




# 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    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_=0 )    ->		int:

    """simple docstring"""


    if name is None:
        __UpperCamelCase											=  None
    else:
        __UpperCamelCase											=  '''.''' * max(0   ,       spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
        __UpperCamelCase											=  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    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Union[str, Any]:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Optional[Any]:

    """simple docstring"""


    __UpperCamelCase											=  {}

    # old versions did not store training args
    __UpperCamelCase											=  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))

        __UpperCamelCase											=  ds_args.tokenizer_type
        __UpperCamelCase											=  ds_args.padded_vocab_size
        __UpperCamelCase											=  ds_args.max_position_embeddings
        __UpperCamelCase											=  ds_args.hidden_size
        __UpperCamelCase											=  ds_args.num_layers
        __UpperCamelCase											=  ds_args.num_attention_heads
        __UpperCamelCase											=  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											=  config.num_attention_heads
    # The hidden_size per head.
    __UpperCamelCase											=  config.hidden_size // heads
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
        __UpperCamelCase											=  input_state_dict['''checkpoint_version''']
    else:
        __UpperCamelCase											=  0.0

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

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

    # The position embeddings.
    __UpperCamelCase											=  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											=  pos_embeddings

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

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

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

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

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

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

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

        # The name of the layer.
        __UpperCamelCase											=  F"bert.encoder.layer.{layer_idx}"

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith('''layernorm''' ):
            __UpperCamelCase											=  '''attention.ln''' if op_name.startswith('''input''' ) else '''ln'''
            __UpperCamelCase											=  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											=  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.
            __UpperCamelCase											=  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											=  attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
            __UpperCamelCase											=  attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
            __UpperCamelCase											=  attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

            # Clear the stored tensor.
            __UpperCamelCase											=  None

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

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

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

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

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

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

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

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

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

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

    # It should be done!
    return output_state_dict




def    __SCREAMING_SNAKE_CASE				(					)    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  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.'''   ,       )
    __UpperCamelCase											=  parser.parse_args()

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

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

    # Convert.
    print('''Converting''' )
    __UpperCamelCase											=  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.
    __UpperCamelCase											=  os.path.join(lowercase_   ,       '''pytorch_model.bin''' )
    print(F"Saving checkpoint to \"{output_checkpoint_file}\"" )
    torch.save(lowercase_   ,       lowercase_ )


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

if __name__ == "__main__":
 main()

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












def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise TypeError('''recursive_lucas_number accepts only integer arguments.''' )
    if n_th_number == 0:
        return 2
    if n_th_number == 1:
        return 1

    return recursive_lucas_number(n_th_number - 1 ) + recursive_lucas_number(
        n_th_number - 2 )




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise TypeError('''dynamic_lucas_number accepts only integer arguments.''' )
    __UpperCamelCase						, __UpperCamelCase											=  2, 1
    for _ in range(lowercase_ ):
        __UpperCamelCase						, __UpperCamelCase											=  b, a + b
    return a


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 a_										=       int(input("Enter the number of terms in lucas series:\n").strip())
 print("Using recursive function to calculate lucas series:")
 print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
 print("\nUsing dynamic function to calculate lucas series:")
 print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))






