




'''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   ):





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





 def     snake_case__						(      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:
         snake_case_	:      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:
         snake_case_	:      List[Any]            =  1 - attention_mask

     # get token embeddings from IDs
     snake_case_	:      Optional[Any]            =  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:
         snake_case_	:      int            =  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 				UpperCAmelCase_			(		nn.Module   ):





 def __init__(      self	,    lowercase_	,    lowercase_	,    lowercase_):
     super().__init__()
     snake_case_	:      Any            =  MegaLM(lowercase_	,    lowercase_	,    lowercase_)
     snake_case_	:      Dict            =  nn.Linear(mega_args.encoder_embed_dim	,    lowercase_)
     snake_case_	:      int            =  nn.Dropout(p=0.1)





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_=True	,    lowercase_=0):
     snake_case_	:      Optional[int]            =  self.mega(lowercase_	,    lowercase_	,    lowercase_	,    lowercase_)
     return self.mlm_head(self.dropout(lowercase_))


def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

    # load the original encoder
    snake_case_	:      Any            =  OriginalMegaForMaskedLM(**__SCREAMING_SNAKE_CASE    ).eval()

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

    # create a new config from the old one
    snake_case_	:      Any            =  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=__SCREAMING_SNAKE_CASE,						norm_affine=__SCREAMING_SNAKE_CASE,						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=__SCREAMING_SNAKE_CASE,						add_lm_hidden_dense_layer=__SCREAMING_SNAKE_CASE,						)

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

        if new_module_name:
            snake_case_	:      List[Any]            =  new_module_name

    if len(__SCREAMING_SNAKE_CASE    ) != 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_	:      Optional[Any]            =  original_state_dict.pop(__SCREAMING_SNAKE_CASE    )

    # 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(__SCREAMING_SNAKE_CASE    )    )

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

    # test on a randomly generated input sequence
    snake_case_	:      Dict            =  torch.randint(0,						hf_config.vocab_size,						size=(4, 2_5_6)    )
    snake_case_	:      Optional[Any]            =  torch.ones_like(__SCREAMING_SNAKE_CASE    )
    # mask a few tokens to make sure masking is applied appropriately :)
    snake_case_	:      Dict            =  0

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

    if success:
        print("Yay!"    )
        hf_mlm.save_pretrained(__SCREAMING_SNAKE_CASE    )
    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_	:      Any            =  AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE    )
        tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE    )


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'''
def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Optional[int]            =  len(__SCREAMING_SNAKE_CASE    )

    # Set the gap value to be decreased by a factor of 1.3
    # after each iteration
    snake_case_	:      Optional[int]            =  1.3

    # Continue sorting until the gap is 1
    while gap > 1:
        # Decrease the gap value
        snake_case_	:      Optional[Any]            =  int(gap / shrink    )

        # Sort the elements using insertion sort
        for i in range(__SCREAMING_SNAKE_CASE,						len(__SCREAMING_SNAKE_CASE    )    ):
            snake_case_	:      Any            =  collection[i]
            snake_case_	:      int            =  i
            while j >= gap and collection[j - gap] > temp:
                snake_case_	:      Dict            =  collection[j - gap]
                j -= gap
            snake_case_	:      Optional[int]            =  temp

    return collection


if __name__ == "__main__":
  import doctest

  doctest.testmod()




