


"""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    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	int       ,      _snake_case :	Dict=0	):
      # Format the message.
      if name is None:
            lowerCAmelCase    :		List[Any]				=      None
      else:
            lowerCAmelCase    :		int				=      '''.''' * max(0       ,      spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
            lowerCAmelCase    :		Tuple				=      fmt.format(_snake_case	)

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


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


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	List[str]       ,      _snake_case :	Tuple	):
      # The converted output model.
      lowerCAmelCase    :		List[Any]				=      {}

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

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

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

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

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

      # The transformer.
      lowerCAmelCase    :		Dict				=      lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

      # The simple map of names for "automated" rules.
      lowerCAmelCase    :		int				=      {
          '''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    :		int				=      layer_re.match(_snake_case	)

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

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

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

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

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

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

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

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

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

      # The final layernorm.
      lowerCAmelCase    :		Optional[Any]				=      transformer['''final_layernorm.weight''']
      lowerCAmelCase    :		Tuple				=      transformer['''final_layernorm.bias''']

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

      # It should be done!
      return output_state_dict


def    _snake_case  (  ):
      # Create the argument parser.
      lowerCAmelCase    :		Optional[Any]				=      argparse.ArgumentParser()
      parser.add_argument('''--print-checkpoint-structure'''       ,      action='''store_true'''	)
      parser.add_argument(
          '''path_to_checkpoint'''       ,      type=_snake_case       ,      help='''Path to the checkpoint file (.zip archive or direct .pt file)'''       ,      )
      parser.add_argument(
          '''--config_file'''       ,      default=''''''       ,      type=_snake_case       ,      help='''An optional config json file describing the pre-trained model.'''       ,      )
      lowerCAmelCase    :		Optional[int]				=      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    :		List[Any]				=      torch.load(_snake_case       ,      map_location='''cpu'''	)
      else:
            lowerCAmelCase    :		int				=      torch.load(args.path_to_checkpoint       ,      map_location='''cpu'''	)

      lowerCAmelCase    :		List[str]				=      input_state_dict.get('''args'''       ,      _snake_case	)

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

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

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

      # Convert.
      print('''Converting'''	)
      lowerCAmelCase    :		int				=      convert_megatron_checkpoint(_snake_case       ,      _snake_case       ,      _snake_case	)

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

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

      lowerCAmelCase    :		Optional[Any]				=      AutoTokenizer.from_pretrained(_snake_case	)
      lowerCAmelCase    :		Dict				=      type(_snake_case	).__name__
      lowerCAmelCase    :		Dict				=      tokenizer_class

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

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

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


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

if __name__ == "__main__":
 main()

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






"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


snake_case__						:   Dict			 = logging.getLogger(__name__)


def    _snake_case  (  _snake_case :	Any       ,      _snake_case :	Any	):
      return (preds == labels).mean()



@dataclass
class 						snake_case_:
     __UpperCamelCase     =    field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,    )



@dataclass
class 						snake_case_:
     __UpperCamelCase     =    field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()	)}	)
     __UpperCamelCase     =    field(metadata={'''help''': '''Should contain the data files for the task.'''}	)
     __UpperCamelCase     =    field(
         default=128							,    metadata={
             '''help''': (
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )
         }							,    )
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)



def    _snake_case  (  ):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      lowerCAmelCase    :		str				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
      lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir	)
          and os.listdir(training_args.output_dir	)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                ''' --overwrite_output_dir to overcome.'''	)

      # Setup logging
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,      datefmt='''%m/%d/%Y %H:%M:%S'''       ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,      )
      logger.warning(
          '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''       ,      training_args.local_rank       ,      training_args.device       ,      training_args.n_gpu       ,      bool(training_args.local_rank != -1	)       ,      training_args.fpaa       ,      )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank	):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info('''Training/evaluation parameters %s'''       ,      _snake_case	)

      # Set seed
      set_seed(training_args.seed	)

      try:
            lowerCAmelCase    :		Tuple				=      processors[data_args.task_name]()
            lowerCAmelCase    :		Any				=      processor.get_labels()
            lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	)
      except KeyError:
            raise ValueError('''Task not found: %s''' % (data_args.task_name)	)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      lowerCAmelCase    :		List[Any]				=      AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,      num_labels=_snake_case       ,      finetuning_task=data_args.task_name       ,      cache_dir=model_args.cache_dir       ,      )
      lowerCAmelCase    :		Optional[Any]				=      AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,      cache_dir=model_args.cache_dir       ,      )
      lowerCAmelCase    :		List[str]				=      AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path       ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)       ,      config=_snake_case       ,      cache_dir=model_args.cache_dir       ,      )

      # Get datasets
      lowerCAmelCase    :		Dict				=      (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,      tokenizer=_snake_case       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.train       ,      )
          if training_args.do_train
          else None
      )
      lowerCAmelCase    :		Any				=      (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,      tokenizer=_snake_case       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.dev       ,      )
          if training_args.do_eval
          else None
      )

      def compute_metrics(_snake_case :	EvalPrediction	) -> Dict:
            lowerCAmelCase    :		int				=      np.argmax(p.predictions       ,      axis=1	)
            return {"acc": simple_accuracy(_snake_case       ,      p.label_ids	)}

      # Data collator
      lowerCAmelCase    :		List[Any]				=      DataCollatorWithPadding(_snake_case       ,      pad_to_multiple_of=8	) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCAmelCase    :		Union[str, Any]				=      Trainer(
          model=_snake_case       ,      args=_snake_case       ,      train_dataset=_snake_case       ,      eval_dataset=_snake_case       ,      compute_metrics=_snake_case       ,      data_collator=_snake_case       ,      )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
      lowerCAmelCase    :		int				=      {}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''	)

            lowerCAmelCase    :		Any				=      trainer.evaluate()

            lowerCAmelCase    :		int				=      os.path.join(training_args.output_dir       ,      '''eval_results.txt'''	)
            if trainer.is_world_master():
                  with open(_snake_case       ,      '''w'''	) as writer:
                        logger.info('''***** Eval results *****'''	)
                        for key, value in result.items():
                              logger.info('''  %s = %s'''       ,      _snake_case       ,      _snake_case	)
                              writer.write('''%s = %s\n''' % (key, value)	)

                        results.update(_snake_case	)

      return results


def    _snake_case  (  _snake_case :	List[str]	):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
 main()


