






# 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_bert/convert_megatron_bert_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 MegatronBertConfig

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__=0						) ->       Union[str, Any]:
  # Format the message.
  if name is None:
    UpperCamelCase_:      Union[str, Any]					   =       None
  else:
    UpperCamelCase_:      int					   =       '.' * max(0			,					spaces - 2						) + '# {:' + str(5_0 - spaces						) + 's}'
    UpperCamelCase_:      Optional[int]					   =       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 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						) ->       Dict:
  # 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 BERT.
  UpperCamelCase_:      List[str]					   =       param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    UpperCamelCase_:      Dict					   =       (num_heads, hidden_size, num_splits) + input_shape[1:]
    UpperCamelCase_:      Optional[int]					   =       param.view(*UpperCAmelCase__						)
    UpperCamelCase_:      int					   =       param.transpose(0			,					2						)
    UpperCamelCase_:      Optional[Any]					   =       param.transpose(1			,					2						).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    UpperCamelCase_:      Optional[Any]					   =       (num_heads, num_splits, hidden_size) + input_shape[1:]
    UpperCamelCase_:      Optional[int]					   =       param.view(*UpperCAmelCase__						)
    UpperCamelCase_:      Any					   =       param.transpose(0			,					1						).contiguous()
  UpperCamelCase_:      Any					   =       param.view(*UpperCAmelCase__						)
  return param

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						) ->       Tuple:
  # The converted output model.
  UpperCamelCase_:      List[str]					   =       {}

  # old versions did not store training args
  UpperCamelCase_:      Tuple					   =       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))

    UpperCamelCase_:      int					   =       ds_args.tokenizer_type
    UpperCamelCase_:      Union[str, Any]					   =       ds_args.padded_vocab_size
    UpperCamelCase_:      int					   =       ds_args.max_position_embeddings
    UpperCamelCase_:      Dict					   =       ds_args.hidden_size
    UpperCamelCase_:      Tuple					   =       ds_args.num_layers
    UpperCamelCase_:      Tuple					   =       ds_args.num_attention_heads
    UpperCamelCase_:      Any					   =       ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
    # pprint(config)

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

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

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

  # The position embeddings.
  UpperCamelCase_:      Union[str, Any]					   =       embeddings['position_embeddings']['weight']
  assert pos_embeddings.size(0						) == config.max_position_embeddings and pos_embeddings.size(1						) == config.hidden_size
  # Store the position embeddings.
  UpperCamelCase_:      List[str]					   =       pos_embeddings

  # The token-type embeddings.
  UpperCamelCase_:      List[Any]					   =       embeddings['tokentype_embeddings']['weight']
  # Store the position embeddings.
  UpperCamelCase_:      Tuple					   =       tokentype_embeddings

  # The transformer.
  UpperCamelCase_:      Union[str, Any]					   =       lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

  # The simple map of names for "automated" rules.
  UpperCamelCase_:      Dict					   =       {
      'attention.dense': '.attention.output.dense.',
      'self_attention.dense': '.attention.output.dense.',
      'mlp.dense_h_to_4h': '.intermediate.dense.',
      'mlp.dense_4h_to_h': '.output.dense.',
  }

  # Keep track of the attention/query/value tensor.
  UpperCamelCase_:      Tuple					   =       None

  # Extract the layers.
  for key, val in transformer.items():
    # Match the name.
    UpperCamelCase_:      Any					   =       layer_re.match(UpperCAmelCase__						)

    # 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_:      Optional[Any]					   =       m.group(2						)
    # Is it a weight or a bias?
    UpperCamelCase_:      Optional[int]					   =       m.group(3						)

    # The name of the layer.
    UpperCamelCase_:      List[Any]					   =       F'''bert.encoder.layer.{layer_idx}'''

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'						):
      UpperCamelCase_:      Optional[Any]					   =       'attention.ln' if op_name.startswith('input'						) else 'ln'
      UpperCamelCase_:      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":
      # Make sure the QKV pointer is nil.
      assert attention_qkv_weight is None, ""

      UpperCamelCase_:      int					   =       fix_query_key_value_ordering(UpperCAmelCase__			,					UpperCAmelCase__			,					3			,					UpperCAmelCase__			,					UpperCAmelCase__						)
      # Store the tensor as we need the bias as well to interleave QKV and biases.
      UpperCamelCase_:      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":
      # Make sure we read the weight tensor.
      assert attention_qkv_weight is not None, ""

      # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
      UpperCamelCase_:      Optional[int]					   =       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
      UpperCamelCase_:      Optional[int]					   =       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
      UpperCamelCase_:      Optional[int]					   =       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

      UpperCamelCase_:      Dict					   =       fix_query_key_value_ordering(UpperCAmelCase__			,					UpperCAmelCase__			,					3			,					UpperCAmelCase__			,					UpperCAmelCase__						)
      # Split the bias.
      UpperCamelCase_:      List[Any]					   =       out_val[0 * config.hidden_size : 1 * config.hidden_size]
      UpperCamelCase_:      Optional[Any]					   =       out_val[1 * config.hidden_size : 2 * config.hidden_size]
      UpperCamelCase_:      List[str]					   =       out_val[2 * config.hidden_size : 3 * config.hidden_size]

