code: 
  
  
  
  # Copyright (c) 2021-, NVIDIA CORPORATION.  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.
  
  ####################################################################################################
  
  #
  # Note: If when running this conversion script you're getting an exception:
  #     ModuleNotFoundError: No module named 'megatron.model.enums'
  # you need to tell python where to find the clone of Megatron-LM, e.g.:
  #
  # cd /tmp
  # git clone https://github.com/NVIDIA/Megatron-LM
  # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
  #
  # if you already have it cloned elsewhere, simply adjust the path to the existing path
  #
  # If the training was done using a Megatron-LM fork, e.g.,
  # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
  # in your path, i.e., /path/to/Megatron-DeepSpeed/
  #
  
  import argparse
  import os
  import re
  import zipfile
  
  import torch
  
  from transformers import MegatronBertConfig
  def 	UpperCamelCase			(     snake_case__ :					str ,  snake_case__ :					Any ,  snake_case__ :					Optional[Any]=0			)			->  List[Any]:
     # Format the message.
     if name is None:
        UpperCamelCase					:						Optional[int]					=			None
     else:
        UpperCamelCase					:						Dict					=			'.' * max(0 ,  spaces - 2			) + '# {:' + str(50 - spaces			) + 's}'
        UpperCamelCase					:						Tuple					=			fmt.format(snake_case__			)
  
     # Print and recurse (if needed).
     if isinstance(snake_case__ ,  snake_case__			):
        if msg is not None:
           print(snake_case__			)
        for k in val.keys():
           recursive_print(snake_case__ ,  val[k] ,  spaces + 2			)
     elif isinstance(snake_case__ ,  torch.Tensor			):
        print(snake_case__ ,  ':' ,  val.size()			)
     else:
        print(snake_case__ ,  ':' ,  snake_case__			)
  def 	UpperCamelCase			(     snake_case__ :					Any ,  snake_case__ :					Optional[Any] ,  snake_case__ :					Dict ,  snake_case__ :					Optional[int] ,  snake_case__ :					int			)			->  int:
     # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
     # for compatibility with later versions of NVIDIA Megatron-LM.
     # The inverse operation is performed inside Megatron-LM to read checkpoints:
     # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
     # If param is the weight tensor of the self-attention block, the returned tensor
     # will have to be transposed one more time to be read by HuggingFace BERT.
     UpperCamelCase					:						Any					=			param.size()
     if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        UpperCamelCase					:						int					=			(num_heads, hidden_size, num_splits) + input_shape[1:]
        UpperCamelCase					:						Tuple					=			param.view(*snake_case__			)
        UpperCamelCase					:						str					=			param.transpose(0 ,  2			)
        UpperCamelCase					:						Any					=			param.transpose(1 ,  2			).contiguous()
     elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        UpperCamelCase					:						Optional[int]					=			(num_heads, num_splits, hidden_size) + input_shape[1:]
        UpperCamelCase					:						str					=			param.view(*snake_case__			)
        UpperCamelCase					:						List[Any]					=			param.transpose(0 ,  1			).contiguous()
     UpperCamelCase					:						int					=			param.view(*snake_case__			)
     return param
  def 	UpperCamelCase			(     snake_case__ :					Tuple ,  snake_case__ :					List[str] ,  snake_case__ :					str			)			->  Tuple:
     # The converted output model.
     UpperCamelCase					:						int					=			{}
  
     # old versions did not store training args
     UpperCamelCase					:						str					=			input_state_dict.get('args' ,  snake_case__			)
     if ds_args is not None:
        # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
        # from pprint import pprint
        # pprint(vars(ds_args))
  
        UpperCamelCase					:						Any					=			ds_args.tokenizer_type
        UpperCamelCase					:						Tuple					=			ds_args.padded_vocab_size
        UpperCamelCase					:						Optional[int]					=			ds_args.max_position_embeddings
        UpperCamelCase					:						Optional[Any]					=			ds_args.hidden_size
        UpperCamelCase					:						List[str]					=			ds_args.num_layers
        UpperCamelCase					:						List[Any]					=			ds_args.num_attention_heads
        UpperCamelCase					:						str					=			ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
        # pprint(config)
  
     # The number of heads.
     UpperCamelCase					:						Any					=			config.num_attention_heads
     # The hidden_size per head.
     UpperCamelCase					:						Optional[Any]					=			config.hidden_size // heads
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
        UpperCamelCase					:						Optional[Any]					=			input_state_dict['checkpoint_version']
     else:
        UpperCamelCase					:						str					=			0.0
  
     # The model.
     UpperCamelCase					:						int					=			input_state_dict['model']
     # The language model.
     UpperCamelCase					:						int					=			model['language_model']
     # The embeddings.
     UpperCamelCase					:						str					=			lm['embedding']
  
     # The word embeddings.
     UpperCamelCase					:						List[str]					=			embeddings['word_embeddings']['weight']
     # Truncate the embedding table to vocab_size rows.
     UpperCamelCase					:						Dict					=			word_embeddings[: config.vocab_size, :]
     # Store the word embeddings.
     UpperCamelCase					:						Union[str, Any]					=			word_embeddings
  
