






# 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 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase=0   ):


    '''simple docstring'''



    if name is None:
        _lowercase     			=						None
    else:
        _lowercase     			=						'.' * max(0,   spaces - 2   ) + '# {:' + str(50 - spaces   ) + 's}'
        _lowercase     			=						fmt.format(_lowercase   )

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


    '''simple docstring'''



    _lowercase     			=						param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        _lowercase     			=						(num_heads, hidden_size, num_splits) + input_shape[1:]
        _lowercase     			=						param.view(*_lowercase   )
        _lowercase     			=						param.transpose(0,   2   )
        _lowercase     			=						param.transpose(1,   2   ).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        _lowercase     			=						(num_heads, num_splits, hidden_size) + input_shape[1:]
        _lowercase     			=						param.view(*_lowercase   )
        _lowercase     			=						param.transpose(0,   1   ).contiguous()
    _lowercase     			=						param.view(*_lowercase   )
    return param
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						{}

    # old versions did not store training args
    _lowercase     			=						input_state_dict.get('args',   _lowercase   )
    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     			=						ds_args.tokenizer_type
        _lowercase     			=						ds_args.padded_vocab_size
        _lowercase     			=						ds_args.max_position_embeddings
        _lowercase     			=						ds_args.hidden_size
        _lowercase     			=						ds_args.num_layers
        _lowercase     			=						ds_args.num_attention_heads
        _lowercase     			=						ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
        # pprint(config)

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

    # The model.
    _lowercase     			=						input_state_dict['model']
    # The language model.
    _lowercase     			=						model['language_model']
    # The embeddings.
    _lowercase     			=						lm['embedding']

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

    # The position embeddings.
    _lowercase     			=						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.
    _lowercase     			=						pos_embeddings

    # The token-type embeddings.
    _lowercase     			=						embeddings['tokentype_embeddings']['weight']
    # Store the position embeddings.
    _lowercase     			=						tokentype_embeddings

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

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

    # The simple map of names for "automated" rules.
    _lowercase     			=						{
        '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.
    _lowercase     			=						None

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

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

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

        # The name of the layer.
        _lowercase     			=						f'''bert.encoder.layer.{layer_idx}'''

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith('layernorm'   ):
            _lowercase     			=						'attention.ln' if op_name.startswith('input'   ) else 'ln'
            _lowercase     			=						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, ""

            _lowercase     			=						fix_query_key_value_ordering(_lowercase,   _lowercase,   3,   _lowercase,   _lowercase   )
            # Store the tensor as we need the bias as well to interleave QKV and biases.
            _lowercase     			=						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.
            _lowercase     			=						attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
            _lowercase     			=						attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
            _lowercase     			=						attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

            _lowercase     			=						fix_query_key_value_ordering(_lowercase,   _lowercase,   3,   _lowercase,   _lowercase   )
            # Split the bias.
            _lowercase     			=						out_val[0 * config.hidden_size : 1 * config.hidden_size]
            _lowercase     			=						out_val[1 * config.hidden_size : 2 * config.hidden_size]
            _lowercase     			=						out_val[2 * config.hidden_size : 3 * config.hidden_size]

            # Store.
            _lowercase     			=						q
            _lowercase     			=						q_bias
            _lowercase     			=						k
            _lowercase     			=						k_bias
            _lowercase     			=						v
            _lowercase     			=						v_bias

            # Clear the stored tensor.
            _lowercase     			=						None

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

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

    # The pooler.
    _lowercase     			=						lm['pooler']

    # Store the matrix and the bias.
    _lowercase     			=						pooler['dense.weight']
    _lowercase     			=						pooler['dense.bias']

    # The LM head from Megatron (for RACE).
    _lowercase     			=						model['lm_head']

    # The transform matrix.
    _lowercase     			=						lm_head['dense.weight']
    _lowercase     			=						lm_head['dense.bias']

    # The transform LN.
    _lowercase     			=						lm_head['layernorm.weight']
    _lowercase     			=						lm_head['layernorm.bias']

    # For the decoder, we replicate the weights.
    _lowercase     			=						word_embeddings
    _lowercase     			=						lm_head['bias']

    # The classifier from Megatron (for MLNI).
    _lowercase     			=						model['binary_head']

    # Store the classifier.
    _lowercase     			=						binary_head['weight']
    _lowercase     			=						binary_head['bias']

    # It should be done!
    return output_state_dict
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						argparse.ArgumentParser()
    parser.add_argument('--print-checkpoint-structure',   action='store_true'   )
    parser.add_argument('path_to_checkpoint',   type=_lowercase,   help='Path to the ZIP file containing the checkpoint'   )
    parser.add_argument(
        '--config_file',   default='',   type=_lowercase,   help='An optional config json file describing the pre-trained model.',   )
    _lowercase     			=						parser.parse_args()

    # Extract the basename.
    _lowercase     			=						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     			=						torch.load(_lowercase,   map_location='cpu'   )
    else:
        _lowercase     			=						torch.load(args.path_to_checkpoint,   map_location='cpu'   )

    if args.config_file == "":
        # Default config of megatron-bert 345m
        _lowercase     			=						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
        _lowercase     			=						input_state_dict['model']['lm_head']['bias'].numel()
    else:
        _lowercase     			=						MegatronBertConfig.from_json_file(args.config_file   )

    # Convert.
    print('Converting'   )
    _lowercase     			=						convert_megatron_checkpoint(_lowercase,   _lowercase,   _lowercase   )

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

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

    # Store the state_dict to file.
    _lowercase     			=						os.path.join(_lowercase,   'pytorch_model.bin'   )
    print(f'''Saving checkpoint to "{output_checkpoint_file}"'''   )
    torch.save(_lowercase,   _lowercase   )


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

if __name__ == "__main__":
   main()

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCamelCase					    =	{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50Tokenizer''']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50TokenizerFast''']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa import MBartaaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
   import sys

   lowerCamelCase					    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
