






# 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__						(  A		,				A		,				A=0					):
   # Format the message.
   if name is None:
      lowercase__						:	Optional[int]         = None
   else:
      lowercase__						:	Union[str, Any]         = """.""" * max(0		,				spaces - 2					) + """# {:""" + str(50 - spaces					) + """s}"""
      lowercase__						:	Union[str, Any]         = fmt.format(A					)

   # Print and recurse (if needed).
   if isinstance(A		,				A					):
      if msg is not None:
         print(A					)
      for k in val.keys():
         recursive_print(A		,				val[k]		,				spaces + 2					)
   elif isinstance(A		,				torch.Tensor					):
      print(A		,				""":"""		,				val.size()					)
   else:
      print(A		,				""":"""		,				A					)
def       a__						(  A		,				A		,				A		,				A		,				A					):
   # 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.
   lowercase__						:	int         = param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      lowercase__						:	Optional[int]         = (num_heads, hidden_size, num_splits) + input_shape[1:]
      lowercase__						:	Union[str, Any]         = param.view(*A					)
      lowercase__						:	int         = param.transpose(0		,				2					)
      lowercase__						:	Any         = param.transpose(1		,				2					).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowercase__						:	List[Any]         = (num_heads, num_splits, hidden_size) + input_shape[1:]
      lowercase__						:	Optional[int]         = param.view(*A					)
      lowercase__						:	str         = param.transpose(0		,				1					).contiguous()
   lowercase__						:	Union[str, Any]         = param.view(*A					)
   return param
def       a__						(  A		,				A		,				A					):
   # The converted output model.
   lowercase__						:	Any         = {}

   # old versions did not store training args
   lowercase__						:	str         = input_state_dict.get("""args"""		,				A					)
   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__						:	str         = ds_args.tokenizer_type
      lowercase__						:	Tuple         = ds_args.padded_vocab_size
      lowercase__						:	Optional[int]         = ds_args.max_position_embeddings
      lowercase__						:	int         = ds_args.hidden_size
      lowercase__						:	Optional[int]         = ds_args.num_layers
      lowercase__						:	List[str]         = ds_args.num_attention_heads
      lowercase__						:	int         = 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__						:	Optional[int]         = config.num_attention_heads
   # The hidden_size per head.
   lowercase__						:	Optional[int]         = config.hidden_size // heads
   # Megatron-LM checkpoint version
   if "checkpoint_version" in input_state_dict.keys():
      lowercase__						:	Any         = input_state_dict["""checkpoint_version"""]
   else:
      lowercase__						:	int         = 0.0

   # The model.
   lowercase__						:	Dict         = input_state_dict["""model"""]
   # The language model.
   lowercase__						:	Dict         = model["""language_model"""]
   # The embeddings.
   lowercase__						:	Any         = lm["""embedding"""]

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

   # The position embeddings.
   lowercase__						:	int         = 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__						:	str         = pos_embeddings

   # The token-type embeddings.
   lowercase__						:	List[str]         = embeddings["""tokentype_embeddings"""]["""weight"""]
   # Store the position embeddings.
   lowercase__						:	Any         = tokentype_embeddings

   # The transformer.
   lowercase__						:	List[Any]         = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

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

   # The simple map of names for "automated" rules.
   lowercase__						:	List[Any]         = {
       """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__						:	str         = None

   # Extract the layers.
   for key, val in transformer.items():
      # Match the name.
      lowercase__						:	Optional[Any]         = layer_re.match(A					)

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

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

      # The name of the layer.
      lowercase__						:	Any         = F'''bert.encoder.layer.{layer_idx}'''

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith("""layernorm"""					):
         lowercase__						:	List[Any]         = """attention.ln""" if op_name.startswith("""input"""					) else """ln"""
         lowercase__						:	Union[str, 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, ""

         lowercase__						:	Dict         = fix_query_key_value_ordering(A		,				A		,				3		,				A		,				A					)
         # Store the tensor as we need the bias as well to interleave QKV and biases.
         lowercase__						:	Optional[int]         = 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__						:	int         = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
         lowercase__						:	str         = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
         lowercase__						:	List[Any]         = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

