




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



def 					UpperCamelCase__       (						lowercase__					:       Tuple    ,       lowercase__					:       Tuple    ,       lowercase__					:       int				):
		# The converted output model.
		snake_case						:  Optional[int]        =			{}

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

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

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

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

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

		# The position embeddings.
		snake_case						:  Dict        =			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.
		snake_case						:  Tuple        =			pos_embeddings

		# The token-type embeddings.
		snake_case						:  Dict        =			embeddings["tokentype_embeddings"]["weight"]
		# Store the position embeddings.
		snake_case						:  Optional[int]        =			tokentype_embeddings

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

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

		# The simple map of names for "automated" rules.
		snake_case						:  Optional[int]        =			{
		    "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.
		snake_case						:  List[Any]        =			None

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

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

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

				# The name of the layer.
				snake_case						:  Optional[int]        =			F'''bert.encoder.layer.{layer_idx}'''

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

						snake_case						:  Dict        =			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.
						snake_case						:  Tuple        =			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.
						snake_case						:  List[str]        =			attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
						snake_case						:  List[str]        =			attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
						snake_case						:  Any        =			attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

						# Clear the stored tensor.
						snake_case						:  Optional[int]        =			None

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

    # The final layernorm.
		snake_case						:  Tuple        =			transformer["final_layernorm.weight"]
		snake_case						:  int        =			transformer["final_layernorm.bias"]

		# The pooler.
		snake_case						:  Optional[Any]        =			lm["pooler"]

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

		# The LM head from Megatron (for RACE).
		snake_case						:  Optional[int]        =			model["lm_head"]

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

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

		# For the decoder, we replicate the weights.
		snake_case						:  int        =			word_embeddings
		snake_case						:  str        =			lm_head["bias"]

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

		# Store the classifier.
		snake_case						:  str        =			binary_head["weight"]
		snake_case						:  Tuple        =			binary_head["bias"]

		# It should be done!
		return output_state_dict



def 					UpperCamelCase__       (						):
		# Create the argument parser.
		snake_case						:  Any        =			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."    ,       )
		snake_case						:  int        =			parser.parse_args()

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

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

		# Convert.
		print("Converting"				)
		snake_case						:  List[str]        =			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.
		snake_case						:  Optional[int]        =			os.path.join(lowercase__    ,       "pytorch_model.bin"				)
		print(F'''Saving checkpoint to "{output_checkpoint_file}"'''				)
		torch.save(lowercase__    ,       lowercase__				)


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

if __name__ == "__main__":
							main()

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







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       set    ,       lowercase__					:       set				):
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_a={set_a}] is not a set'''
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_b={set_b}] is not a set'''

		return {a + b for a in set_a for b in set_b}


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

							testmod()

