






'''simple docstring'''




# 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		(    __lowercase     ,						__lowercase     ,						__lowercase=0 ) ->		Dict:

      '''simple docstring'''


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

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

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase ) ->		List[Any]:

      '''simple docstring'''


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

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase ) ->		Optional[int]:

      '''simple docstring'''


      _A      =			{}

      # old versions did not store training args
      _A      =			input_state_dict.get("args"     ,						lowercase__ )
      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))

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

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

      # The model.
      _A      =			input_state_dict["model"]
      # The language model.
      _A      =			model["language_model"]
      # The embeddings.
      _A      =			lm["embedding"]

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

      # The position embeddings.
      _A      =			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.
      _A      =			pos_embeddings

      # The token-type embeddings.
      _A      =			embeddings["tokentype_embeddings"]["weight"]
      # Store the position embeddings.
      _A      =			tokentype_embeddings

      # The transformer.
      _A      =			lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

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

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

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

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

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

            # The name of the layer.
            _A      =			F'''bert.encoder.layer.{layer_idx}'''

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

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

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

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

                  # Clear the stored tensor.
                  _A      =			None

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

    # The final layernorm.
      _A      =			transformer["final_layernorm.weight"]
      _A      =			transformer["final_layernorm.bias"]

      # The pooler.
      _A      =			lm["pooler"]

      # Store the matrix and the bias.
      _A      =			pooler["dense.weight"]
      _A      =			pooler["dense.bias"]

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

      # The transform matrix.
      _A      =			lm_head["dense.weight"]
      _A      =			lm_head["dense.bias"]

      # The transform LN.
      _A      =			lm_head["layernorm.weight"]
      _A      =			lm_head["layernorm.bias"]

      # For the decoder, we replicate the weights.
      _A      =			word_embeddings
      _A      =			lm_head["bias"]

      # The classifier from Megatron (for MLNI).
      _A      =			model["binary_head"]

      # Store the classifier.
      _A      =			binary_head["weight"]
      _A      =			binary_head["bias"]

      # It should be done!
      return output_state_dict

def       __lowercase		(    ) ->		str:

      '''simple docstring'''


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

      # Extract the basename.
      _A      =			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:
                        _A      =			torch.load(lowercase__     ,						map_location="cpu" )
      else:
            _A      =			torch.load(args.path_to_checkpoint     ,						map_location="cpu" )

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

      # Convert.
      print("Converting" )
      _A      =			convert_megatron_checkpoint(lowercase__     ,						lowercase__     ,						lowercase__ )

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

      # Store the config to file.
      print("Saving config" )
      config.save_pretrained(lowercase__ )

      # Store the state_dict to file.
      _A      =			os.path.join(lowercase__     ,						"pytorch_model.bin" )
      print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
      torch.save(lowercase__     ,						lowercase__ )


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

if __name__ == "__main__":
    main()

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














'''simple docstring'''




from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			data
         _A      =			None

   def __str__(	self				:			str	):





         '''simple docstring'''





         return f'''{self.data}'''





class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			None
         _A      =			None

   def __iter__(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			self.front
         while node:
               yield node.data
               _A      =			node.next

   def __len__(	self				:			Union[str, Any]	):





         '''simple docstring'''





         return len(tuple(iter(self	)	)	)

   def __str__(	self				:			int	):





         '''simple docstring'''





         return " <- ".join(str(__UpperCAmelCase	) for item in self	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         return len(self	) == 0

   def 		lowerCAmelCase				(	self				:			int   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			Node(__UpperCAmelCase	)
         if self.is_empty():
               _A      =			_A      =			node
         else:
               assert isinstance(self.rear   ,   __UpperCAmelCase	)
               _A      =			node
               _A      =			node

   def 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         if self.is_empty():
               raise IndexError("dequeue from empty queue"	)
         assert isinstance(self.front   ,   __UpperCAmelCase	)
         _A      =			self.front
         _A      =			self.front.next
         if self.front is None:
               _A      =			None
         return node.data

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			_A      =			None


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

    testmod()