      # Store.
      UpperCamelCase_:      str					   =       q
      UpperCamelCase_:      List[Any]					   =       q_bias
      UpperCamelCase_:      List[Any]					   =       k
      UpperCamelCase_:      List[str]					   =       k_bias
      UpperCamelCase_:      Optional[int]					   =       v
      UpperCamelCase_:      Optional[Any]					   =       v_bias

      # Clear the stored tensor.
      UpperCamelCase_:      List[str]					   =       None

    # Copy weights and biases as is.
    elif weight_or_bias in ["weight", "bias"]:
      UpperCamelCase_:      Optional[Any]					   =       megatron_to_transformers[op_name]
      UpperCamelCase_:      str					   =       val

    # The final layernorm.
  UpperCamelCase_:      int					   =       transformer['final_layernorm.weight']
  UpperCamelCase_:      Tuple					   =       transformer['final_layernorm.bias']

  # The pooler.
  UpperCamelCase_:      Any					   =       lm['pooler']

  # Store the matrix and the bias.
  UpperCamelCase_:      List[str]					   =       pooler['dense.weight']
  UpperCamelCase_:      Dict					   =       pooler['dense.bias']

  # The LM head from Megatron (for RACE).
  UpperCamelCase_:      Optional[int]					   =       model['lm_head']

  # The transform matrix.
  UpperCamelCase_:      int					   =       lm_head['dense.weight']
  UpperCamelCase_:      Dict					   =       lm_head['dense.bias']

  # The transform LN.
  UpperCamelCase_:      Any					   =       lm_head['layernorm.weight']
  UpperCamelCase_:      List[Any]					   =       lm_head['layernorm.bias']

  # For the decoder, we replicate the weights.
  UpperCamelCase_:      List[str]					   =       word_embeddings
  UpperCamelCase_:      int					   =       lm_head['bias']

  # The classifier from Megatron (for MLNI).
  UpperCamelCase_:      Union[str, Any]					   =       model['binary_head']

  # Store the classifier.
  UpperCamelCase_:      Dict					   =       binary_head['weight']
  UpperCamelCase_:      List[str]					   =       binary_head['bias']

  # It should be done!
  return output_state_dict

def 						snake_case      () ->       Tuple:
  # Create the argument parser.
  UpperCamelCase_:      str					   =       argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'			,					action='store_true'						)
  parser.add_argument('path_to_checkpoint'			,					type=UpperCAmelCase__			,					help='Path to the ZIP file containing the checkpoint'						)
  parser.add_argument(
      '--config_file'			,					default=''			,					type=UpperCAmelCase__			,					help='An optional config json file describing the pre-trained model.'			,					)
  UpperCamelCase_:      int					   =       parser.parse_args()

  # Extract the basename.
  UpperCamelCase_:      Optional[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:
        UpperCamelCase_:      Optional[Any]					   =       torch.load(UpperCAmelCase__			,					map_location='cpu'						)
  else:
    UpperCamelCase_:      str					   =       torch.load(args.path_to_checkpoint			,					map_location='cpu'						)

  if args.config_file == "":
    # Default config of megatron-bert 345m
    UpperCamelCase_:      Tuple					   =       MegatronBertConfig()

    # different megatron-bert-*-345m models have different vocab sizes, so override the default
    # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
    UpperCamelCase_:      Optional[int]					   =       input_state_dict['model']['lm_head']['bias'].numel()
  else:
    UpperCamelCase_:      Any					   =       MegatronBertConfig.from_json_file(args.config_file						)

  # Convert.
  print('Converting'						)
  UpperCamelCase_:      str					   =       convert_megatron_checkpoint(UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)

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

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

  # Store the state_dict to file.
  UpperCamelCase_:      Optional[int]					   =       os.path.join(UpperCAmelCase__			,					'pytorch_model.bin'						)
  print(F'''Saving checkpoint to "{output_checkpoint_file}"'''						)
  torch.save(UpperCAmelCase__			,					UpperCAmelCase__						)


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

if __name__ == "__main__":
   main()

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







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_reformer import ReformerTokenizer
else:
   A_	:	Union[str, Any]				=				None


A_	:	List[Any]				=				logging.get_logger(__name__)


A_	:	List[str]				=				'▁'

A_	:	int				=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A_	:	Optional[Any]				=				{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

A_	:	List[Any]				=				{
    'google/reformer-crime-and-punishment': 524288,
}




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	int								=VOCAB_FILES_NAMES
       a       :	Optional[Any]								=PRETRAINED_VOCAB_FILES_MAP
       a       :	List[Any]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a       :	Tuple								=['''input_ids''', '''attention_mask''']
       a       :	Optional[Any]								=ReformerTokenizer





       def __init__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase="</s>"						,    _lowerCamelCase="<unk>"						,    _lowerCamelCase=[]						,    **_lowerCamelCase						,    ):
         super().__init__(
             _lowerCamelCase						,    tokenizer_file=_lowerCamelCase						,    eos_token=_lowerCamelCase						,    unk_token=_lowerCamelCase						,    additional_special_tokens=_lowerCamelCase						,    **_lowerCamelCase						,    )

         UpperCamelCase_:      Any					   =       vocab_file
         UpperCamelCase_:      str					   =       False if not self.vocab_file else True





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase = None							):
         if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'							)

         if not os.path.isdir(_lowerCamelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         UpperCamelCase_:      str					   =       os.path.join(
             _lowerCamelCase						,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
           copyfile(self.vocab_file						,    _lowerCamelCase							)

         return (out_vocab_file,)