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 			__lowercase     (				a__							,     a__							,     a__=0     )       ->				List[Any]:
         # Format the message.
         if name is None:
                __SCREAMING_SNAKE_CASE								=      None
         else:
                __SCREAMING_SNAKE_CASE								=      '.' * max(0							,     spaces - 2     ) + '# {:' + str(50 - spaces     ) + 's}'
                __SCREAMING_SNAKE_CASE								=      fmt.format(a__     )
  
         # Print and recurse (if needed).
         if isinstance(a__							,     a__     ):
                if msg is not None:
                       print(a__     )
                for k in val.keys():
                       recursive_print(a__							,     val[k]							,     spaces + 2     )
         elif isinstance(a__							,     torch.Tensor     ):
                print(a__							,     ':'							,     val.size()     )
         else:
                print(a__							,     ':'							,     a__     )
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__							,     a__							,     a__     )       ->				Optional[Any]:
         # 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.
         __SCREAMING_SNAKE_CASE								=      param.size()
         if checkpoint_version == 1.0:
                # version 1.0 stores [num_heads * hidden_size * num_splits, :]
                __SCREAMING_SNAKE_CASE								=      (num_heads, hidden_size, num_splits) + input_shape[1:]
                __SCREAMING_SNAKE_CASE								=      param.view(*a__     )
                __SCREAMING_SNAKE_CASE								=      param.transpose(0							,     2     )
                __SCREAMING_SNAKE_CASE								=      param.transpose(1							,     2     ).contiguous()
         elif checkpoint_version >= 2.0:
                # other versions store [num_heads * num_splits * hidden_size, :]
                __SCREAMING_SNAKE_CASE								=      (num_heads, num_splits, hidden_size) + input_shape[1:]
                __SCREAMING_SNAKE_CASE								=      param.view(*a__     )
                __SCREAMING_SNAKE_CASE								=      param.transpose(0							,     1     ).contiguous()
         __SCREAMING_SNAKE_CASE								=      param.view(*a__     )
         return param
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__     )       ->				Optional[Any]:
         # The converted output model.
         __SCREAMING_SNAKE_CASE								=      {}
  
         # old versions did not store training args
         __SCREAMING_SNAKE_CASE								=      input_state_dict.get('args'							,     a__     )
         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))
  
                __SCREAMING_SNAKE_CASE								=      ds_args.tokenizer_type
                __SCREAMING_SNAKE_CASE								=      ds_args.padded_vocab_size
                __SCREAMING_SNAKE_CASE								=      ds_args.max_position_embeddings
                __SCREAMING_SNAKE_CASE								=      ds_args.hidden_size
                __SCREAMING_SNAKE_CASE								=      ds_args.num_layers
                __SCREAMING_SNAKE_CASE								=      ds_args.num_attention_heads
                __SCREAMING_SNAKE_CASE								=      ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
                # pprint(config)
  
         # The number of heads.
         __SCREAMING_SNAKE_CASE								=      config.num_attention_heads
         # The hidden_size per head.
         __SCREAMING_SNAKE_CASE								=      config.hidden_size // heads
         # Megatron-LM checkpoint version
         if "checkpoint_version" in input_state_dict.keys():
                __SCREAMING_SNAKE_CASE								=      input_state_dict['checkpoint_version']
         else:
                __SCREAMING_SNAKE_CASE								=      0.0
  
         # The model.
         __SCREAMING_SNAKE_CASE								=      input_state_dict['model']
         # The language model.
         __SCREAMING_SNAKE_CASE								=      model['language_model']
         # The embeddings.
         __SCREAMING_SNAKE_CASE								=      lm['embedding']
  
         # The word embeddings.
         __SCREAMING_SNAKE_CASE								=      embeddings['word_embeddings']['weight']
         # Truncate the embedding table to vocab_size rows.
         __SCREAMING_SNAKE_CASE								=      word_embeddings[: config.vocab_size, :]
         # Store the word embeddings.
         __SCREAMING_SNAKE_CASE								=      word_embeddings
  
