'''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 	__UpperCAmelCase				(				nn.Module     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Tuple:
           super().__init__()
           A_     =   mega_args
           A_     =   nn.Embedding(_SCREAMING_SNAKE_CASE    ,     self.mega_args.encoder_embed_dim )
           A_     =   nn.ModuleList([MegaEncoderLayer(self.mega_args ) for _ in range(_SCREAMING_SNAKE_CASE )] )
           A_     =   depth

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=0 )		->     Any:

           # Mega expects embeddings to be (time, batch, embedding size), but
           # Hugging Face returns tokens as (batch, time)
           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(_SCREAMING_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(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

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




class 	__UpperCAmelCase				(				nn.Module     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           super().__init__()
           A_     =   MegaLM(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
           A_     =   nn.Linear(mega_args.encoder_embed_dim    ,     _SCREAMING_SNAKE_CASE )
           A_     =   nn.Dropout(p=0.1 )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=0 )		->     List[str]:
           A_     =   self.mega(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
           return self.mlm_head(self.dropout(_SCREAMING_SNAKE_CASE ) )





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[str],   _UpperCamelCase    :	Any,   _UpperCamelCase    :	str   )	->      str:
    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, 2_56)   )
    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__":
  __snake_case   :      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',
  )

  __snake_case   :      Dict						    =  parser.parse_args()

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







from dataclasses import dataclass
from typing import Callable, Optional

import datasets


@dataclass
class 						a__							(   datasets.BuilderConfig		):
			A__				:   Optional[Callable]								=			None
			A__				:   Optional[dict]								=			None
			A__				:   Optional[datasets.Features]								=			None



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Any:
								assert self.generator is not None, "generator must be specified"

								if self.gen_kwargs is None:
													__a							=   {}






class 						a__							(   datasets.GeneratorBasedBuilder		):
			A__				:   List[Any]								=			GeneratorConfig



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								return datasets.DatasetInfo(features=self.config.features    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,						gen_kwargs=self.config.gen_kwargs    )]







			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Tuple:
								for idx, ex in enumerate(self.config.generator(**UpperCAmelCase    )    ):
													yield idx, ex