     # The position embeddings.
     UpperCamelCase					:						Dict					=			embeddings['position_embeddings']['weight']
     assert pos_embeddings.size(0			) == config.max_position_embeddings and pos_embeddings.size(1			) == config.hidden_size
     # Store the position embeddings.
     UpperCamelCase					:						str					=			pos_embeddings
  
     # The token-type embeddings.
     UpperCamelCase					:						Union[str, Any]					=			embeddings['tokentype_embeddings']['weight']
     # Store the position embeddings.
     UpperCamelCase					:						Optional[int]					=			tokentype_embeddings
  
     # The transformer.
     UpperCamelCase					:						Any					=			lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
  
     # The regex to extract layer names.
     UpperCamelCase					:						Dict					=			re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'			)
  
     # The simple map of names for "automated" rules.
     UpperCamelCase					:						List[Any]					=			{
         'attention.dense': '.attention.output.dense.',
         'self_attention.dense': '.attention.output.dense.',
         'mlp.dense_h_to_4h': '.intermediate.dense.',
         'mlp.dense_4h_to_h': '.output.dense.',
     }
  
     # Keep track of the attention/query/value tensor.
     UpperCamelCase					:						Optional[int]					=			None
  
     # Extract the layers.
     for key, val in transformer.items():
        # Match the name.
        UpperCamelCase					:						int					=			layer_re.match(snake_case__			)
  
        # Stop if that's not a layer
        if m is None:
           break
  
        # The index of the layer.
        UpperCamelCase					:						str					=			int(m.group(1			)			)
        # The name of the operation.
        UpperCamelCase					:						int					=			m.group(2			)
        # Is it a weight or a bias?
        UpperCamelCase					:						Dict					=			m.group(3			)
  
        # The name of the layer.
        UpperCamelCase					:						Any					=			F"""bert.encoder.layer.{layer_idx}"""
  
        # For layernorm(s), simply store the layer norm.
        if op_name.endswith('layernorm'			):
           UpperCamelCase					:						str					=			'attention.ln' if op_name.startswith('input'			) else 'ln'
           UpperCamelCase					:						Any					=			val
  
        # Transpose the QKV matrix.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "weight":
           # Make sure the QKV pointer is nil.
           assert attention_qkv_weight is None, ""
  
           UpperCamelCase					:						Optional[int]					=			fix_query_key_value_ordering(snake_case__ ,  snake_case__ ,  3 ,  snake_case__ ,  snake_case__			)
           # Store the tensor as we need the bias as well to interleave QKV and biases.
           UpperCamelCase					:						Any					=			out_val
  
        # Transpose the bias.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "bias":
           # Make sure we read the weight tensor.
           assert attention_qkv_weight is not None, ""
  
           # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
           UpperCamelCase					:						Tuple					=			attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
           UpperCamelCase					:						List[str]					=			attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
           UpperCamelCase					:						Optional[int]					=			attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
  
           UpperCamelCase					:						Any					=			fix_query_key_value_ordering(snake_case__ ,  snake_case__ ,  3 ,  snake_case__ ,  snake_case__			)
           # Split the bias.
           UpperCamelCase					:						int					=			out_val[0 * config.hidden_size : 1 * config.hidden_size]
           UpperCamelCase					:						Any					=			out_val[1 * config.hidden_size : 2 * config.hidden_size]
           UpperCamelCase					:						List[str]					=			out_val[2 * config.hidden_size : 3 * config.hidden_size]
  
           # Store.
           UpperCamelCase					:						Optional[Any]					=			q
           UpperCamelCase					:						Tuple					=			q_bias
           UpperCamelCase					:						Tuple					=			k
           UpperCamelCase					:						Union[str, Any]					=			k_bias
           UpperCamelCase					:						Any					=			v
           UpperCamelCase					:						Dict					=			v_bias
  
           # Clear the stored tensor.
           UpperCamelCase					:						List[Any]					=			None
  
        # Copy weights and biases as is.
        elif weight_or_bias in ["weight", "bias"]:
           UpperCamelCase					:						Optional[Any]					=			megatron_to_transformers[op_name]
           UpperCamelCase					:						int					=			val
  
      # The final layernorm.
     UpperCamelCase					:						Union[str, Any]					=			transformer['final_layernorm.weight']
     UpperCamelCase					:						Any					=			transformer['final_layernorm.bias']
  
     # The pooler.
     UpperCamelCase					:						int					=			lm['pooler']
  
     # Store the matrix and the bias.
     UpperCamelCase					:						Tuple					=			pooler['dense.weight']
     UpperCamelCase					:						Union[str, Any]					=			pooler['dense.bias']
  
     # The LM head from Megatron (for RACE).
     UpperCamelCase					:						Optional[int]					=			model['lm_head']
  
     # The transform matrix.
     UpperCamelCase					:						str					=			lm_head['dense.weight']
     UpperCamelCase					:						Optional[Any]					=			lm_head['dense.bias']
  
