
"""simple docstring"""
def     __lowerCamelCase  (		UpperCamelCase__ = 400_0000      ):


	"""simple docstring"""






	_UpperCAmelCase =						1
	_UpperCAmelCase =						2
	_UpperCAmelCase =						0
	while j <= n:
		if j % 2 == 0:
			total += j
		_UpperCAmelCase ,      _UpperCAmelCase =						j, i + j

	return total


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






"""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  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__=0      ):


	"""simple docstring"""






	if name is None:
		_UpperCAmelCase =						None
	else:
		_UpperCAmelCase =						"." * max(0				,     spaces - 2      ) + "# {:" + str(50 - spaces      ) + "s}"
		_UpperCAmelCase =						fmt.format(UpperCamelCase__      )

	# Print and recurse (if needed).
	if isinstance(UpperCamelCase__				,     UpperCamelCase__      ):
		if msg is not None:
			print(UpperCamelCase__      )
		for k in val.keys():
			recursive_print(UpperCamelCase__				,     val[k]				,     spaces + 2      )
	elif isinstance(UpperCamelCase__				,     torch.Tensor      ):
		print(UpperCamelCase__				,     ":"				,     val.size()      )
	else:
		print(UpperCamelCase__				,     ":"				,     UpperCamelCase__      )




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						param.size()
	if checkpoint_version == 1.0:
		# version 1.0 stores [num_heads * hidden_size * num_splits, :]
		_UpperCAmelCase =						(num_heads, hidden_size, num_splits) + input_shape[1:]
		_UpperCAmelCase =						param.view(*UpperCamelCase__      )
		_UpperCAmelCase =						param.transpose(0				,     2      )
		_UpperCAmelCase =						param.transpose(1				,     2      ).contiguous()
	elif checkpoint_version >= 2.0:
		# other versions store [num_heads * num_splits * hidden_size, :]
		_UpperCAmelCase =						(num_heads, num_splits, hidden_size) + input_shape[1:]
		_UpperCAmelCase =						param.view(*UpperCamelCase__      )
		_UpperCAmelCase =						param.transpose(0				,     1      ).contiguous()
	_UpperCAmelCase =						param.view(*UpperCamelCase__      )
	return param




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						{}

	# old versions did not store training args
	_UpperCAmelCase =						input_state_dict.get("args"				,     UpperCamelCase__      )
	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 =						ds_args.tokenizer_type
		_UpperCAmelCase =						ds_args.padded_vocab_size
		_UpperCAmelCase =						ds_args.max_position_embeddings
		_UpperCAmelCase =						ds_args.hidden_size
		_UpperCAmelCase =						ds_args.num_layers
		_UpperCAmelCase =						ds_args.num_attention_heads
		_UpperCAmelCase =						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 =						config.num_attention_heads
	# The hidden_size per head.
	_UpperCAmelCase =						config.hidden_size // heads
	# Megatron-LM checkpoint version
	if "checkpoint_version" in input_state_dict.keys():
		_UpperCAmelCase =						input_state_dict["checkpoint_version"]
	else:
		_UpperCAmelCase =						0.0

	# The model.
	_UpperCAmelCase =						input_state_dict["model"]
	# The language model.
	_UpperCAmelCase =						model["language_model"]
	# The embeddings.
	_UpperCAmelCase =						lm["embedding"]

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

	# The position embeddings.
	_UpperCAmelCase =						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 =						pos_embeddings

	# The token-type embeddings.
	_UpperCAmelCase =						embeddings["tokentype_embeddings"]["weight"]
	# Store the position embeddings.
	_UpperCAmelCase =						tokentype_embeddings

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

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

	# The simple map of names for "automated" rules.
	_UpperCAmelCase =						{
	    "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 =						None

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

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

		# The index of the layer.
		_UpperCAmelCase =						int(m.group(1      )      )
		# The name of the operation.
		_UpperCAmelCase =						m.group(2      )
		# Is it a weight or a bias?
		_UpperCAmelCase =						m.group(3      )

