

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					:							List[Any]      ,snake_case					:							List[Any]      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]    ):
           super().__init__()
           SCREAMING_SNAKE_CASE										=mega_args
           SCREAMING_SNAKE_CASE										=nn.Embedding(snake_case      ,self.mega_args.encoder_embed_dim    )
           SCREAMING_SNAKE_CASE										=nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(snake_case    )]    )
           SCREAMING_SNAKE_CASE										=depth


      def 	_lowerCAmelCase					(				self					:							List[str]      ,snake_case					:							str      ,snake_case					:							List[Any]      ,snake_case					:							Union[str, Any]=True      ,snake_case					:							List[Any]=0    ):

           # Mega expects embeddings to be (time, batch, embedding size), but
           # Hugging Face returns tokens as (batch, time)
           if batch_first:
                SCREAMING_SNAKE_CASE										=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										=1 - attention_mask

           # get token embeddings from IDs
           SCREAMING_SNAKE_CASE										=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:
                SCREAMING_SNAKE_CASE										=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					:							Any      ,snake_case					:							List[str]      ,snake_case					:							int      ,snake_case					:							Dict    ):
           super().__init__()
           SCREAMING_SNAKE_CASE										=MegaLM(snake_case      ,snake_case      ,snake_case    )
           SCREAMING_SNAKE_CASE										=nn.Linear(mega_args.encoder_embed_dim      ,snake_case    )
           SCREAMING_SNAKE_CASE										=nn.Dropout(p=0.1    )


      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Optional[Any]      ,snake_case					:							int      ,snake_case					:							Union[str, Any]=True      ,snake_case					:							Any=0    ):
           SCREAMING_SNAKE_CASE										=self.mega(snake_case      ,snake_case      ,snake_case      ,snake_case    )
           return self.mlm_head(self.dropout(snake_case    )    )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







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

     # load the original encoder
     SCREAMING_SNAKE_CASE										=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
     SCREAMING_SNAKE_CASE										=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_,   )

     SCREAMING_SNAKE_CASE										=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
     SCREAMING_SNAKE_CASE										=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										=original_mlm.mega.encoders.state_dict()
     SCREAMING_SNAKE_CASE										={}
     for module_name in original_state_dict.keys():
          SCREAMING_SNAKE_CASE										=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										=module_name.replace('move.beta',   'ema_gate.ema_expansion_matrix'      )
               elif "mega_layer.beta" in module_name:
                    SCREAMING_SNAKE_CASE										=module_name.replace('beta',   'qk_bias'      )
               else:
                    SCREAMING_SNAKE_CASE										=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										=module_name.replace('move.gamma',   'ema_gate.kernel_projection_matrix'      )
               elif "mega_layer.gamma" in module_name:
                    SCREAMING_SNAKE_CASE										=module_name.replace('gamma',   'qk_weight'      )
               else:
                    SCREAMING_SNAKE_CASE										=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										=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										=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										=module_name.replace('move.omega',   'ema_gate.residual_weight'      )

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

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


if __name__ == "__main__":
 _lowerCamelCase								=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",
 )

 _lowerCamelCase								=parser.parse_args()

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




def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     SCREAMING_SNAKE_CASE										=1

     while power > 0:
          if power & 1:
               SCREAMING_SNAKE_CASE										=(result * base) % mod
          SCREAMING_SNAKE_CASE										=power >> 1
          SCREAMING_SNAKE_CASE										=(base * base) % mod

     return result







def       snake_case__    (       ):





     """simple docstring"""







     print(modular_exponential(3,   200,   13      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()

