"""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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Optional[int]=0	)       ->      Dict:
  # Format the message.
  if name is None:
    _lowerCAmelCase						:       int  			=     None
  else:
    _lowerCAmelCase						:       int  			=     """.""" * max(0			,spaces - 2	) + """# {:""" + str(50 - spaces	) + """s}"""
    _lowerCAmelCase						:       Optional[int]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Dict			,_lowerCamelCase						:       Dict	)       ->      Tuple:
  # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
  # for compatibility with later versions of NVIDIA Megatron-LM.
  # The inverse operation is performed inside Megatron-LM to read checkpoints:
  # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
  # If param is the weight tensor of the self-attention block, the returned tensor
  # will have to be transposed one more time to be read by HuggingFace GPT2.
  _lowerCAmelCase						:       int  			=     param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    _lowerCAmelCase						:       Union[str, Any]  			=     (num_heads, hidden_size, num_splits) + input_shape[1:]
    _lowerCAmelCase						:       List[str]  			=     param.view(*_lowerCamelCase	)
    _lowerCAmelCase						:       Tuple  			=     param.transpose(0			,2	)
    _lowerCAmelCase						:       List[Any]  			=     param.transpose(1			,2	).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    _lowerCAmelCase						:       List[Any]  			=     (num_heads, num_splits, hidden_size) + input_shape[1:]
    _lowerCAmelCase						:       str  			=     param.view(*_lowerCamelCase	)
    _lowerCAmelCase						:       int  			=     param.transpose(0			,1	).contiguous()
  _lowerCAmelCase						:       List[Any]  			=     param.view(*_lowerCamelCase	)
  return param





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Dict	)       ->      Optional[int]:
  # The converted output model.
  _lowerCAmelCase						:       Tuple  			=     {}

  # old versions did not store training args
  _lowerCAmelCase						:       List[Any]  			=     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						:       Any  			=     ds_args.padded_vocab_size
    _lowerCAmelCase						:       Tuple  			=     ds_args.max_position_embeddings
    _lowerCAmelCase						:       Dict  			=     ds_args.hidden_size
    _lowerCAmelCase						:       Dict  			=     ds_args.num_layers
    _lowerCAmelCase						:       str  			=     ds_args.num_attention_heads
    _lowerCAmelCase						:       List[Any]  			=     ds_args.ffn_hidden_size
    # pprint(config)

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

  # The model.
  _lowerCAmelCase						:       Tuple  			=     input_state_dict["""model"""]
  # The language model.
  _lowerCAmelCase						:       List[Any]  			=     model["""language_model"""]
  # The embeddings.
  _lowerCAmelCase						:       List[str]  			=     lm["""embedding"""]

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

  # The position embeddings.
  _lowerCAmelCase						:       List[str]  			=     embeddings["""position_embeddings"""]["""weight"""]
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  _lowerCAmelCase						:       int  			=     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.
  _lowerCAmelCase						:       Tuple  			=     pos_embeddings

  # The transformer.
  _lowerCAmelCase						:       List[Any]  			=     lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

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

  # The simple map of names for "automated" rules.
  _lowerCAmelCase						:       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.
    _lowerCAmelCase						:       Optional[Any]  			=     layer_re.match(_lowerCamelCase	)

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

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

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

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith("""layernorm"""	):
      _lowerCAmelCase						:       Optional[int]  			=     """ln_1""" if op_name.startswith("""input"""	) else """ln_2"""
      _lowerCAmelCase						:       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.
      _lowerCAmelCase						:       Any  			=     torch.tril(torch.ones((n_positions, n_positions)			,dtype=torch.floataa	)	).view(
          1			,1			,_lowerCamelCase			,_lowerCamelCase	)
      _lowerCAmelCase						:       str  			=     causal_mask

      # Insert a "dummy" tensor for masked_bias.
      _lowerCAmelCase						:       str  			=     torch.tensor(-1e4			,dtype=torch.floataa	)
      _lowerCAmelCase						:       Union[str, Any]  			=     masked_bias