		# The name of the layer.
		_UpperCAmelCase =						f"bert.encoder.layer.{layer_idx}"

		# For layernorm(s), simply store the layer norm.
		if op_name.endswith("layernorm"      ):
			_UpperCAmelCase =						"attention.ln" if op_name.startswith("input"      ) else "ln"
			_UpperCAmelCase =						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 =						fix_query_key_value_ordering(UpperCamelCase__				,     UpperCamelCase__				,     3				,     UpperCamelCase__				,     UpperCamelCase__      )
			# Store the tensor as we need the bias as well to interleave QKV and biases.
			_UpperCAmelCase =						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 =						attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
			_UpperCAmelCase =						attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
			_UpperCAmelCase =						attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

			_UpperCAmelCase =						fix_query_key_value_ordering(UpperCamelCase__				,     UpperCamelCase__				,     3				,     UpperCamelCase__				,     UpperCamelCase__      )
			# Split the bias.
			_UpperCAmelCase =						out_val[0 * config.hidden_size : 1 * config.hidden_size]
			_UpperCAmelCase =						out_val[1 * config.hidden_size : 2 * config.hidden_size]
			_UpperCAmelCase =						out_val[2 * config.hidden_size : 3 * config.hidden_size]

			# Store.
			_UpperCAmelCase =						q
			_UpperCAmelCase =						q_bias
			_UpperCAmelCase =						k
			_UpperCAmelCase =						k_bias
			_UpperCAmelCase =						v
			_UpperCAmelCase =						v_bias

			# Clear the stored tensor.
			_UpperCAmelCase =						None

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

    # The final layernorm.
	_UpperCAmelCase =						transformer["final_layernorm.weight"]
	_UpperCAmelCase =						transformer["final_layernorm.bias"]

	# The pooler.
	_UpperCAmelCase =						lm["pooler"]

	# Store the matrix and the bias.
	_UpperCAmelCase =						pooler["dense.weight"]
	_UpperCAmelCase =						pooler["dense.bias"]

	# The LM head from Megatron (for RACE).
	_UpperCAmelCase =						model["lm_head"]

	# The transform matrix.
	_UpperCAmelCase =						lm_head["dense.weight"]
	_UpperCAmelCase =						lm_head["dense.bias"]

	# The transform LN.
	_UpperCAmelCase =						lm_head["layernorm.weight"]
	_UpperCAmelCase =						lm_head["layernorm.bias"]

	# For the decoder, we replicate the weights.
	_UpperCAmelCase =						word_embeddings
	_UpperCAmelCase =						lm_head["bias"]

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

	# Store the classifier.
	_UpperCAmelCase =						binary_head["weight"]
	_UpperCAmelCase =						binary_head["bias"]

	# It should be done!
	return output_state_dict




def     __lowerCamelCase  (		):


	"""simple docstring"""






	_UpperCAmelCase =						argparse.ArgumentParser()
	parser.add_argument("--print-checkpoint-structure"				,     action="store_true"      )
	parser.add_argument("path_to_checkpoint"				,     type=UpperCamelCase__				,     help="Path to the ZIP file containing the checkpoint"      )
	parser.add_argument(
	    "--config_file"				,     default=""				,     type=UpperCamelCase__				,     help="An optional config json file describing the pre-trained model."				,     )
	_UpperCAmelCase =						parser.parse_args()

	# Extract the basename.
	_UpperCAmelCase =						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 =						torch.load(UpperCamelCase__				,     map_location="cpu"      )
	else:
		_UpperCAmelCase =						torch.load(args.path_to_checkpoint				,     map_location="cpu"      )

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

	# Convert.
	print("Converting"      )
	_UpperCAmelCase =						convert_megatron_checkpoint(UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      )

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

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

	# Store the state_dict to file.
	_UpperCAmelCase =						os.path.join(UpperCamelCase__				,     "pytorch_model.bin"      )
	print(f"Saving checkpoint to \"{output_checkpoint_file}\""      )
	torch.save(UpperCamelCase__				,     UpperCamelCase__      )


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

if __name__ == "__main__":
			main()

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