     # The transform LN.
     UpperCamelCase					:						Optional[Any]					=			lm_head['layernorm.weight']
     UpperCamelCase					:						int					=			lm_head['layernorm.bias']
  
     # For the decoder, we replicate the weights.
     UpperCamelCase					:						str					=			word_embeddings
     UpperCamelCase					:						int					=			lm_head['bias']
  
     # The classifier from Megatron (for MLNI).
     UpperCamelCase					:						int					=			model['binary_head']
  
     # Store the classifier.
     UpperCamelCase					:						Union[str, Any]					=			binary_head['weight']
     UpperCamelCase					:						Optional[Any]					=			binary_head['bias']
  
     # It should be done!
     return output_state_dict
  def 	UpperCamelCase			(     )			->  Any:
     # Create the argument parser.
     UpperCamelCase					:						int					=			argparse.ArgumentParser()
     parser.add_argument('--print-checkpoint-structure' ,  action='store_true'			)
     parser.add_argument('path_to_checkpoint' ,  type=snake_case__ ,  help='Path to the ZIP file containing the checkpoint'			)
     parser.add_argument(
         '--config_file' ,  default='' ,  type=snake_case__ ,  help='An optional config json file describing the pre-trained model.' ,  )
     UpperCamelCase					:						int					=			parser.parse_args()
  
     # Extract the basename.
     UpperCamelCase					:						Any					=			os.path.dirname(args.path_to_checkpoint			)
  
     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(F"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""""			)
     if args.path_to_checkpoint.endswith('.zip'			):
        with zipfile.ZipFile(args.path_to_checkpoint ,  'r'			) as checkpoint:
           with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'			) as pytorch_dict:
              UpperCamelCase					:						Tuple					=			torch.load(snake_case__ ,  map_location='cpu'			)
     else:
        UpperCamelCase					:						Union[str, Any]					=			torch.load(args.path_to_checkpoint ,  map_location='cpu'			)
  
     if args.config_file == "":
        # Default config of megatron-bert 345m
        UpperCamelCase					:						int					=			MegatronBertConfig()
  
        # different megatron-bert-*-345m models have different vocab sizes, so override the default
        # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
        UpperCamelCase					:						Union[str, Any]					=			input_state_dict['model']['lm_head']['bias'].numel()
     else:
        UpperCamelCase					:						Dict					=			MegatronBertConfig.from_json_file(args.config_file			)
  
     # Convert.
     print('Converting'			)
     UpperCamelCase					:						List[Any]					=			convert_megatron_checkpoint(snake_case__ ,  snake_case__ ,  snake_case__			)
  
     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
        recursive_print(snake_case__ ,  snake_case__			)
  
     # Store the config to file.
     print('Saving config'			)
     config.save_pretrained(snake_case__			)
  
     # Store the state_dict to file.
     UpperCamelCase					:						int					=			os.path.join(snake_case__ ,  'pytorch_model.bin'			)
     print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""			)
     torch.save(snake_case__ ,  snake_case__			)
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
      main()
  
  ####################################################################################################
  
  
  
  
style_context: 
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  __UpperCAmelCase      = {
      '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
  }
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      Union[str, Any]       =						"mpnet"
         def __init__(    self,    SCREAMING_SNAKE_CASE_=3_0527,    SCREAMING_SNAKE_CASE_=768,    SCREAMING_SNAKE_CASE_=12,    SCREAMING_SNAKE_CASE_=12,    SCREAMING_SNAKE_CASE_=3072,    SCREAMING_SNAKE_CASE_="gelu",    SCREAMING_SNAKE_CASE_=0.1,    SCREAMING_SNAKE_CASE_=0.1,    SCREAMING_SNAKE_CASE_=512,    SCREAMING_SNAKE_CASE_=0.02,    SCREAMING_SNAKE_CASE_=1e-12,    SCREAMING_SNAKE_CASE_=32,    SCREAMING_SNAKE_CASE_=1,    SCREAMING_SNAKE_CASE_=0,    SCREAMING_SNAKE_CASE_=2,    **SCREAMING_SNAKE_CASE_,    )  ->	Optional[int]:
            super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,    bos_token_id=SCREAMING_SNAKE_CASE_,    eos_token_id=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						Any					=			vocab_size
            UpperCamelCase					:						Optional[Any]					=			hidden_size
            UpperCamelCase					:						Union[str, Any]					=			num_hidden_layers
            UpperCamelCase					:						Optional[int]					=			num_attention_heads
            UpperCamelCase					:						Optional[Any]					=			hidden_act
            UpperCamelCase					:						str					=			intermediate_size
            UpperCamelCase					:						Optional[Any]					=			hidden_dropout_prob
            UpperCamelCase					:						Union[str, Any]					=			attention_probs_dropout_prob
            UpperCamelCase					:						List[Any]					=			max_position_embeddings
            UpperCamelCase					:						Union[str, Any]					=			initializer_range
            UpperCamelCase					:						List[str]					=			layer_norm_eps
            UpperCamelCase					:						str					=			relative_attention_num_buckets
  
  
  
  