


'''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       a							(						nn.Module			):


     """simple docstring"""






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






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

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

          # get token embeddings from IDs
          __UpperCAmelCase      :					Any    				=     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:
               __UpperCAmelCase      :					Any    				=     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       a							(						nn.Module			):


     """simple docstring"""






     def __init__(							self	:						str				,					snake_case	:						Tuple				,					snake_case	:						Union[str, Any]				,					snake_case	:						Dict     )		->				Optional[Any]:
          super().__init__()
          __UpperCAmelCase      :					Any    				=     MegaLM(snake_case				,					snake_case				,					snake_case     )
          __UpperCAmelCase      :					int    				=     nn.Linear(mega_args.encoder_embed_dim				,					snake_case     )
          __UpperCAmelCase      :					Tuple    				=     nn.Dropout(p=0.1     )






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




def       _a      (		_lowercase   :      Optional[Any]     ,   _lowercase   :      Any     ,   _lowercase   :      List[str]    ):





     '''simple docstring'''






     with open(os.path.join(_lowercase     ,   '''model_args.pkl'''    )     ,   '''rb'''    ) as f:
          __UpperCAmelCase      :					Any    				=     pkl.load(_lowercase    )

     # load the original encoder
     __UpperCAmelCase      :					int    				=     OriginalMegaForMaskedLM(**_lowercase    ).eval()

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

     # create a new config from the old one
     __UpperCAmelCase      :					Tuple    				=     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=_lowercase     ,   norm_affine=_lowercase     ,   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=_lowercase     ,   add_lm_hidden_dense_layer=_lowercase     ,   )

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

          if new_module_name:
               __UpperCAmelCase      :					Any    				=     new_module_name

     if len(_lowercase    ) != 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      :					List[Any]    				=     original_state_dict.pop(_lowercase    )

     # 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(_lowercase    )    )

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

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

     # run forward passes
     __UpperCAmelCase      :					Tuple    				=     original_mlm(_lowercase     ,   _lowercase     ,   batch_first=_lowercase     ,   ignore_mask_value=0    )
     __UpperCAmelCase      :					Optional[Any]    				=     hf_mlm(_lowercase     ,   _lowercase    )[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      :					Union[str, Any]    				=     torch.allclose(_lowercase     ,   _lowercase     ,   atol=1E-3    )

     if success:
          print('''Yay!'''    )
          hf_mlm.save_pretrained(_lowercase    )
     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[str]    				=     AutoTokenizer.from_pretrained(_lowercase    )
          tokenizer.save_pretrained(_lowercase    )


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     :Tuple												=  parser.parse_args()

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



'''simple docstring'''



def       _a      (		_lowercase   :      str    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Union[str, Any]    				=     5381
     for x in s:
          __UpperCAmelCase      :					Union[str, Any]    				=     ((hash_value << 5) + hash_value) + ord(_lowercase    )
     return hash_value & 0XFFFFFFFF