Decimal, getcontext


def 					UpperCamelCase						(	snake_case = 4_00_00_00 ):




   '''simple docstring'''

   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			int(snake_case )
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.''' )
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			1_00
   SCREAMING_SNAKE_CASE__      :					int							    =			(Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )

   SCREAMING_SNAKE_CASE__      :					int							    =			(math.floor(math.log(n * (phi + 2)						,  snake_case ) - 1 ) // 3) * 3 + 2
   SCREAMING_SNAKE_CASE__      :					int							    =			Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
   SCREAMING_SNAKE_CASE__      :					Any							    =			num // 2
   return int(snake_case )


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

