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				,	A				,	A				,	A )	->     Dict:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    mega_args
          _SCREAMING_SNAKE_CASE             =    nn.Embedding(A				,	self.mega_args.encoder_embed_dim )
          _SCREAMING_SNAKE_CASE             =    nn.ModuleList([MegaEncoderLayer(self.mega_args ) for _ in range(A )] )
          _SCREAMING_SNAKE_CASE             =    depth
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A				,	A=True				,	A=0 )	->     Optional[int]:
  
          # Mega expects embeddings to be (time, batch, embedding size), but
          # Hugging Face returns tokens as (batch, time)
          if batch_first:
              _SCREAMING_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:
              _SCREAMING_SNAKE_CASE             =    1 - attention_mask
  
          # get token embeddings from IDs
          _SCREAMING_SNAKE_CASE             =    self.embedding_layer(A )
  
          # pass through the Mega layers
          # input is (time, batch, encoder dim) and output is the same
          for encoder in self.encoders:
              _SCREAMING_SNAKE_CASE             =    encoder(A				,	A )
  
          # return according to the shape specified
          if batch_first:
              # (T, B, H) --> (B, T, H)
              return torch.transpose(A				,	0				,	1 )
          else:
              return embeds
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A				,	A )	->     Any:
          super().__init__()
          _SCREAMING_SNAKE_CASE             =    MegaLM(A				,	A				,	A )
          _SCREAMING_SNAKE_CASE             =    nn.Linear(mega_args.encoder_embed_dim				,	A )
          _SCREAMING_SNAKE_CASE             =    nn.Dropout(p=0.1 )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A				,	A=True				,	A=0 )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    self.mega(A				,	A				,	A				,	A )
          return self.mlm_head(self.dropout(A ) )
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					List[str]					)		->Dict:
      with open(os.path.join(__lowerCamelCase      , """model_args.pkl"""					)      , """rb"""					) as f:
          _SCREAMING_SNAKE_CASE             =    pkl.load(__lowerCamelCase					)
  
      # load the original encoder
      _SCREAMING_SNAKE_CASE             =    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
      _SCREAMING_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=__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      , )
  
      _SCREAMING_SNAKE_CASE             =    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
      _SCREAMING_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
      _SCREAMING_SNAKE_CASE             =    original_mlm.mega.encoders.state_dict()
      _SCREAMING_SNAKE_CASE             =    {}
      for module_name in original_state_dict.keys():
          _SCREAMING_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:
                  _SCREAMING_SNAKE_CASE             =    module_name.replace("""move.beta"""      , """ema_gate.ema_expansion_matrix"""					)
              elif "mega_layer.beta" in module_name:
                  _SCREAMING_SNAKE_CASE             =    module_name.replace("""beta"""      , """qk_bias"""					)
              else:
                  _SCREAMING_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:
                  _SCREAMING_SNAKE_CASE             =    module_name.replace("""move.gamma"""      , """ema_gate.kernel_projection_matrix"""					)
              elif "mega_layer.gamma" in module_name:
                  _SCREAMING_SNAKE_CASE             =    module_name.replace("""gamma"""      , """qk_weight"""					)
              else:
                  _SCREAMING_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:
              _SCREAMING_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:
              _SCREAMING_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:
              _SCREAMING_SNAKE_CASE             =    module_name.replace("""move.omega"""      , """ema_gate.residual_weight"""					)
  
          if new_module_name:
              _SCREAMING_SNAKE_CASE             =    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():
          _SCREAMING_SNAKE_CASE             =    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
      _SCREAMING_SNAKE_CASE             =    torch.randint(0      , hf_config.vocab_size      , size=(4, 256)					)
      _SCREAMING_SNAKE_CASE             =    torch.ones_like(__lowerCamelCase					)
      # mask a few tokens to make sure masking is applied appropriately :)
      _SCREAMING_SNAKE_CASE             =    0
  
      # run forward passes
      _SCREAMING_SNAKE_CASE             =    original_mlm(__lowerCamelCase      , __lowerCamelCase      , batch_first=__lowerCamelCase      , ignore_mask_value=0					)
      _SCREAMING_SNAKE_CASE             =    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
      _SCREAMING_SNAKE_CASE             =    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"""					)
          _SCREAMING_SNAKE_CASE             =    AutoTokenizer.from_pretrained(__lowerCamelCase					)
          tokenizer.save_pretrained(__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        lowercase_			       =				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""",
        )
  
        lowercase_			       =				parser.parse_args()
  
        convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  from typing import List
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
      if name != "training_function" and name != "main":
          raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    [], False
      for line in lines:
          if not found_start and F'def {name}' in line:
              _SCREAMING_SNAKE_CASE             =    True
              good_lines.append(__lowerCamelCase					)
              continue
          if found_start:
              if name == "training_function" and "def main" in line:
                  return good_lines
              if name == "main" and "if __name__" in line:
                  return good_lines
              good_lines.append(__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
      return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      if secondary_filename is not None:
          with open(__lowerCamelCase      , """r"""					) as f:
              _SCREAMING_SNAKE_CASE             =    f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
      if parser_only:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
      else:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(
                  get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
  
      _SCREAMING_SNAKE_CASE             =    """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""
  
      # Specific code in our script that differs from the full version, aka what is new
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
      for i in range(len(__lowerCamelCase					) - 1					):
          if i not in passed_idxs:
              _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_feature_code.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
                  else:
                      # Skip over the `config['num_epochs'] = 2` statement
                      _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
  
      # Extract out just the new parts from the full_file_training_func
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      for i, line in enumerate(__lowerCamelCase					):
          if i not in passed_idxs:
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_full_example_parts.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
  
      # Finally, get the overall diff
      _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
      if secondary_filename is not None:
          _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
          _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]
  
      return diff_from_example
  
  
  
  