





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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Tuple:
						# The converted output model.
						UpperCAmelCase_      :   List[Any]      =	{}

						# old versions did not store training args
						UpperCAmelCase_      :   Optional[Any]      =	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))

												UpperCAmelCase_      :   List[str]      =	ds_args.tokenizer_type
												UpperCAmelCase_      :   Any      =	ds_args.padded_vocab_size
												UpperCAmelCase_      :   List[str]      =	ds_args.max_position_embeddings
												UpperCAmelCase_      :   int      =	ds_args.hidden_size
												UpperCAmelCase_      :   Optional[Any]      =	ds_args.num_layers
												UpperCAmelCase_      :   Union[str, Any]      =	ds_args.num_attention_heads
												UpperCAmelCase_      :   Optional[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.
						UpperCAmelCase_      :   Tuple      =	config.num_attention_heads
						# The hidden_size per head.
						UpperCAmelCase_      :   Dict      =	config.hidden_size // heads
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												UpperCAmelCase_      :   Optional[Any]      =	input_state_dict['''checkpoint_version''']
						else:
												UpperCAmelCase_      :   Dict      =	0.0

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

						# The word embeddings.
						UpperCAmelCase_      :   Dict      =	embeddings['''word_embeddings''']['''weight''']
						# Truncate the embedding table to vocab_size rows.
						UpperCAmelCase_      :   List[str]      =	word_embeddings[: config.vocab_size, :]
						# Store the word embeddings.
						UpperCAmelCase_      :   Union[str, Any]      =	word_embeddings

						# The position embeddings.
						UpperCAmelCase_      :   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.
						UpperCAmelCase_      :   List[Any]      =	pos_embeddings

						# The token-type embeddings.
						UpperCAmelCase_      :   Tuple      =	embeddings['''tokentype_embeddings''']['''weight''']
						# Store the position embeddings.
						UpperCAmelCase_      :   Union[str, Any]      =	tokentype_embeddings

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

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

						# The simple map of names for "automated" rules.
						UpperCAmelCase_      :   List[str]      =	{
						    '''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.
						UpperCAmelCase_      :   str      =	None

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

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

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

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

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

																		UpperCAmelCase_      :   List[str]      =	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.
																		UpperCAmelCase_      :   Union[str, 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.
																		UpperCAmelCase_      :   Tuple      =	attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		UpperCAmelCase_      :   List[str]      =	attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		UpperCAmelCase_      :   Optional[int]      =	attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

																		# Clear the stored tensor.
																		UpperCAmelCase_      :   Tuple      =	None

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

    # The final layernorm.
						UpperCAmelCase_      :   Union[str, Any]      =	transformer['''final_layernorm.weight''']
						UpperCAmelCase_      :   Tuple      =	transformer['''final_layernorm.bias''']

						# The pooler.
						UpperCAmelCase_      :   int      =	lm['''pooler''']

						# Store the matrix and the bias.
						UpperCAmelCase_      :   Any      =	pooler['''dense.weight''']
						UpperCAmelCase_      :   List[str]      =	pooler['''dense.bias''']

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

						# The transform matrix.
						UpperCAmelCase_      :   List[str]      =	lm_head['''dense.weight''']
						UpperCAmelCase_      :   Any      =	lm_head['''dense.bias''']

						# The transform LN.
						UpperCAmelCase_      :   str      =	lm_head['''layernorm.weight''']
						UpperCAmelCase_      :   Optional[Any]      =	lm_head['''layernorm.bias''']

						# For the decoder, we replicate the weights.
						UpperCAmelCase_      :   List[Any]      =	word_embeddings
						UpperCAmelCase_      :   List[str]      =	lm_head['''bias''']

						# The classifier from Megatron (for MLNI).
						UpperCAmelCase_      :   str      =	model['''binary_head''']

						# Store the classifier.
						UpperCAmelCase_      :   Optional[int]      =	binary_head['''weight''']
						UpperCAmelCase_      :   Tuple      =	binary_head['''bias''']

						# It should be done!
						return output_state_dict





def 							lowerCamelCase_   (	)			->						int:
						# Create the argument parser.
						UpperCAmelCase_      :   List[Any]      =	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.''',      )
						UpperCAmelCase_      :   Any      =	parser.parse_args()

						# Extract the basename.
						UpperCAmelCase_      :   Optional[Any]      =	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:
																								UpperCAmelCase_      :   List[Any]      =	torch.load(SCREAMING_SNAKE_CASE__,      map_location='''cpu''' )
						else:
												UpperCAmelCase_      :   List[Any]      =	torch.load(args.path_to_checkpoint,      map_location='''cpu''' )

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

						# Convert.
						print('''Converting''' )
						UpperCAmelCase_      :   Optional[Any]      =	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.
						UpperCAmelCase_      :   List[str]      =	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()

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







"""simple docstring"""




__lowerCAmelCase							:  Any 				=		{
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 24,
    '''5''': 120,
    '''6''': 720,
    '''7''': 5040,
    '''8''': 40320,
    '''9''': 362880,
}

__lowerCAmelCase							:  List[Any] 				=		{145: 145}

__lowerCAmelCase							:  Any 				=		{
    145: 0,
    169: 3,
    36301: 3,
    1454: 3,
    871: 2,
    45361: 2,
    872: 2,
}

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if n in CACHE_SUM_DIGIT_FACTORIALS:
				return CACHE_SUM_DIGIT_FACTORIALS[n]
		a__   		=     sum(DIGIT_FACTORIALS[let] for let in str(UpperCamelCase    )    )
		a__   		=     ret
		return ret

def __snake_case (      UpperCamelCase						,					UpperCamelCase = None    )       ->    int:


		"""simple docstring"""





		a__   		=     previous or set()
		if n in CHAIN_LENGTH_CACHE:
				return CHAIN_LENGTH_CACHE[n]
		a__   		=     sum_digit_factorials(UpperCamelCase    )
		if next_number in previous:
				a__   		=     0
				return 0
		else:
				previous.add(UpperCamelCase    )
				a__   		=     1 + chain_length(UpperCamelCase						,					UpperCamelCase    )
				a__   		=     ret
				return ret

def __snake_case (      UpperCamelCase = 60						,					UpperCamelCase = 1_000_000    )       ->    int:


		"""simple docstring"""





		return sum(1 for i in range(1						,					UpperCamelCase    ) if chain_length(UpperCamelCase    ) == num_terms    )


if __name__ == "__main__":
					print(f"{solution() = }")




