





'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    _lowerCAmelCase		     = [0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    _lowerCAmelCase		     = 1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(SCREAMING_SNAKE_CASE_						,    n + 1      ):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()









'''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_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 		__a(SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]						,    SCREAMING_SNAKE_CASE_					:   int=0      ):



    '''simple docstring'''





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

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


def 		__a(SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Dict						,    SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]      ):



    '''simple docstring'''





    _lowerCAmelCase		     = {}

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

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

    # The model.
    _lowerCAmelCase		     = input_state_dict["model"]
    # The language model.
    _lowerCAmelCase		     = model["language_model"]
    # The embeddings.
    _lowerCAmelCase		     = lm["embedding"]

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

    # The position embeddings.
    _lowerCAmelCase		     = 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.
    _lowerCAmelCase		     = pos_embeddings

    # The token-type embeddings.
    _lowerCAmelCase		     = embeddings["tokentype_embeddings"]["weight"]
    # Store the position embeddings.
    _lowerCAmelCase		     = tokentype_embeddings

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

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

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

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

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

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

        # The name of the layer.
        _lowerCAmelCase		     = F'''bert.encoder.layer.{layer_idx}'''

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

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

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

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

            # Clear the stored tensor.
            _lowerCAmelCase		     = None

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

    # The final layernorm.
    _lowerCAmelCase		     = transformer["final_layernorm.weight"]
    _lowerCAmelCase		     = transformer["final_layernorm.bias"]

    # The pooler.
    _lowerCAmelCase		     = lm["pooler"]

    # Store the matrix and the bias.
    _lowerCAmelCase		     = pooler["dense.weight"]
    _lowerCAmelCase		     = pooler["dense.bias"]

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

    # The transform matrix.
    _lowerCAmelCase		     = lm_head["dense.weight"]
    _lowerCAmelCase		     = lm_head["dense.bias"]

    # The transform LN.
    _lowerCAmelCase		     = lm_head["layernorm.weight"]
    _lowerCAmelCase		     = lm_head["layernorm.bias"]

    # For the decoder, we replicate the weights.
    _lowerCAmelCase		     = word_embeddings
    _lowerCAmelCase		     = lm_head["bias"]

    # The classifier from Megatron (for MLNI).
    _lowerCAmelCase		     = model["binary_head"]

    # Store the classifier.
    _lowerCAmelCase		     = binary_head["weight"]
    _lowerCAmelCase		     = binary_head["bias"]

    # It should be done!
    return output_state_dict


def 		__a():



    '''simple docstring'''





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

    # Extract the basename.
    _lowerCAmelCase		     = 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		     = torch.load(SCREAMING_SNAKE_CASE_						,    map_location="cpu"      )
    else:
        _lowerCAmelCase		     = torch.load(args.path_to_checkpoint						,    map_location="cpu"      )

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

    # Convert.
    print("Converting"      )
    _lowerCAmelCase		     = convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )

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

    # Store the config to file.
    print("Saving config"      )
    config.save_pretrained(SCREAMING_SNAKE_CASE_      )

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


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

if __name__ == "__main__":
       main()

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


