
"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     AlbertConfig.from_json_file(_UpperCamelCase						)
  print(f'''Building PyTorch model from configuration: {config}'''						)
  _lowercase:			Any       =     AlbertForPreTraining(_UpperCamelCase						)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
  torch.save(model.state_dict() ,				_UpperCamelCase						)


if __name__ == "__main__":
  A__   :					Dict							=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--albert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained ALBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  A__   :					Tuple							=   parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)


"""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  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=0						):






  """simple docstring"""



  if name is None:
    _lowercase:			str       =     None
  else:
    _lowercase:			Optional[Any]       =     '''.''' * max(0 ,				spaces - 2						) + '''# {:''' + str(50 - spaces						) + '''s}'''
    _lowercase:			Union[str, Any]       =     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  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[Any]       =     {}

  # old versions did not store training args
  _lowercase:			int       =     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))

    _lowercase:			str       =     ds_args.padded_vocab_size
    _lowercase:			Dict       =     ds_args.max_position_embeddings
    _lowercase:			List[str]       =     ds_args.hidden_size
    _lowercase:			List[Any]       =     ds_args.num_layers
    _lowercase:			Optional[int]       =     ds_args.num_attention_heads
    _lowercase:			Any       =     ds_args.ffn_hidden_size
    # pprint(config)

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

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

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

  # The position embeddings.
  _lowercase:			Optional[Any]       =     embeddings['''position_embeddings''']['''weight''']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  _lowercase:			List[str]       =     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.
  _lowercase:			Any       =     pos_embeddings

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

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

  # The simple map of names for "automated" rules.
  _lowercase:			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.
    _lowercase:			str       =     layer_re.match(_UpperCamelCase						)

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

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

    # The name of the layer.
    _lowercase:			Dict       =     f'''transformer.h.{layer_idx}'''

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('''layernorm'''						):
      _lowercase:			Any       =     '''ln_1''' if op_name.startswith('''input'''						) else '''ln_2'''
      _lowercase:			Tuple       =     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.
      _lowercase:			Any       =     torch.tril(torch.ones((n_positions, n_positions) ,				dtype=torch.floataa						)						).view(
          1 ,				1 ,				_UpperCamelCase ,				_UpperCamelCase						)
      _lowercase:			List[Any]       =     causal_mask

      # Insert a "dummy" tensor for masked_bias.
      _lowercase:			Any       =     torch.tensor(-1e4 ,				dtype=torch.floataa						)
      _lowercase:			Tuple       =     masked_bias

      _lowercase:			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.
      _lowercase:			str       =     out_val.transpose(0 ,				1						).contiguous()
      # Store.
      _lowercase:			Tuple       =     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":
      _lowercase:			List[Any]       =     fix_query_key_value_ordering(_UpperCamelCase ,				_UpperCamelCase ,				3 ,				_UpperCamelCase ,				_UpperCamelCase						)
      # Store. No change of shape.
      _lowercase:			Union[str, Any]       =     out_val

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

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

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

  # The final layernorm.
  _lowercase:			str       =     transformer['''final_layernorm.weight''']
  _lowercase:			Dict       =     transformer['''final_layernorm.bias''']

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

  # It should be done!
  return output_state_dict




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			List[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.''' ,				)
  _lowercase:			int       =     parser.parse_args()

  # Extract the basename.
  _lowercase:			Tuple       =     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:
        _lowercase:			str       =     torch.load(_UpperCamelCase ,				map_location='''cpu'''						)
  else:
    _lowercase:			Optional[int]       =     torch.load(args.path_to_checkpoint ,				map_location='''cpu'''						)

  _lowercase:			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:
        _lowercase:			List[str]       =     '''gelu_fast'''
      elif ds_args.openai_gelu:
        _lowercase:			Optional[int]       =     '''gelu_new'''
      else:
        _lowercase:			Any       =     '''gelu'''
    else:
      # in the very early days this used to be "gelu_new"
      _lowercase:			Optional[int]       =     '''gelu_new'''

    # Spell out all parameters in case the defaults change.
    _lowercase:			List[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=_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=50_256 ,				eos_token_id=50_256 ,				)
  else:
    _lowercase:			Optional[int]       =     GPTaConfig.from_json_file(args.config_file						)

  _lowercase:			str       =     ['''GPT2LMHeadModel''']

  # Convert.
  print('''Converting'''						)
  _lowercase:			Optional[int]       =     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:
    _lowercase:			Optional[int]       =     ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      _lowercase:			int       =     '''gpt2'''
    elif tokenizer_type == "PretrainedFromHF":
      _lowercase:			str       =     ds_args.tokenizer_name_or_path
    else:
      raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''						)
  else:
    _lowercase:			str       =     '''gpt2'''

  _lowercase:			List[Any]       =     AutoTokenizer.from_pretrained(_UpperCamelCase						)
  _lowercase:			Any       =     type(_UpperCamelCase						).__name__
  _lowercase:			Tuple       =     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.
  _lowercase:			int       =     os.path.join(_UpperCamelCase ,				'''pytorch_model.bin'''						)
  print(f'''Saving checkpoint to "{output_checkpoint_file}"'''						)
  torch.save(_UpperCamelCase ,				_UpperCamelCase						)


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

if __name__ == "__main__":
  main()

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