
"""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 A_			(nn.Module   ):




  '''simple docstring'''







  def __init__(	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     super().__init__()
     UpperCAmelCase_      :      Dict      =					mega_args
     UpperCAmelCase_      :      Union[str, Any]      =					nn.Embedding(lowercase_       ,     self.mega_args.encoder_embed_dim						)
     UpperCAmelCase_      :      Union[str, Any]      =					nn.ModuleList([MegaEncoderLayer(self.mega_args						) for _ in range(lowercase_						)]						)
     UpperCAmelCase_      :      Union[str, Any]      =					depth







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_=True       ,     lowercase_=0						):

     """simple docstring"""

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

     # get token embeddings from IDs
     UpperCAmelCase_      :      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:
        UpperCAmelCase_      :      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 A_			(nn.Module   ):




  '''simple docstring'''







  def __init__(	self       ,     lowercase_       ,     lowercase_       ,     lowercase_						):

     """simple docstring"""
     super().__init__()
     UpperCAmelCase_      :      List[str]      =					MegaLM(lowercase_       ,     lowercase_       ,     lowercase_						)
     UpperCAmelCase_      :      str      =					nn.Linear(mega_args.encoder_embed_dim       ,     lowercase_						)
     UpperCAmelCase_      :      List[str]      =					nn.Dropout(p=0.1						)







  def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_=True       ,     lowercase_=0						):

     """simple docstring"""
     UpperCAmelCase_      :      Any      =					self.mega(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
     return self.mlm_head(self.dropout(lowercase_						)						)







def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   with open(os.path.join(__lowerCamelCase,	"model_args.pkl"						),	"rb"						) as f:
      UpperCAmelCase_      :      Tuple      =					pkl.load(__lowerCamelCase						)

   # load the original encoder
   UpperCAmelCase_      :      Dict      =					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
   UpperCAmelCase_      :      Optional[int]      =					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,	)

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

      if new_module_name:
         UpperCAmelCase_      :      Any      =					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():
      UpperCAmelCase_      :      Tuple      =					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
   UpperCAmelCase_      :      Dict      =					torch.randint(0,	hf_config.vocab_size,	size=(4, 256)						)
   UpperCAmelCase_      :      Any      =					torch.ones_like(__lowerCamelCase						)
   # mask a few tokens to make sure masking is applied appropriately :)
   UpperCAmelCase_      :      List[str]      =					0

   # run forward passes
   UpperCAmelCase_      :      str      =					original_mlm(__lowerCamelCase,	__lowerCamelCase,	batch_first=__lowerCamelCase,	ignore_mask_value=0						)
   UpperCAmelCase_      :      List[str]      =					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
   UpperCAmelCase_      :      Tuple      =					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"						)
      UpperCAmelCase_      :      int      =					AutoTokenizer.from_pretrained(__lowerCamelCase						)
      tokenizer.save_pretrained(__lowerCamelCase						)


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 			__a  (	__lowerCamelCase = 1000						):
   UpperCAmelCase_      :      List[Any]      =					0
   UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
   total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
   UpperCAmelCase_      :      Any      =					(n - 1) // 5
   total += ((terms) * (10 + (terms - 1) * 5)) // 2
   UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
   total -= ((terms) * (30 + (terms - 1) * 15)) // 2
   return total


if __name__ == "__main__":
    print(f"""{solution() = }""")


