






# 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 ,   __lowerCAmelCase ,   __lowerCAmelCase=0      )			->     int:
     # Format the message.
     if name is None:
          UpperCamelCase__      :       Union[str, Any]	    =  None
     else:
          UpperCamelCase__      :       Any	    =  "." * max(0 ,   spaces - 2      ) + "# {:" + str(50 - spaces      ) + "s}"
          UpperCamelCase__      :       str	    =  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 ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[Any]:
     # 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.
     UpperCamelCase__      :       Dict	    =  param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          UpperCamelCase__      :       Any	    =  (num_heads, hidden_size, num_splits) + input_shape[1:]
          UpperCamelCase__      :       Tuple	    =  param.view(*__lowerCAmelCase      )
          UpperCamelCase__      :       str	    =  param.transpose(0 ,   2      )
          UpperCamelCase__      :       List[str]	    =  param.transpose(1 ,   2      ).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          UpperCamelCase__      :       int	    =  (num_heads, num_splits, hidden_size) + input_shape[1:]
          UpperCamelCase__      :       Dict	    =  param.view(*__lowerCAmelCase      )
          UpperCamelCase__      :       List[str]	    =  param.transpose(0 ,   1      ).contiguous()
     UpperCamelCase__      :       Optional[int]	    =  param.view(*__lowerCAmelCase      )
     return param







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[Any]:
     # The converted output model.
     UpperCamelCase__      :       str	    =  {}

     # old versions did not store training args
     UpperCamelCase__      :       List[str]	    =  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))

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

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

     # The model.
     UpperCamelCase__      :       Optional[int]	    =  input_state_dict["model"]
     # The language model.
     UpperCamelCase__      :       List[Any]	    =  model["language_model"]
     # The embeddings.
     UpperCamelCase__      :       List[str]	    =  lm["embedding"]

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

     # The position embeddings.
     UpperCamelCase__      :       Union[str, Any]	    =  embeddings["position_embeddings"]["weight"]
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     UpperCamelCase__      :       Optional[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.
     UpperCamelCase__      :       Tuple	    =  pos_embeddings

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

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

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

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

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

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

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

               # Insert a "dummy" tensor for masked_bias.
               UpperCamelCase__      :       Any	    =  torch.tensor(-1E4 ,   dtype=torch.floataa      )
               UpperCamelCase__      :       int	    =  masked_bias

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

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

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

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

     # The final layernorm.
     UpperCamelCase__      :       Any	    =  transformer["final_layernorm.weight"]
     UpperCamelCase__      :       Tuple	    =  transformer["final_layernorm.bias"]

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

     # It should be done!
     return output_state_dict







def 		SCREAMING_SNAKE_CASE							( )			->     Optional[Any]:
     # Create the argument parser.
     UpperCamelCase__      :       int	    =  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." ,   )
     UpperCamelCase__      :       Optional[int]	    =  parser.parse_args()

     # Extract the basename.
     UpperCamelCase__      :       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:
                    UpperCamelCase__      :       Union[str, Any]	    =  torch.load(__lowerCAmelCase ,   map_location="cpu"      )
     else:
          UpperCamelCase__      :       Optional[int]	    =  torch.load(args.path_to_checkpoint ,   map_location="cpu"      )

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

          # Spell out all parameters in case the defaults change.
          UpperCamelCase__      :       Tuple	    =  GPTaConfig(
              vocab_size=5_0257 ,   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=0.0_2 ,   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=5_0256 ,   eos_token_id=5_0256 ,   )
     else:
          UpperCamelCase__      :       Any	    =  GPTaConfig.from_json_file(args.config_file      )

     UpperCamelCase__      :       Any	    =  ["GPT2LMHeadModel"]

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

     UpperCamelCase__      :       List[Any]	    =  AutoTokenizer.from_pretrained(__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[Any]	    =  type(__lowerCAmelCase      ).__name__
     UpperCamelCase__      :       Union[str, Any]	    =  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.
     UpperCamelCase__      :       List[str]	    =  os.path.join(__lowerCAmelCase ,   "pytorch_model.bin"      )
     print(f'Saving checkpoint to "{output_checkpoint_file}"'      )
     torch.save(__lowerCAmelCase ,   __lowerCAmelCase      )


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

if __name__ == "__main__":
 main()

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







from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=A__	)
class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			field(default='''language-modeling'''	,       metadata={'''include_in_asdict_even_if_is_default''': True}	)

 _lowerCAmelCase	:	ClassVar[Features]				=			Features({'''text''': Value('''string'''	)}	)
 _lowerCAmelCase	:	ClassVar[Features]				=			Features({}	)
 _lowerCAmelCase	:	str				=			"text"







 @property
 def  __lowercase (		self :       str						):


      '''simple docstring'''






      return {self.text_column: "text"}