
'''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	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=0 )     ->       Dict:


		'''simple docstring'''


		if name is None:
				snake_case     :					List[Any]     =	None
		else:
				snake_case     :					Optional[Any]     =	'''.''' * max(0   ,					spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
				snake_case     :					List[str]     =	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	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:


		'''simple docstring'''


		snake_case     :					Any     =	param.size()
		if checkpoint_version == 1.0:
				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
				snake_case     :					Union[str, Any]     =	(num_heads, hidden_size, num_splits) + input_shape[1:]
				snake_case     :					List[str]     =	param.view(*lowercase__ )
				snake_case     :					Dict     =	param.transpose(0   ,					2 )
				snake_case     :					Dict     =	param.transpose(1   ,					2 ).contiguous()
		elif checkpoint_version >= 2.0:
				# other versions store [num_heads * num_splits * hidden_size, :]
				snake_case     :					Union[str, Any]     =	(num_heads, num_splits, hidden_size) + input_shape[1:]
				snake_case     :					List[str]     =	param.view(*lowercase__ )
				snake_case     :					Union[str, Any]     =	param.transpose(0   ,					1 ).contiguous()
		snake_case     :					Optional[int]     =	param.view(*lowercase__ )
		return param




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Optional[int]:


		'''simple docstring'''


		snake_case     :					Tuple     =	{}

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

		# The model.
		snake_case     :					Any     =	input_state_dict['''model''']
		# The language model.
		snake_case     :					Union[str, Any]     =	model['''language_model''']
		# The embeddings.
		snake_case     :					Union[str, Any]     =	lm['''embedding''']

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

		# The position embeddings.
		snake_case     :					Any     =	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     :					int     =	pos_embeddings

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

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

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

		# The simple map of names for "automated" rules.
		snake_case     :					Optional[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.
		snake_case     :					Dict     =	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     :					Dict     =	m.group(2 )
				# Is it a weight or a bias?
				snake_case     :					List[str]     =	m.group(3 )

				# The name of the layer.
				snake_case     :					Union[str, Any]     =	F'bert.encoder.layer.{layer_idx}'

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

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

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

						# Clear the stored tensor.
						snake_case     :					int     =	None

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

    # The final layernorm.
		snake_case     :					Any     =	transformer['''final_layernorm.weight''']
		snake_case     :					List[str]     =	transformer['''final_layernorm.bias''']

		# The pooler.
		snake_case     :					List[Any]     =	lm['''pooler''']

		# Store the matrix and the bias.
		snake_case     :					int     =	pooler['''dense.weight''']
		snake_case     :					Optional[int]     =	pooler['''dense.bias''']

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

		# The transform matrix.
		snake_case     :					int     =	lm_head['''dense.weight''']
		snake_case     :					str     =	lm_head['''dense.bias''']

		# The transform LN.
		snake_case     :					int     =	lm_head['''layernorm.weight''']
		snake_case     :					Tuple     =	lm_head['''layernorm.bias''']

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

		# The classifier from Megatron (for MLNI).
		snake_case     :					List[Any]     =	model['''binary_head''']

		# Store the classifier.
		snake_case     :					Union[str, Any]     =	binary_head['''weight''']
		snake_case     :					int     =	binary_head['''bias''']

		# It should be done!
		return output_state_dict




def       _UpperCamelCase	(					)     ->       str:


		'''simple docstring'''


		snake_case     :					List[str]     =	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     :					str     =	parser.parse_args()

		# Extract the basename.
		snake_case     :					Dict     =	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     :					List[Any]     =	torch.load(lowercase__   ,					map_location='''cpu''' )
		else:
				snake_case     :					Union[str, Any]     =	torch.load(args.path_to_checkpoint   ,					map_location='''cpu''' )

		if args.config_file == "":
				# Default config of megatron-bert 345m
				snake_case     :					Optional[int]     =	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     :					Optional[Any]     =	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     :					Any     =	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     :					List[Any]     =	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'''
import numpy as np




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:


		'''simple docstring'''


		snake_case ,snake_case     :					str     =	a.shape
		snake_case     :					Union[str, Any]     =	min(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
		snake_case     :					List[Any]     =	np.eye(SCREAMING_SNAKE_CASE__ )
		snake_case     :					Union[str, Any]     =	a.copy()

		for k in range(t - 1 ):
				# select a column of modified matrix A':
				snake_case     :					Union[str, Any]     =	r[k:, [k]]
				# construct first basis vector
				snake_case     :					str     =	np.zeros_like(SCREAMING_SNAKE_CASE__ )
				snake_case     :					List[str]     =	1.0
				# determine scaling factor
				snake_case     :					int     =	np.linalg.norm(SCREAMING_SNAKE_CASE__ )
				# construct vector v for Householder reflection
				snake_case     :					Optional[int]     =	x + np.sign(x[0] ) * alpha * ea
				v /= np.linalg.norm(SCREAMING_SNAKE_CASE__ )

				# construct the Householder matrix
				snake_case     :					Tuple     =	np.eye(m - k ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				snake_case     :					int     =	np.block([[np.eye(SCREAMING_SNAKE_CASE__ ), np.zeros((k, m - k) )], [np.zeros((m - k, k) ), q_k]] )

				snake_case     :					int     =	q @ q_k.T
				snake_case     :					Dict     =	q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()


