





# 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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__=0			)  ->    List[str]:







			'''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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Union[str, Any]:







			'''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 						__SCREAMING_SNAKE_CASE  (    )  ->    List[str]:







			'''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()

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










import math





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    bool:







			'''simple docstring'''

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5      ,      int(math.sqrt(UpperCamelCase__			) + 1			)      ,      6			):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__ = 6008_5147_5143			)  ->    int:







			'''simple docstring'''

			try:
						UpperCAmelCase       		=    int(UpperCamelCase__			)
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.'''			)
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.'''			)
			UpperCAmelCase       		=    0
			if is_prime(UpperCamelCase__			):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(UpperCamelCase__			):
						return n
			for i in range(3      ,      int(math.sqrt(UpperCamelCase__			)			) + 1      ,      2			):
						if n % i == 0:
									if is_prime(n // i			):
												UpperCAmelCase       		=    n // i
												break
									elif is_prime(UpperCamelCase__			):
												UpperCAmelCase       		=    i
			return max_number


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



