# 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			(					lowerCamelCase_:  List[str]				,	lowerCamelCase_:  Tuple				,	lowerCamelCase_:  List[Any]=0		):



				'''simple docstring'''







				if name is None:
								A     :  Dict   =				None
				else:
								A     :  Any   =				'''.''' * max(0				,	spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
								A     :  Optional[Any]   =				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 							_lowerCamelCase			(					lowerCamelCase_:  List[str]				,	lowerCamelCase_:  Optional[int]				,	lowerCamelCase_:  List[str]				,	lowerCamelCase_:  Any				,	lowerCamelCase_:  Optional[Any]		):



				'''simple docstring'''







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





def 							_lowerCamelCase			(					lowerCamelCase_:  List[Any]				,	lowerCamelCase_:  int				,	lowerCamelCase_:  Union[str, Any]		):



				'''simple docstring'''







				A     :  Union[str, Any]   =				{}

				# old versions did not store training args
				A     :  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))

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

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

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

				# The position embeddings.
				A     :  Tuple   =				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.
				A     :  Tuple   =				pos_embeddings

				# The token-type embeddings.
				A     :  Optional[Any]   =				embeddings['''tokentype_embeddings''']['''weight''']
				# Store the position embeddings.
				A     :  Optional[int]   =				tokentype_embeddings

				# The transformer.
				A     :  Dict   =				lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

				# The simple map of names for "automated" rules.
				A     :  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.
				A     :  Optional[Any]   =				None

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

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

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

								# The name of the layer.
								A     :  Optional[Any]   =				f"""bert.encoder.layer.{layer_idx}"""

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

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

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

												# Store.
												A     :  str   =				q
												A     :  Union[str, Any]   =				q_bias
												A     :  str   =				k
												A     :  Union[str, Any]   =				k_bias
												A     :  Tuple   =				v
												A     :  Optional[int]   =				v_bias

												# Clear the stored tensor.
												A     :  int   =				None

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

    # The final layernorm.
				A     :  Optional[int]   =				transformer['''final_layernorm.weight''']
				A     :  List[str]   =				transformer['''final_layernorm.bias''']

				# The pooler.
				A     :  Optional[int]   =				lm['''pooler''']

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

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

				# The transform matrix.
				A     :  int   =				lm_head['''dense.weight''']
				A     :  Any   =				lm_head['''dense.bias''']

				# The transform LN.
				A     :  List[Any]   =				lm_head['''layernorm.weight''']
				A     :  Tuple   =				lm_head['''layernorm.bias''']

				# For the decoder, we replicate the weights.
				A     :  Tuple   =				word_embeddings
				A     :  int   =				lm_head['''bias''']

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

				# Store the classifier.
				A     :  Optional[Any]   =				binary_head['''weight''']
				A     :  List[Any]   =				binary_head['''bias''']

				# It should be done!
				return output_state_dict





def 							_lowerCamelCase			(					):



				'''simple docstring'''







				A     :  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.'''				,	)
				A     :  Dict   =				parser.parse_args()

				# Extract the basename.
				A     :  List[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:
																A     :  Tuple   =				torch.load(lowercase__				,	map_location='''cpu'''		)
				else:
								A     :  Dict   =				torch.load(args.path_to_checkpoint				,	map_location='''cpu'''		)

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

				# Convert.
				print('''Converting'''		)
				A     :  Union[str, 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.
				A     :  Tuple   =				os.path.join(lowercase__				,	'''pytorch_model.bin'''		)
				print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""		)
				torch.save(lowercase__				,	lowercase__		)


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

if __name__ == "__main__":
					main()

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

def 							_lowerCamelCase			(					lowerCamelCase_:  list		):



				'''simple docstring'''







				A     :  Optional[int]   =				1.3
				A     :  List[str]   =				len(lowerCamelCase_		)
				A     :  Optional[Any]   =				False

				while not completed:
								# Update the gap value for a next comb
								A     :  str   =				int(gap / shrink_factor		)
								if gap <= 1:
												A     :  Tuple   =				True

								A     :  List[Any]   =				0
								while index + gap < len(lowerCamelCase_		):
												if data[index] > data[index + gap]:
																# Swap values
																A ,       A     :  int   =				data[index + gap], data[index]
																A     :  Dict   =				False
												index += 1

				return data


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase_  =	input("Enter numbers separated by a comma:\n").strip()
					UpperCamelCase_  =	[int(item) for item in user_input.split(",")]
					print(comb_sort(unsorted))