




# 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       lowerCAmelCase_      (	lowerCamelCase:							Dict  ,					lowerCamelCase:							Tuple  ,					lowerCamelCase:							List[Any]=0    )       ->      Tuple:


      '''simple docstring'''




      if name is None:
            lowerCamelCase__   	=					None
      else:
            lowerCamelCase__   	=					'.' * max(0  ,					spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
            lowerCamelCase__   	=					fmt.format(lowerCamelCase    )

      # Print and recurse (if needed).
      if isinstance(lowerCamelCase  ,					lowerCamelCase    ):
            if msg is not None:
                  print(lowerCamelCase    )
            for k in val.keys():
                  recursive_print(lowerCamelCase  ,					val[k]  ,					spaces + 2    )
      elif isinstance(lowerCamelCase  ,					torch.Tensor    ):
            print(lowerCamelCase  ,					':'  ,					val.size()    )
      else:
            print(lowerCamelCase  ,					':'  ,					lowerCamelCase    )

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]  ,					lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							Any  ,					lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					param.size()
      if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            lowerCamelCase__   	=					(num_heads, hidden_size, num_splits) + input_shape[1:]
            lowerCamelCase__   	=					param.view(*lowerCamelCase    )
            lowerCamelCase__   	=					param.transpose(0  ,					2    )
            lowerCamelCase__   	=					param.transpose(1  ,					2    ).contiguous()
      elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            lowerCamelCase__   	=					(num_heads, num_splits, hidden_size) + input_shape[1:]
            lowerCamelCase__   	=					param.view(*lowerCamelCase    )
            lowerCamelCase__   	=					param.transpose(0  ,					1    ).contiguous()
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      return param

def       lowerCAmelCase_      (	lowerCamelCase:							int  ,					lowerCamelCase:							int  ,					lowerCamelCase:							Tuple    )       ->      Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					{}

      # old versions did not store training args
      lowerCamelCase__   	=					input_state_dict.get('args'  ,					lowerCamelCase    )
      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))

            lowerCamelCase__   	=					ds_args.tokenizer_type
            lowerCamelCase__   	=					ds_args.padded_vocab_size
            lowerCamelCase__   	=					ds_args.max_position_embeddings
            lowerCamelCase__   	=					ds_args.hidden_size
            lowerCamelCase__   	=					ds_args.num_layers
            lowerCamelCase__   	=					ds_args.num_attention_heads
            lowerCamelCase__   	=					ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
            # pprint(config)

      # The number of heads.
      lowerCamelCase__   	=					config.num_attention_heads
      # The hidden_size per head.
      lowerCamelCase__   	=					config.hidden_size // heads
      # Megatron-LM checkpoint version
      if "checkpoint_version" in input_state_dict.keys():
            lowerCamelCase__   	=					input_state_dict['checkpoint_version']
      else:
            lowerCamelCase__   	=					0.0

      # The model.
      lowerCamelCase__   	=					input_state_dict['model']
      # The language model.
      lowerCamelCase__   	=					model['language_model']
      # The embeddings.
      lowerCamelCase__   	=					lm['embedding']

      # The word embeddings.
      lowerCamelCase__   	=					embeddings['word_embeddings']['weight']
      # Truncate the embedding table to vocab_size rows.
      lowerCamelCase__   	=					word_embeddings[: config.vocab_size, :]
      # Store the word embeddings.
      lowerCamelCase__   	=					word_embeddings

      # The position embeddings.
      lowerCamelCase__   	=					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.
      lowerCamelCase__   	=					pos_embeddings

      # The token-type embeddings.
      lowerCamelCase__   	=					embeddings['tokentype_embeddings']['weight']
      # Store the position embeddings.
      lowerCamelCase__   	=					tokentype_embeddings

      # The transformer.
      lowerCamelCase__   	=					lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

      # The regex to extract layer names.
      lowerCamelCase__   	=					re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'    )

      # The simple map of names for "automated" rules.
      lowerCamelCase__   	=					{
          '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.
      lowerCamelCase__   	=					None

      # Extract the layers.
      for key, val in transformer.items():
            # Match the name.
            lowerCamelCase__   	=					layer_re.match(lowerCamelCase    )

            # Stop if that's not a layer
            if m is None:
                  break

            # The index of the layer.
            lowerCamelCase__   	=					int(m.group(1    )    )
            # The name of the operation.
            lowerCamelCase__   	=					m.group(2    )
            # Is it a weight or a bias?
            lowerCamelCase__   	=					m.group(3    )