         # Clear the stored tensor.
         lowercase__						:	Dict         = None

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

    # The final layernorm.
   lowercase__						:	List[Any]         = transformer["""final_layernorm.weight"""]
   lowercase__						:	Tuple         = transformer["""final_layernorm.bias"""]

   # The pooler.
   lowercase__						:	Tuple         = lm["""pooler"""]

   # Store the matrix and the bias.
   lowercase__						:	List[str]         = pooler["""dense.weight"""]
   lowercase__						:	Tuple         = pooler["""dense.bias"""]

   # The LM head from Megatron (for RACE).
   lowercase__						:	Union[str, Any]         = model["""lm_head"""]

   # The transform matrix.
   lowercase__						:	Union[str, Any]         = lm_head["""dense.weight"""]
   lowercase__						:	int         = lm_head["""dense.bias"""]

   # The transform LN.
   lowercase__						:	Tuple         = lm_head["""layernorm.weight"""]
   lowercase__						:	Union[str, Any]         = lm_head["""layernorm.bias"""]

   # For the decoder, we replicate the weights.
   lowercase__						:	Optional[int]         = word_embeddings
   lowercase__						:	Any         = lm_head["""bias"""]

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

   # Store the classifier.
   lowercase__						:	str         = binary_head["""weight"""]
   lowercase__						:	List[Any]         = binary_head["""bias"""]

   # It should be done!
   return output_state_dict
def       a__						(  ):
   # Create the argument parser.
   lowercase__						:	Optional[int]         = argparse.ArgumentParser()
   parser.add_argument("""--print-checkpoint-structure"""		,				action="""store_true"""					)
   parser.add_argument("""path_to_checkpoint"""		,				type=A		,				help="""Path to the ZIP file containing the checkpoint"""					)
   parser.add_argument(
       """--config_file"""		,				default=""""""		,				type=A		,				help="""An optional config json file describing the pre-trained model."""		,				)
   lowercase__						:	Union[str, Any]         = parser.parse_args()

   # Extract the basename.
   lowercase__						:	str         = 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__						:	Any         = torch.load(A		,				map_location="""cpu"""					)
   else:
      lowercase__						:	str         = torch.load(args.path_to_checkpoint		,				map_location="""cpu"""					)

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

   # Convert.
   print("""Converting"""					)
   lowercase__						:	Dict         = convert_megatron_checkpoint(A		,				A		,				A					)

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

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

   # Store the state_dict to file.
   lowercase__						:	List[Any]         = os.path.join(A		,				"""pytorch_model.bin"""					)
   print(F'''Saving checkpoint to "{output_checkpoint_file}"'''					)
   torch.save(A		,				A					)


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

if __name__ == "__main__":
 main()

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










from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class        __snake_case(					datasets.BuilderConfig      ):
 lowercase :       Optional[Callable]		      =		None
 lowercase :       Optional[dict]		      =		None
 lowercase :       Optional[datasets.Features]		      =		None



 def 						UpperCAmelCase    (	self   :   int	):

    """simple docstring"""






    assert self.generator is not None, "generator must be specified"

    if self.gen_kwargs is None:
       lowercase__						:	List[Any]         = {}
class        __snake_case(					datasets.GeneratorBasedBuilder      ):
 lowercase :       Dict		      =		GeneratorConfig



 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    return datasets.DatasetInfo(features=self.config.features	)



 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   Optional[int]	):

    """simple docstring"""






    return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,		gen_kwargs=self.config.gen_kwargs	)]







 def 						UpperCAmelCase    (	self   :   Dict ,		**UpperCAmelCase__   :   List[str]	):

    """simple docstring"""






    for idx, ex in enumerate(self.config.generator(**UpperCAmelCase__	)	):
       yield idx, ex