         # The position embeddings.
         __SCREAMING_SNAKE_CASE								=      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.
         __SCREAMING_SNAKE_CASE								=      pos_embeddings
  
         # The token-type embeddings.
         __SCREAMING_SNAKE_CASE								=      embeddings['tokentype_embeddings']['weight']
         # Store the position embeddings.
         __SCREAMING_SNAKE_CASE								=      tokentype_embeddings
  
         # The transformer.
         __SCREAMING_SNAKE_CASE								=      lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
  
         # The regex to extract layer names.
         __SCREAMING_SNAKE_CASE								=      re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'     )
  
         # The simple map of names for "automated" rules.
         __SCREAMING_SNAKE_CASE								=      {
             '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.
         __SCREAMING_SNAKE_CASE								=      None
  
         # Extract the layers.
         for key, val in transformer.items():
                # Match the name.
                __SCREAMING_SNAKE_CASE								=      layer_re.match(a__     )
  
                # Stop if that's not a layer
                if m is None:
                       break
  
                # The index of the layer.
                __SCREAMING_SNAKE_CASE								=      int(m.group(1     )     )
                # The name of the operation.
                __SCREAMING_SNAKE_CASE								=      m.group(2     )
                # Is it a weight or a bias?
                __SCREAMING_SNAKE_CASE								=      m.group(3     )
  
                # The name of the layer.
                __SCREAMING_SNAKE_CASE								=      f"""bert.encoder.layer.{layer_idx}"""
  
                # For layernorm(s), simply store the layer norm.
                if op_name.endswith('layernorm'     ):
                       __SCREAMING_SNAKE_CASE								=      'attention.ln' if op_name.startswith('input'     ) else 'ln'
                       __SCREAMING_SNAKE_CASE								=      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, ""
  
                       __SCREAMING_SNAKE_CASE								=      fix_query_key_value_ordering(a__							,     a__							,     3							,     a__							,     a__     )
                       # Store the tensor as we need the bias as well to interleave QKV and biases.
                       __SCREAMING_SNAKE_CASE								=      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.
                       __SCREAMING_SNAKE_CASE								=      attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
                       __SCREAMING_SNAKE_CASE								=      attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
                       __SCREAMING_SNAKE_CASE								=      attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
  
                       __SCREAMING_SNAKE_CASE								=      fix_query_key_value_ordering(a__							,     a__							,     3							,     a__							,     a__     )
                       # Split the bias.
                       __SCREAMING_SNAKE_CASE								=      out_val[0 * config.hidden_size : 1 * config.hidden_size]
                       __SCREAMING_SNAKE_CASE								=      out_val[1 * config.hidden_size : 2 * config.hidden_size]
                       __SCREAMING_SNAKE_CASE								=      out_val[2 * config.hidden_size : 3 * config.hidden_size]
  
                       # Store.
                       __SCREAMING_SNAKE_CASE								=      q
                       __SCREAMING_SNAKE_CASE								=      q_bias
                       __SCREAMING_SNAKE_CASE								=      k
                       __SCREAMING_SNAKE_CASE								=      k_bias
                       __SCREAMING_SNAKE_CASE								=      v
                       __SCREAMING_SNAKE_CASE								=      v_bias
  
                       # Clear the stored tensor.
                       __SCREAMING_SNAKE_CASE								=      None
  
                # Copy weights and biases as is.
                elif weight_or_bias in ["weight", "bias"]:
                       __SCREAMING_SNAKE_CASE								=      megatron_to_transformers[op_name]
                       __SCREAMING_SNAKE_CASE								=      val
  
      # The final layernorm.
         __SCREAMING_SNAKE_CASE								=      transformer['final_layernorm.weight']
         __SCREAMING_SNAKE_CASE								=      transformer['final_layernorm.bias']
  
         # The pooler.
         __SCREAMING_SNAKE_CASE								=      lm['pooler']
  
         # Store the matrix and the bias.
         __SCREAMING_SNAKE_CASE								=      pooler['dense.weight']
         __SCREAMING_SNAKE_CASE								=      pooler['dense.bias']
  