            # The name of the layer.
            lowerCamelCase__   	=					f"""bert.encoder.layer.{layer_idx}"""

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith('layernorm'    ):
                  lowerCamelCase__   	=					'attention.ln' if op_name.startswith('input'    ) else 'ln'
                  lowerCamelCase__   	=					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, ""

                  lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
                  # Store the tensor as we need the bias as well to interleave QKV and biases.
                  lowerCamelCase__   	=					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.
                  lowerCamelCase__   	=					attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
                  lowerCamelCase__   	=					attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
                  lowerCamelCase__   	=					attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

                  lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
                  # Split the bias.
                  lowerCamelCase__   	=					out_val[0 * config.hidden_size : 1 * config.hidden_size]
                  lowerCamelCase__   	=					out_val[1 * config.hidden_size : 2 * config.hidden_size]
                  lowerCamelCase__   	=					out_val[2 * config.hidden_size : 3 * config.hidden_size]

                  # Store.
                  lowerCamelCase__   	=					q
                  lowerCamelCase__   	=					q_bias
                  lowerCamelCase__   	=					k
                  lowerCamelCase__   	=					k_bias
                  lowerCamelCase__   	=					v
                  lowerCamelCase__   	=					v_bias

                  # Clear the stored tensor.
                  lowerCamelCase__   	=					None

            # Copy weights and biases as is.
            elif weight_or_bias in ["weight", "bias"]:
                  lowerCamelCase__   	=					megatron_to_transformers[op_name]
                  lowerCamelCase__   	=					val

    # The final layernorm.
      lowerCamelCase__   	=					transformer['final_layernorm.weight']
      lowerCamelCase__   	=					transformer['final_layernorm.bias']

      # The pooler.
      lowerCamelCase__   	=					lm['pooler']

      # Store the matrix and the bias.
      lowerCamelCase__   	=					pooler['dense.weight']
      lowerCamelCase__   	=					pooler['dense.bias']

      # The LM head from Megatron (for RACE).
      lowerCamelCase__   	=					model['lm_head']

      # The transform matrix.
      lowerCamelCase__   	=					lm_head['dense.weight']
      lowerCamelCase__   	=					lm_head['dense.bias']

      # The transform LN.
      lowerCamelCase__   	=					lm_head['layernorm.weight']
      lowerCamelCase__   	=					lm_head['layernorm.bias']

      # For the decoder, we replicate the weights.
      lowerCamelCase__   	=					word_embeddings
      lowerCamelCase__   	=					lm_head['bias']

      # The classifier from Megatron (for MLNI).
      lowerCamelCase__   	=					model['binary_head']

      # Store the classifier.
      lowerCamelCase__   	=					binary_head['weight']
      lowerCamelCase__   	=					binary_head['bias']

      # It should be done!
      return output_state_dict

def       lowerCAmelCase_      (	)       ->      Dict:


      '''simple docstring'''




      lowerCamelCase__   	=					argparse.ArgumentParser()
      parser.add_argument('--print-checkpoint-structure'  ,					action='store_true'    )
      parser.add_argument('path_to_checkpoint'  ,					type=lowerCamelCase  ,					help='Path to the ZIP file containing the checkpoint'    )
      parser.add_argument(
          '--config_file'  ,					default=''  ,					type=lowerCamelCase  ,					help='An optional config json file describing the pre-trained model.'  ,					)
      lowerCamelCase__   	=					parser.parse_args()

      # Extract the basename.
      lowerCamelCase__   	=					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:
                        lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )
      else:
            lowerCamelCase__   	=					torch.load(args.path_to_checkpoint  ,					map_location='cpu'    )

      if args.config_file == "":
            # Default config of megatron-bert 345m
            lowerCamelCase__   	=					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
            lowerCamelCase__   	=					input_state_dict['model']['lm_head']['bias'].numel()
      else:
            lowerCamelCase__   	=					MegatronBertConfig.from_json_file(args.config_file    )

      # Convert.
      print('Converting'    )
      lowerCamelCase__   	=					convert_megatron_checkpoint(lowerCamelCase  ,					lowerCamelCase  ,					lowerCamelCase    )

      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
            recursive_print(lowerCamelCase  ,					lowerCamelCase    )

      # Store the config to file.
      print('Saving config'    )
      config.save_pretrained(lowerCamelCase    )

      # Store the state_dict to file.
      lowerCamelCase__   	=					os.path.join(lowerCamelCase  ,					'pytorch_model.bin'    )
      print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
      torch.save(lowerCamelCase  ,					lowerCamelCase    )


####################################################################################################

if __name__ == "__main__":
      main()

####################################################################################################





def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					str(lowerCamelCase    ).strip()
      if not bin_string:
            raise ValueError('Empty string was passed to the function'    )
      lowerCamelCase__   	=					bin_string[0] == '-'
      if is_negative:
            lowerCamelCase__   	=					bin_string[1:]
      if not all(char in '01' for char in bin_string    ):
            raise ValueError('Non-binary value was passed to the function'    )
      lowerCamelCase__   	=					0
      for char in bin_string:
            lowerCamelCase__   	=					2 * decimal_number + int(lowerCamelCase    )
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
      from doctest import testmod

      testmod()