
'''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  lowerCAmelCase__   (       nn.Module			):



  '''simple docstring'''






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


  def 		_lowerCamelCase	(self  : str	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : List[str]=True	, lowerCAmelCase_  : Union[str, Any]=0    )   ->       int:






        if batch_first:
              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:
              snake_case_						       =							1 - attention_mask

        # get token embeddings from IDs
        snake_case_						       =							self.embedding_layer(lowerCAmelCase_    )

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

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



class  lowerCAmelCase__   (       nn.Module			):



  '''simple docstring'''






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


  def 		_lowerCamelCase	(self  : List[str]	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Union[str, Any]=True	, lowerCAmelCase_  : List[str]=0    )   ->       Any:
        snake_case_						       =							self.mega(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )
        return self.mlm_head(self.dropout(lowerCAmelCase_    )    )

def   snake_case_       (							_a     :							Optional[Any] ,					_a     :							Any ,					_a     :							List[Any]			):


      with open(os.path.join(_a ,					"model_args.pkl"			) ,					"rb"			) as f:
            snake_case_						       =							pkl.load(_a			)

      # load the original encoder
      snake_case_						       =							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
      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=_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 ,					)

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

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

      # run forward passes
      snake_case_						       =							original_mlm(_a ,					_a ,					batch_first=_a ,					ignore_mask_value=0			)
      snake_case_						       =							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
      snake_case_						       =							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"			)
            snake_case_						       =							AutoTokenizer.from_pretrained(_a			)
            tokenizer.save_pretrained(_a			)


if __name__ == "__main__":
     A           =   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           =   parser.parse_args()

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



'''simple docstring'''





from typing import TYPE_CHECKING

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


A           =   {
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A           =   ["DetaImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A           =   [
         "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DetaForObjectDetection",
         "DetaModel",
         "DetaPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_deta import DetaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deta import (
              DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetaForObjectDetection,
              DetaModel,
              DetaPreTrainedModel,
          )

else:
     import sys

     A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