         # The LM head from Megatron (for RACE).
         __SCREAMING_SNAKE_CASE								=      model['lm_head']
  
         # The transform matrix.
         __SCREAMING_SNAKE_CASE								=      lm_head['dense.weight']
         __SCREAMING_SNAKE_CASE								=      lm_head['dense.bias']
  
         # The transform LN.
         __SCREAMING_SNAKE_CASE								=      lm_head['layernorm.weight']
         __SCREAMING_SNAKE_CASE								=      lm_head['layernorm.bias']
  
         # For the decoder, we replicate the weights.
         __SCREAMING_SNAKE_CASE								=      word_embeddings
         __SCREAMING_SNAKE_CASE								=      lm_head['bias']
  
         # The classifier from Megatron (for MLNI).
         __SCREAMING_SNAKE_CASE								=      model['binary_head']
  
         # Store the classifier.
         __SCREAMING_SNAKE_CASE								=      binary_head['weight']
         __SCREAMING_SNAKE_CASE								=      binary_head['bias']
  
         # It should be done!
         return output_state_dict
  
  
  
  
  
  
  def 			__lowercase     (				)       ->				Union[str, Any]:
         # Create the argument parser.
         __SCREAMING_SNAKE_CASE								=      argparse.ArgumentParser()
         parser.add_argument('--print-checkpoint-structure'							,     action='store_true'     )
         parser.add_argument('path_to_checkpoint'							,     type=a__							,     help='Path to the ZIP file containing the checkpoint'     )
         parser.add_argument(
             '--config_file'							,     default=''							,     type=a__							,     help='An optional config json file describing the pre-trained model.'							,     )
         __SCREAMING_SNAKE_CASE								=      parser.parse_args()
  
         # Extract the basename.
         __SCREAMING_SNAKE_CASE								=      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:
                              __SCREAMING_SNAKE_CASE								=      torch.load(a__							,     map_location='cpu'     )
         else:
                __SCREAMING_SNAKE_CASE								=      torch.load(args.path_to_checkpoint							,     map_location='cpu'     )
  
         if args.config_file == "":
                # Default config of megatron-bert 345m
                __SCREAMING_SNAKE_CASE								=      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
                __SCREAMING_SNAKE_CASE								=      input_state_dict['model']['lm_head']['bias'].numel()
         else:
                __SCREAMING_SNAKE_CASE								=      MegatronBertConfig.from_json_file(args.config_file     )
  
         # Convert.
         print('Converting'     )
         __SCREAMING_SNAKE_CASE								=      convert_megatron_checkpoint(a__							,     a__							,     a__     )
  
         # Print the structure of converted state dict.
         if args.print_checkpoint_structure:
                recursive_print(a__							,     a__     )
  
         # Store the config to file.
         print('Saving config'     )
         config.save_pretrained(a__     )
  
         # Store the state_dict to file.
         __SCREAMING_SNAKE_CASE								=      os.path.join(a__							,     'pytorch_model.bin'     )
         print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""     )
         torch.save(a__							,     a__     )
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
     main()
  
  ####################################################################################################
  
  
  
  
  
  
  
style_context: 
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable
  from dataclasses import dataclass
  from typing import Any
  
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				List[str]       						=						('''_front''', '''_back''', '''_len''')
  
  						@dataclass
  						class      UpperCAmelCase_						:
  
  												'''simple docstring'''
  
  
  
  
  												UpperCamelCase__    :				Any       						=						None
  												UpperCamelCase__    :				Deque._Node | None       						=						None
  												UpperCamelCase__    :				Deque._Node | None       						=						None
  
  
  
  
  
  
  
  						class      UpperCAmelCase_						:
  
  												'''simple docstring'''
  
  
  
  
  												UpperCamelCase__    :				Optional[int]       						=						('''_cur''',)
  
  
  
  
  
  
  												def __init__(					self   ,							_A	):
  
  
  
  
  
  
  
  																			'''simple docstring'''
  
  																			__SCREAMING_SNAKE_CASE								=      cur
  
  
  
  
  
  
  												def __iter__(					self	):
  
  
  
  
  
  
  
