





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 UpperCAmelCase_							(   nn.Module   ):


  def __init__(       self   ,__snake_case   ,__snake_case   ,__snake_case   ):



       """simple docstring"""





       super().__init__()
       A_					      =					mega_args
       A_					      =					nn.Embedding(__snake_case   ,self.mega_args.encoder_embed_dim   )
       A_					      =					nn.ModuleList([MegaEncoderLayer(self.mega_args   ) for _ in range(__snake_case   )]   )
       A_					      =					depth




  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,__snake_case=True   ,__snake_case=0   ):



       """simple docstring"""





       if batch_first:
            A_					      =					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:
            A_					      =					1 - attention_mask

       # get token embeddings from IDs
       A_					      =					self.embedding_layer(__snake_case   )

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

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





class UpperCAmelCase_							(   nn.Module   ):


  def __init__(       self   ,__snake_case   ,__snake_case   ,__snake_case   ):



       """simple docstring"""





       super().__init__()
       A_					      =					MegaLM(__snake_case   ,__snake_case   ,__snake_case   )
       A_					      =					nn.Linear(mega_args.encoder_embed_dim   ,__snake_case   )
       A_					      =					nn.Dropout(p=0.1   )




  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,__snake_case=True   ,__snake_case=0   ):



       """simple docstring"""





       A_					      =					self.mega(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   )
       return self.mlm_head(self.dropout(__snake_case   )   )




def     UpperCAmelCase_						(						_UpperCAmelCase       :Any     ,	_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :Any							)  ->							str:
     '''simple docstring'''


     with open(os.path.join(_UpperCAmelCase     ,	'''model_args.pkl'''							)     ,	'''rb'''							) as f:
          A_					      =					pkl.load(_UpperCAmelCase							)

     # load the original encoder
     A_					      =					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
     A_					      =					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     ,	)

     A_					      =					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
     A_					      =					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
     A_					      =					original_mlm.mega.encoders.state_dict()
     A_					      =					{}
     for module_name in original_state_dict.keys():
          A_					      =					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:
                    A_					      =					module_name.replace('''move.beta'''     ,	'''ema_gate.ema_expansion_matrix'''							)
               elif "mega_layer.beta" in module_name:
                    A_					      =					module_name.replace('''beta'''     ,	'''qk_bias'''							)
               else:
                    A_					      =					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:
                    A_					      =					module_name.replace('''move.gamma'''     ,	'''ema_gate.kernel_projection_matrix'''							)
               elif "mega_layer.gamma" in module_name:
                    A_					      =					module_name.replace('''gamma'''     ,	'''qk_weight'''							)
               else:
                    A_					      =					module_name.replace('''gamma'''     ,	'''g_param'''							)
        # alpha is used in EMA and positional bias; renaming to improve readability
          elif "move.alpha" in module_name:
               A_					      =					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:
               A_					      =					module_name.replace('''move.delta'''     ,	'''ema_gate.damping_factor'''							)
          # omega is only used in EMA; renaming to improve readability
          elif "omega" in module_name:
               A_					      =					module_name.replace('''move.omega'''     ,	'''ema_gate.residual_weight'''							)

          if new_module_name:
               A_					      =					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():
          A_					      =					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
     A_					      =					torch.randint(0     ,	hf_config.vocab_size     ,	size=(4, 256)							)
     A_					      =					torch.ones_like(_UpperCAmelCase							)
     # mask a few tokens to make sure masking is applied appropriately :)
     A_					      =					0

     # run forward passes
     A_					      =					original_mlm(_UpperCAmelCase     ,	_UpperCAmelCase     ,	batch_first=_UpperCAmelCase     ,	ignore_mask_value=0							)
     A_					      =					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
     A_					      =					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'''							)
          A_					      =					AutoTokenizer.from_pretrained(_UpperCAmelCase							)
          tokenizer.save_pretrained(_UpperCAmelCase							)


if __name__ == "__main__":
      a__			:    str			      =		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',
      )

      a__			:    List[str]			      =		parser.parse_args()

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










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]							)
               else:
                    wrong_args.append(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



