# 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_gpt2/convert_megatron_gpt2_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 AutoTokenizer, GPTaConfig
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase=0						)	->	List[str]:

      '''simple docstring'''



      if name is None:
            _snake_case						:					Union[str, Any]    =   None
      else:
            _snake_case						:					List[str]    =   "." * max(0 ,      spaces - 2						) + "# {:" + str(50 - spaces						) + "s}"
            _snake_case						:					Optional[Any]    =   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  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	Union[str, Any]:

      '''simple docstring'''



      _snake_case						:					int    =   param.size()
      if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            _snake_case						:					Optional[Any]    =   (num_heads, hidden_size, num_splits) + input_shape[1:]
            _snake_case						:					int    =   param.view(*__lowercase						)
            _snake_case						:					Union[str, Any]    =   param.transpose(0 ,      2						)
            _snake_case						:					List[str]    =   param.transpose(1 ,      2						).contiguous()
      elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            _snake_case						:					Dict    =   (num_heads, num_splits, hidden_size) + input_shape[1:]
            _snake_case						:					Union[str, Any]    =   param.view(*__lowercase						)
            _snake_case						:					Union[str, Any]    =   param.transpose(0 ,      1						).contiguous()
      _snake_case						:					Optional[int]    =   param.view(*__lowercase						)
      return param
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	Optional[Any]:

      '''simple docstring'''



      _snake_case						:					List[Any]    =   {}

      # old versions did not store training args
      _snake_case						:					Any    =   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))

            _snake_case						:					str    =   ds_args.padded_vocab_size
            _snake_case						:					List[Any]    =   ds_args.max_position_embeddings
            _snake_case						:					Union[str, Any]    =   ds_args.hidden_size
            _snake_case						:					Union[str, Any]    =   ds_args.num_layers
            _snake_case						:					List[Any]    =   ds_args.num_attention_heads
            _snake_case						:					Any    =   ds_args.ffn_hidden_size
            # pprint(config)

      # The number of heads.
      _snake_case						:					Union[str, Any]    =   config.n_head
      # The hidden_size per head.
      _snake_case						:					int    =   config.n_embd // config.n_head
      # Megatron-LM checkpoint version
      if "checkpoint_version" in input_state_dict.keys():
            _snake_case						:					int    =   input_state_dict["checkpoint_version"]
      else:
            _snake_case						:					Optional[Any]    =   0.0

      # The model.
      _snake_case						:					Union[str, Any]    =   input_state_dict["model"]
      # The language model.
      _snake_case						:					str    =   model["language_model"]
      # The embeddings.
      _snake_case						:					List[str]    =   lm["embedding"]

      # The word embeddings.
      _snake_case						:					Optional[int]    =   embeddings["word_embeddings"]["weight"]
      # Truncate the embedding table to vocab_size rows.
      _snake_case						:					Tuple    =   word_embeddings[: config.vocab_size, :]
      _snake_case						:					List[Any]    =   word_embeddings

      # The position embeddings.
      _snake_case						:					List[Any]    =   embeddings["position_embeddings"]["weight"]
      # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
      _snake_case						:					Dict    =   pos_embeddings.size(0						)
      if n_positions != config.n_positions:
            raise ValueError(
                F"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"""						)
      # Store the position embeddings.
      _snake_case						:					Any    =   pos_embeddings

      # The transformer.
      _snake_case						:					Union[str, Any]    =   lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

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

      # The simple map of names for "automated" rules.
      _snake_case						:					Union[str, Any]    =   {
          "attention.dense": ".attn.c_proj.",
          "self_attention.dense": ".attn.c_proj.",
          "mlp.dense_h_to_4h": ".mlp.c_fc.",
          "mlp.dense_4h_to_h": ".mlp.c_proj.",
      }

      # Extract the layers.
      for key, val in transformer.items():
            # Match the name.
            _snake_case						:					Optional[Any]    =   layer_re.match(__lowercase						)

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

            # The index of the layer.
            _snake_case						:					Optional[Any]    =   int(m.group(1						)						)
            # The name of the operation.
            _snake_case						:					Optional[int]    =   m.group(2						)
            # Is it a weight or a bias?
            _snake_case						:					Any    =   m.group(3						)

            # The name of the layer.
            _snake_case						:					List[Any]    =   F"""transformer.h.{layer_idx}"""

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith("layernorm"						):
                  _snake_case						:					Union[str, Any]    =   "ln_1" if op_name.startswith("input"						) else "ln_2"
                  _snake_case						:					str    =   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":
                  # Insert a tensor of 1x1xDxD bias.
                  _snake_case						:					str    =   torch.tril(torch.ones((n_positions, n_positions) ,      dtype=torch.floataa						)						).view(
                      1 ,      1 ,      __lowercase ,      __lowercase						)
                  _snake_case						:					Union[str, Any]    =   causal_mask

                  # Insert a "dummy" tensor for masked_bias.
                  _snake_case						:					Optional[Any]    =   torch.tensor(-1e4 ,      dtype=torch.floataa						)
                  _snake_case						:					int    =   masked_bias

