

# 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 	lowerCamelCase__	(   _a							,     _a							,     _a=0):
	# Format the message.
	if name is None:
		SCREAMING_SNAKE_CASE				: str			       =    None
	else:
		SCREAMING_SNAKE_CASE				: List[str]			       =    "." * max(0							,     spaces - 2) + "# {:" + str(50 - spaces) + "s}"
		SCREAMING_SNAKE_CASE				: List[str]			       =    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 	lowerCamelCase__	(   _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.
	SCREAMING_SNAKE_CASE				: List[Any]			       =    param.size()
	if checkpoint_version == 1.0:
		# version 1.0 stores [num_heads * hidden_size * num_splits, :]
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    (num_heads, hidden_size, num_splits) + input_shape[1:]
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    param.view(*_a)
		SCREAMING_SNAKE_CASE				: int			       =    param.transpose(0							,     2)
		SCREAMING_SNAKE_CASE				: List[str]			       =    param.transpose(1							,     2).contiguous()
	elif checkpoint_version >= 2.0:
		# other versions store [num_heads * num_splits * hidden_size, :]
		SCREAMING_SNAKE_CASE				: str			       =    (num_heads, num_splits, hidden_size) + input_shape[1:]
		SCREAMING_SNAKE_CASE				: str			       =    param.view(*_a)
		SCREAMING_SNAKE_CASE				: List[str]			       =    param.transpose(0							,     1).contiguous()
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    param.view(*_a)
	return param

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	# The converted output model.
	SCREAMING_SNAKE_CASE				: Tuple			       =    {}

	# old versions did not store training args
	SCREAMING_SNAKE_CASE				: Dict			       =    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))

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

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

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

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

	# The position embeddings.
	SCREAMING_SNAKE_CASE				: 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.
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    pos_embeddings

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

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

	# The regex to extract layer names.
	SCREAMING_SNAKE_CASE				: List[str]			       =    re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")

	# The simple map of names for "automated" rules.
	SCREAMING_SNAKE_CASE				: Union[str, 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.
	SCREAMING_SNAKE_CASE				: List[str]			       =    None

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

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

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

		# The name of the layer.
		SCREAMING_SNAKE_CASE				: str			       =    f"bert.encoder.layer.{layer_idx}"

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

			SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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.
			SCREAMING_SNAKE_CASE				: 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.
			SCREAMING_SNAKE_CASE				: Dict			       =    attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
			SCREAMING_SNAKE_CASE				: Optional[int]			       =    attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
			SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

			# Clear the stored tensor.
			SCREAMING_SNAKE_CASE				: str			       =    None

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

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

	# The pooler.
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    lm["pooler"]

	# Store the matrix and the bias.
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    pooler["dense.weight"]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    pooler["dense.bias"]

	# The LM head from Megatron (for RACE).
	SCREAMING_SNAKE_CASE				: List[str]			       =    model["lm_head"]

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

	# The transform LN.
	SCREAMING_SNAKE_CASE				: int			       =    lm_head["layernorm.weight"]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    lm_head["layernorm.bias"]

	# For the decoder, we replicate the weights.
	SCREAMING_SNAKE_CASE				: List[Any]			       =    word_embeddings
	SCREAMING_SNAKE_CASE				: Dict			       =    lm_head["bias"]

	# The classifier from Megatron (for MLNI).
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    model["binary_head"]

	# Store the classifier.
	SCREAMING_SNAKE_CASE				: Dict			       =    binary_head["weight"]
	SCREAMING_SNAKE_CASE				: List[Any]			       =    binary_head["bias"]

	# It should be done!
	return output_state_dict

def 	lowerCamelCase__	(   ):
	# Create the argument parser.
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    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."							,     )
	SCREAMING_SNAKE_CASE				: Tuple			       =    parser.parse_args()

	# Extract the basename.
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    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:
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    torch.load(_a							,     map_location="cpu")
	else:
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.load(args.path_to_checkpoint							,     map_location="cpu")

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

	# Convert.
	print("Converting")
	SCREAMING_SNAKE_CASE				: Tuple			       =    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.
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    os.path.join(_a							,     "pytorch_model.bin")
	print(f"Saving checkpoint to \"{output_checkpoint_file}\"")
	torch.save(_a							,     _a)


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

if __name__ == "__main__":
			main()

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


import re

def 	lowerCamelCase__	(   _a):
	def docstring_decorator(_a):
		SCREAMING_SNAKE_CASE				: List[Any]			       =    fn.__doc__
		SCREAMING_SNAKE_CASE				: List[str]			       =    func_doc.split("\n")
		SCREAMING_SNAKE_CASE				: Dict			       =    0
		while i < len(_a) and re.search(r"^\s*Examples?:\s*$"							,     lines[i]) is None:
			i += 1
		if i < len(_a):
			SCREAMING_SNAKE_CASE				: Tuple			       =    example_docstring
			SCREAMING_SNAKE_CASE				: List[Any]			       =    "\n".join(_a)
		else:
			raise ValueError(
			    f"The function {fn} should have an empty 'Examples:' in its docstring as placeholder, "
			    f"current docstring is:\n{func_doc}")
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    func_doc
		return fn

	return docstring_decorator