





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     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       super().__init__()
       lowerCAmelCase_       :							Optional[int]			 =		mega_args
       lowerCAmelCase_       :							Any			 =		nn.Embedding(_lowercase     ,    self.mega_args.encoder_embed_dim    )
       lowerCAmelCase_       :							Dict			 =		nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(_lowercase    )]    )
       lowerCAmelCase_       :							Optional[int]			 =		depth







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase=True     ,    _lowercase=0    ):

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

       # get token embeddings from IDs
       lowerCAmelCase_       :							str			 =		self.embedding_layer(_lowercase    )

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

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





class   lowercase__     (				nn.Module	):







  def __init__(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       super().__init__()
       lowerCAmelCase_       :							List[str]			 =		MegaLM(_lowercase     ,    _lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							int			 =		nn.Linear(mega_args.encoder_embed_dim     ,    _lowercase    )
       lowerCAmelCase_       :							Tuple			 =		nn.Dropout(p=0.1    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase=True     ,    _lowercase=0    ):
       lowerCAmelCase_       :							List[Any]			 =		self.mega(_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       return self.mlm_head(self.dropout(_lowercase    )    )






def 				_lowerCAmelCase			(	_a :       Dict       , _a :       Any       , _a :       List[Any]      )						->      List[Any]:
     with open(os.path.join(_a       , """model_args.pkl"""      )       , """rb"""      ) as f:
          lowerCAmelCase_       :							Optional[Any]			 =		pkl.load(_a      )

     # load the original encoder
     lowerCAmelCase_       :							int			 =		OriginalMegaForMaskedLM(**_a      ).eval()

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

     # create a new config from the old one
     lowerCAmelCase_       :							Dict			 =		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=_a       , norm_affine=_a       , 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=_a       , add_lm_hidden_dense_layer=_a       , )

     lowerCAmelCase_       :							List[Any]			 =		MegaForMaskedLM(_a      ).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
     lowerCAmelCase_       :							str			 =		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
     lowerCAmelCase_       :							Dict			 =		original_mlm.mega.encoders.state_dict()
     lowerCAmelCase_       :							Union[str, Any]			 =		{}
     for module_name in original_state_dict.keys():
          lowerCAmelCase_       :							Union[str, 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:
                    lowerCAmelCase_       :							int			 =		module_name.replace("""move.beta"""       , """ema_gate.ema_expansion_matrix"""      )
               elif "mega_layer.beta" in module_name:
                    lowerCAmelCase_       :							Dict			 =		module_name.replace("""beta"""       , """qk_bias"""      )
               else:
                    lowerCAmelCase_       :							List[str]			 =		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:
                    lowerCAmelCase_       :							List[Any]			 =		module_name.replace("""move.gamma"""       , """ema_gate.kernel_projection_matrix"""      )
               elif "mega_layer.gamma" in module_name:
                    lowerCAmelCase_       :							Union[str, Any]			 =		module_name.replace("""gamma"""       , """qk_weight"""      )
               else:
                    lowerCAmelCase_       :							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:
               lowerCAmelCase_       :							str			 =		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:
               lowerCAmelCase_       :							int			 =		module_name.replace("""move.delta"""       , """ema_gate.damping_factor"""      )
          # omega is only used in EMA; renaming to improve readability
          elif "omega" in module_name:
               lowerCAmelCase_       :							int			 =		module_name.replace("""move.omega"""       , """ema_gate.residual_weight"""      )

          if new_module_name:
               lowerCAmelCase_       :							str			 =		new_module_name

     if len(_a      ) != 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():
          lowerCAmelCase_       :							Dict			 =		original_state_dict.pop(_a      )

     # 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(_a      )      )

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

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

     # run forward passes
     lowerCAmelCase_       :							Any			 =		original_mlm(_a       , _a       , batch_first=_a       , ignore_mask_value=0      )
     lowerCAmelCase_       :							Union[str, Any]			 =		hf_mlm(_a       , _a      )[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
     lowerCAmelCase_       :							List[Any]			 =		torch.allclose(_a       , _a       , atol=1E-3      )

     if success:
          print("""Yay!"""      )
          hf_mlm.save_pretrained(_a      )
     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"""      )
          lowerCAmelCase_       :							List[Any]			 =		AutoTokenizer.from_pretrained(_a      )
          tokenizer.save_pretrained(_a      )


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

      UpperCAmelCase_       : Any							=    parser.parse_args()

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







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_       : List[Any]							=    {
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    ["""RetriBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    [
          """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RetriBertModel""",
          """RetriBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
      from .tokenization_retribert import RetriBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_retribert_fast import RetriBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_retribert import (
                RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RetriBertModel,
                RetriBertPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_       : Tuple							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