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

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

    # Copy the bias.
    elif weight_or_bias == "bias":
      _lowerCAmelCase						:       Union[str, Any]  			=     megatron_to_transformers[op_name]
      _lowerCAmelCase						:       List[str]  			=     val

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

  # The final layernorm.
  _lowerCAmelCase						:       Union[str, Any]  			=     transformer["""final_layernorm.weight"""]
  _lowerCAmelCase						:       Union[str, Any]  			=     transformer["""final_layernorm.bias"""]

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

  # It should be done!
  return output_state_dict





def 							SCREAMING_SNAKE_CASE     (			)       ->      int:
  # Create the argument parser.
  _lowerCAmelCase						:       Tuple  			=     argparse.ArgumentParser()
  parser.add_argument("""--print-checkpoint-structure"""			,action="""store_true"""	)
  parser.add_argument(
      """path_to_checkpoint"""			,type=_lowerCamelCase			,help="""Path to the checkpoint file (.zip archive or direct .pt file)"""			,)
  parser.add_argument(
      """--config_file"""			,default=""""""			,type=_lowerCamelCase			,help="""An optional config json file describing the pre-trained model."""			,)
  _lowerCAmelCase						:       Dict  			=     parser.parse_args()

  # Extract the basename.
  _lowerCAmelCase						:       Optional[int]  			=     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						:       int  			=     torch.load(_lowerCamelCase			,map_location="""cpu"""	)
  else:
    _lowerCAmelCase						:       List[Any]  			=     torch.load(args.path_to_checkpoint			,map_location="""cpu"""	)

  _lowerCAmelCase						:       Union[str, Any]  			=     input_state_dict.get("""args"""			,_lowerCamelCase	)

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

    # Spell out all parameters in case the defaults change.
    _lowerCAmelCase						:       Dict  			=     GPTaConfig(
        vocab_size=50257			,n_positions=1024			,n_embd=1024			,n_layer=24			,n_head=16			,n_inner=4096			,activation_function=_lowerCamelCase			,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=_lowerCamelCase			,summary_activation=_lowerCamelCase			,summary_proj_to_labels=_lowerCamelCase			,summary_first_dropout=0.1			,scale_attn_weights=_lowerCamelCase			,use_cache=_lowerCamelCase			,bos_token_id=50256			,eos_token_id=50256			,)
  else:
    _lowerCAmelCase						:       int  			=     GPTaConfig.from_json_file(args.config_file	)

  _lowerCAmelCase						:       Tuple  			=     ["""GPT2LMHeadModel"""]

  # Convert.
  print("""Converting"""	)
  _lowerCAmelCase						:       int  			=     convert_megatron_checkpoint(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)

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

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

  _lowerCAmelCase						:       Union[str, Any]  			=     AutoTokenizer.from_pretrained(_lowerCamelCase	)
  _lowerCAmelCase						:       int  			=     type(_lowerCamelCase	).__name__
  _lowerCAmelCase						:       str  			=     tokenizer_class

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

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

  # Store the state_dict to file.
  _lowerCAmelCase						:       int  			=     os.path.join(_lowerCamelCase			,"""pytorch_model.bin"""	)
  print(f"Saving checkpoint to \"{output_checkpoint_file}\""	)
  torch.save(_lowerCamelCase			,_lowerCamelCase	)


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

if __name__ == "__main__":
     main()

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :       List[Any]									=  {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       Dict									=  [
        """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """NllbMoeForConditionalGeneration""",
        """NllbMoeModel""",
        """NllbMoePreTrainedModel""",
        """NllbMoeTop2Router""",
        """NllbMoeSparseMLP""",
    ]


if TYPE_CHECKING:
    from .configuration_nllb_moe import (
        NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
        NllbMoeConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_nllb_moe import (
            NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
            NllbMoeForConditionalGeneration,
            NllbMoeModel,
            NllbMoePreTrainedModel,
            NllbMoeSparseMLP,
            NllbMoeTopaRouter,
        )


else:
    import sys

    lowercase      :       List[Any]									=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


