code: 
  """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)
  
  
  
style_context: 
  """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() = }""")
  
  
  