  																			'''simple docstring'''
  
  																			return self
  
  
  
  
  
  
  												def  _A			(					self	):
  
  
  
  
  
  
  
  																			'''simple docstring'''
  
  																			if self._cur is None:
  																										# finished iterating
  																										raise StopIteration
  																			__SCREAMING_SNAKE_CASE								=      self._cur.val
  																			__SCREAMING_SNAKE_CASE								=      self._cur.next_node
  
  																			return val
  
  
  
  
  
  
  
  						def __init__(					self   ,							_A = None	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      0
  
  													if iterable is not None:
  																				# append every value to the deque
  																				for val in iterable:
  																											self.append(_A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self._Node(_A   ,							_A   ,							_A	)
  													if self.is_empty():
  																				# front = back
  																				__SCREAMING_SNAKE_CASE								=      __SCREAMING_SNAKE_CASE								=      node
  																				__SCREAMING_SNAKE_CASE								=      1
  													else:
  																				# connect nodes
  																				__SCREAMING_SNAKE_CASE								=      node
  																				__SCREAMING_SNAKE_CASE								=      self._back
  																				__SCREAMING_SNAKE_CASE								=      node  # assign new back to the new node
  
  																				self._len += 1
  
  																				# make sure there were no errors
  																				assert not self.is_empty(), "Error on appending value."
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self._Node(_A   ,							_A   ,							_A	)
  													if self.is_empty():
  																				# front = back
  																				__SCREAMING_SNAKE_CASE								=      __SCREAMING_SNAKE_CASE								=      node
  																				__SCREAMING_SNAKE_CASE								=      1
  													else:
  																				# connect nodes
  																				__SCREAMING_SNAKE_CASE								=      self._front
  																				__SCREAMING_SNAKE_CASE								=      node
  																				__SCREAMING_SNAKE_CASE								=      node  # assign new front to the new node
  
  																				self._len += 1
  
  																				# make sure there were no errors
  																				assert not self.is_empty(), "Error on appending value."
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													for val in iterable:
  																				self.append(_A	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													for val in iterable:
  																				self.appendleft(_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													assert not self.is_empty(), "Deque is empty."
  
  													__SCREAMING_SNAKE_CASE								=      self._back
  													__SCREAMING_SNAKE_CASE								=      self._back.prev_node  # set new back
  													# drop the last node - python will deallocate memory automatically
  													__SCREAMING_SNAKE_CASE								=      None
  
  													self._len -= 1
  
  													return topop.val
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													assert not self.is_empty(), "Deque is empty."
  
  													__SCREAMING_SNAKE_CASE								=      self._front
  													__SCREAMING_SNAKE_CASE								=      self._front.next_node  # set new front and drop the first node
  													__SCREAMING_SNAKE_CASE								=      None
  
  													self._len -= 1
  
  													return topop.val
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return self._front is None
  
  
  
  
  
  
  						def __len__(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return self._len
  
  
  
  
  
  
  						def __eq__(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if not isinstance(_A   ,							_A	):
  																				return NotImplemented
  
  													__SCREAMING_SNAKE_CASE								=      self._front
  													__SCREAMING_SNAKE_CASE								=      other._front
  
  													# if the length of the dequeues are not the same, they are not equal
  													if len(self	) != len(_A	):
  																				return False
  
  													while me is not None and oth is not None:
  																				# compare every value
  																				if me.val != oth.val:
  																											return False
  																				__SCREAMING_SNAKE_CASE								=      me.next_node
  																				__SCREAMING_SNAKE_CASE								=      oth.next_node
  
  													return True
  
  
  
  
  
  
  						def __iter__(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return Deque._Iterator(self._front	)
  
  
  
  
  
  
  						def __repr__(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      []
  													__SCREAMING_SNAKE_CASE								=      self._front
  													while aux is not None:
  																				# append the values in a list to display
  																				values_list.append(aux.val	)
  																				__SCREAMING_SNAKE_CASE								=      aux.next_node
  
  													return f"""[{", ".join(repr(_A	) for val in values_list	)}]"""
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  
  
  