





"""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_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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=0					):






  """simple docstring"""





  if name is None:
    A_  :       Optional[Any]										=  None
  else:
    A_  :       Dict										=  '.' * max(0 ,					spaces - 2					) + '# {:' + str(50 - spaces					) + 's}'
    A_  :       List[str]										=  fmt.format(_UpperCAmelCase					)

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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       int										=  {}

  # old versions did not store training args
  A_  :       Optional[Any]										=  input_state_dict.get('args' ,					_UpperCAmelCase					)
  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_  :       str										=  ds_args.padded_vocab_size
    A_  :       Tuple										=  ds_args.max_position_embeddings
    A_  :       int										=  ds_args.hidden_size
    A_  :       str										=  ds_args.num_layers
    A_  :       List[Any]										=  ds_args.num_attention_heads
    A_  :       str										=  ds_args.ffn_hidden_size
    # pprint(config)

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

  # The model.
  A_  :       Optional[Any]										=  input_state_dict['model']
  # The language model.
  A_  :       List[str]										=  model['language_model']
  # The embeddings.
  A_  :       Optional[Any]										=  lm['embedding']

  # The word embeddings.
  A_  :       Tuple										=  embeddings['word_embeddings']['weight']
  # Truncate the embedding table to vocab_size rows.
  A_  :       Union[str, Any]										=  word_embeddings[: config.vocab_size, :]
  A_  :       Union[str, Any]										=  word_embeddings

  # The position embeddings.
  A_  :       Tuple										=  embeddings['position_embeddings']['weight']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  A_  :       Tuple										=  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.
  A_  :       Dict										=  pos_embeddings

  # The transformer.
  A_  :       Optional[int]										=  lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

  # The regex to extract layer names.
  A_  :       List[Any]										=  re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'					)

  # The simple map of names for "automated" rules.
  A_  :       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.
    A_  :       List[Any]										=  layer_re.match(_UpperCAmelCase					)

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

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

    # The name of the layer.
    A_  :       Union[str, Any]										=  f"""transformer.h.{layer_idx}"""

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

      # Insert a "dummy" tensor for masked_bias.
      A_  :       Optional[Any]										=  torch.tensor(-1E4 ,					dtype=torch.floataa					)
      A_  :       Any										=  masked_bias

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

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

    # Copy the bias.
    elif weight_or_bias == "bias":
      A_  :       Optional[Any]										=  megatron_to_transformers[op_name]
      A_  :       Any										=  val

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

  # The final layernorm.
  A_  :       List[Any]										=  transformer['final_layernorm.weight']
  A_  :       Union[str, Any]										=  transformer['final_layernorm.bias']

  # For LM head, transformers' wants the matrix to weight embeddings.
  A_  :       List[str]										=  word_embeddings

  # It should be done!
  return output_state_dict





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





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

  # Extract the basename.
  A_  :       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:
        A_  :       List[str]										=  torch.load(_UpperCAmelCase ,					map_location='cpu'					)
  else:
    A_  :       int										=  torch.load(args.path_to_checkpoint ,					map_location='cpu'					)

  A_  :       Dict										=  input_state_dict.get('args' ,					_UpperCAmelCase					)

  # 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:
        A_  :       int										=  'gelu_fast'
      elif ds_args.openai_gelu:
        A_  :       Any										=  'gelu_new'
      else:
        A_  :       Dict										=  'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      A_  :       Dict										=  'gelu_new'

    # Spell out all parameters in case the defaults change.
    A_  :       Any										=  GPTaConfig(
        vocab_size=50257 ,					n_positions=1024 ,					n_embd=1024 ,					n_layer=24 ,					n_head=16 ,					n_inner=4096 ,					activation_function=_UpperCAmelCase ,					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=_UpperCAmelCase ,					summary_activation=_UpperCAmelCase ,					summary_proj_to_labels=_UpperCAmelCase ,					summary_first_dropout=0.1 ,					scale_attn_weights=_UpperCAmelCase ,					use_cache=_UpperCAmelCase ,					bos_token_id=50256 ,					eos_token_id=50256 ,					)
  else:
    A_  :       List[str]										=  GPTaConfig.from_json_file(args.config_file					)

  A_  :       Optional[Any]										=  ['GPT2LMHeadModel']

  # Convert.
  print('Converting'					)
  A_  :       Union[str, Any]										=  convert_megatron_checkpoint(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

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

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

  A_  :       Any										=  AutoTokenizer.from_pretrained(_UpperCAmelCase					)
  A_  :       Dict										=  type(_UpperCAmelCase					).__name__
  A_  :       int										=  tokenizer_class

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

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

  # Store the state_dict to file.
  A_  :       List[Any]										=  os.path.join(_UpperCAmelCase ,					'pytorch_model.bin'					)
  print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""					)
  torch.save(_UpperCAmelCase ,					_UpperCAmelCase					)


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

if __name__ == "__main__":
   main()

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






"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)