                  _snake_case						:					List[Any]    =   fix_query_key_value_ordering(__lowercase ,      __lowercase ,      3 ,      __lowercase ,      __lowercase						)
                  # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
                  _snake_case						:					Dict    =   out_val.transpose(0 ,      1						).contiguous()
                  # Store.
                  _snake_case						:					Dict    =   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":
                  _snake_case						:					Optional[Any]    =   fix_query_key_value_ordering(__lowercase ,      __lowercase ,      3 ,      __lowercase ,      __lowercase						)
                  # Store. No change of shape.
                  _snake_case						:					List[Any]    =   out_val

            # Transpose the weights.
            elif weight_or_bias == "weight":
                  _snake_case						:					Optional[Any]    =   megatron_to_transformers[op_name]
                  _snake_case						:					int    =   val.transpose(0 ,      1						)

            # Copy the bias.
            elif weight_or_bias == "bias":
                  _snake_case						:					Optional[int]    =   megatron_to_transformers[op_name]
                  _snake_case						:					Tuple    =   val

    # DEBUG.
      assert config.n_layer == layer_idx + 1

      # The final layernorm.
      _snake_case						:					str    =   transformer["final_layernorm.weight"]
      _snake_case						:					Optional[Any]    =   transformer["final_layernorm.bias"]

      # For LM head, transformers' wants the matrix to weight embeddings.
      _snake_case						:					Tuple    =   word_embeddings

      # It should be done!
      return output_state_dict
def  snake_case						()	->	Tuple:

      '''simple docstring'''



      _snake_case						:					List[str]    =   argparse.ArgumentParser()
      parser.add_argument("--print-checkpoint-structure" ,      action="store_true"						)
      parser.add_argument(
          "path_to_checkpoint" ,      type=__lowercase ,      help="Path to the checkpoint file (.zip archive or direct .pt file)" ,      )
      parser.add_argument(
          "--config_file" ,      default="" ,      type=__lowercase ,      help="An optional config json file describing the pre-trained model." ,      )
      _snake_case						:					Optional[Any]    =   parser.parse_args()

      # Extract the basename.
      _snake_case						:					List[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:
                        _snake_case						:					Dict    =   torch.load(__lowercase ,      map_location="cpu"						)
      else:
            _snake_case						:					List[str]    =   torch.load(args.path_to_checkpoint ,      map_location="cpu"						)

      _snake_case						:					Tuple    =   input_state_dict.get("args" ,      __lowercase						)

      # Read the config, or default to the model released by NVIDIA.
      if args.config_file == "":
            if ds_args is not None:
                  if ds_args.bias_gelu_fusion:
                        _snake_case						:					Union[str, Any]    =   "gelu_fast"
                  elif ds_args.openai_gelu:
                        _snake_case						:					Dict    =   "gelu_new"
                  else:
                        _snake_case						:					Optional[Any]    =   "gelu"
            else:
                  # in the very early days this used to be "gelu_new"
                  _snake_case						:					str    =   "gelu_new"

            # Spell out all parameters in case the defaults change.
            _snake_case						:					str    =   GPTaConfig(
                vocab_size=50_257 ,      n_positions=1_024 ,      n_embd=1_024 ,      n_layer=24 ,      n_head=16 ,      n_inner=4_096 ,      activation_function=__lowercase ,      resid_pdrop=0.1 ,      embd_pdrop=0.1 ,      attn_pdrop=0.1 ,      layer_norm_epsilon=1e-5 ,      initializer_range=0.02 ,      summary_type="cls_index" ,      summary_use_proj=__lowercase ,      summary_activation=__lowercase ,      summary_proj_to_labels=__lowercase ,      summary_first_dropout=0.1 ,      scale_attn_weights=__lowercase ,      use_cache=__lowercase ,      bos_token_id=50_256 ,      eos_token_id=50_256 ,      )
      else:
            _snake_case						:					Tuple    =   GPTaConfig.from_json_file(args.config_file						)

      _snake_case						:					str    =   ["GPT2LMHeadModel"]

      # Convert.
      print("Converting"						)
      _snake_case						:					Union[str, Any]    =   convert_megatron_checkpoint(__lowercase ,      __lowercase ,      __lowercase						)

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

      # Add tokenizer class info to config
      # see https://github.com/huggingface/transformers/issues/13906)
      if ds_args is not None:
            _snake_case						:					List[str]    =   ds_args.tokenizer_type
            if tokenizer_type == "GPT2BPETokenizer":
                  _snake_case						:					str    =   "gpt2"
            elif tokenizer_type == "PretrainedFromHF":
                  _snake_case						:					str    =   ds_args.tokenizer_name_or_path
            else:
                  raise ValueError(F"""Unrecognized tokenizer_type {tokenizer_type}"""						)
      else:
            _snake_case						:					str    =   "gpt2"

      _snake_case						:					Union[str, Any]    =   AutoTokenizer.from_pretrained(__lowercase						)
      _snake_case						:					Dict    =   type(__lowercase						).__name__
      _snake_case						:					Optional[int]    =   tokenizer_class

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

      # Save tokenizer based on args
      print(F"""Adding {tokenizer_class} tokenizer files"""						)
      tokenizer.save_pretrained(__lowercase						)

      # Store the state_dict to file.
      _snake_case						:					Union[str, Any]    =   os.path.join(__lowercase ,      "pytorch_model.bin"						)
      print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""						)
      torch.save(__lowercase ,      __lowercase						)


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

if __name__ == "__main__":
    main()

####################################################################################################
def  snake_case						(__lowercase						)	->	list:

						'''simple docstring'''



						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__lowercase						)						)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
				__import__('doctest').testmod()