





'''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 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_=0					):


		"""simple docstring"""



		if name is None:
				SCREAMING_SNAKE_CASE				:							Any						=	None
		else:
				SCREAMING_SNAKE_CASE				:							Dict						=	""".""" * max(0	,	spaces - 2					) + """# {:""" + str(50 - spaces					) + """s}"""
				SCREAMING_SNAKE_CASE				:							Optional[int]						=	fmt.format(lowerCamelCase_					)

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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[Any]						=	{}

		# old versions did not store training args
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	input_state_dict.get("""args"""	,	lowerCamelCase_					)
		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))

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

		# The model.
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	input_state_dict["""model"""]
		# The language model.
		SCREAMING_SNAKE_CASE				:							str						=	model["""language_model"""]
		# The embeddings.
		SCREAMING_SNAKE_CASE				:							Any						=	lm["""embedding"""]

		# The word embeddings.
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	embeddings["""word_embeddings"""]["""weight"""]
		# Truncate the embedding table to vocab_size rows.
		SCREAMING_SNAKE_CASE				:							Tuple						=	word_embeddings[: config.vocab_size, :]
		# Store the word embeddings.
		SCREAMING_SNAKE_CASE				:							str						=	word_embeddings

		# The position embeddings.
		SCREAMING_SNAKE_CASE				:							List[str]						=	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.
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	pos_embeddings

		# The token-type embeddings.
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	embeddings["""tokentype_embeddings"""]["""weight"""]
		# Store the position embeddings.
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	tokentype_embeddings

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

		# The regex to extract layer names.
		SCREAMING_SNAKE_CASE				:							List[Any]						=	re.compile(R"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"""					)

		# The simple map of names for "automated" rules.
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	{
		    """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.
		SCREAMING_SNAKE_CASE				:							int						=	None

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

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

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

				# The name of the layer.
				SCREAMING_SNAKE_CASE				:							Any						=	f'''bert.encoder.layer.{layer_idx}'''

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

						SCREAMING_SNAKE_CASE				:							Tuple						=	fix_query_key_value_ordering(lowerCamelCase_	,	lowerCamelCase_	,	3	,	lowerCamelCase_	,	lowerCamelCase_					)
						# Store the tensor as we need the bias as well to interleave QKV and biases.
						SCREAMING_SNAKE_CASE				:							Optional[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.
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
						SCREAMING_SNAKE_CASE				:							Dict						=	attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

						SCREAMING_SNAKE_CASE				:							Any						=	fix_query_key_value_ordering(lowerCamelCase_	,	lowerCamelCase_	,	3	,	lowerCamelCase_	,	lowerCamelCase_					)
						# Split the bias.
						SCREAMING_SNAKE_CASE				:							List[str]						=	out_val[0 * config.hidden_size : 1 * config.hidden_size]
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	out_val[1 * config.hidden_size : 2 * config.hidden_size]
						SCREAMING_SNAKE_CASE				:							str						=	out_val[2 * config.hidden_size : 3 * config.hidden_size]

						# Store.
						SCREAMING_SNAKE_CASE				:							List[str]						=	q
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	q_bias
						SCREAMING_SNAKE_CASE				:							List[Any]						=	k
						SCREAMING_SNAKE_CASE				:							str						=	k_bias
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	v
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	v_bias

						# Clear the stored tensor.
						SCREAMING_SNAKE_CASE				:							List[str]						=	None

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

    # The final layernorm.
		SCREAMING_SNAKE_CASE				:							int						=	transformer["""final_layernorm.weight"""]
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	transformer["""final_layernorm.bias"""]

		# The pooler.
		SCREAMING_SNAKE_CASE				:							List[Any]						=	lm["""pooler"""]

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

		# The LM head from Megatron (for RACE).
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	model["""lm_head"""]

		# The transform matrix.
		SCREAMING_SNAKE_CASE				:							Tuple						=	lm_head["""dense.weight"""]
		SCREAMING_SNAKE_CASE				:							Tuple						=	lm_head["""dense.bias"""]

		# The transform LN.
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	lm_head["""layernorm.weight"""]
		SCREAMING_SNAKE_CASE				:							int						=	lm_head["""layernorm.bias"""]

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

		# The classifier from Megatron (for MLNI).
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model["""binary_head"""]

		# Store the classifier.
		SCREAMING_SNAKE_CASE				:							str						=	binary_head["""weight"""]
		SCREAMING_SNAKE_CASE				:							List[str]						=	binary_head["""bias"""]

		# It should be done!
		return output_state_dict



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	argparse.ArgumentParser()
		parser.add_argument("""--print-checkpoint-structure"""	,	action="""store_true"""					)
		parser.add_argument("""path_to_checkpoint"""	,	type=lowerCamelCase_	,	help="""Path to the ZIP file containing the checkpoint"""					)
		parser.add_argument(
		    """--config_file"""	,	default=""""""	,	type=lowerCamelCase_	,	help="""An optional config json file describing the pre-trained model."""	,	)
		SCREAMING_SNAKE_CASE				:							Dict						=	parser.parse_args()

		# Extract the basename.
		SCREAMING_SNAKE_CASE				:							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:
								SCREAMING_SNAKE_CASE				:							Any						=	torch.load(lowerCamelCase_	,	map_location="""cpu"""					)
		else:
				SCREAMING_SNAKE_CASE				:							Any						=	torch.load(args.path_to_checkpoint	,	map_location="""cpu"""					)

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

		# Convert.
		print("""Converting"""					)
		SCREAMING_SNAKE_CASE				:							int						=	convert_megatron_checkpoint(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					)

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

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

		# Store the state_dict to file.
		SCREAMING_SNAKE_CASE				:							List[Any]						=	os.path.join(lowerCamelCase_	,	"""pytorch_model.bin"""					)
		print(f'''Saving checkpoint to "{output_checkpoint_file}"'''					)
		torch.save(lowerCamelCase_	,	lowerCamelCase_					)


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

if __name__ == "__main__":
							main()

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










'''simple docstring'''




from __future__ import annotations

from collections import Counter



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							int						=	[True] * n
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	True

		for i in range(3	,	int(n**0.5 + 1					)	,	2					):
				SCREAMING_SNAKE_CASE				:							List[Any]						=	i * 2
				while index < n:
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	False
						SCREAMING_SNAKE_CASE				:							Optional[Any]						=	index + i

		SCREAMING_SNAKE_CASE				:							Dict						=	[2]

		for i in range(3	,	lowerCamelCase_	,	2					):
				if is_prime[i]:
						primes.append(lowerCamelCase_					)

		return primes



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	str(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							List[str]						=	[]
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	["""0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9"""]

		for duplicate in Counter(lowerCamelCase_					) - Counter(set(lowerCamelCase_					)					):
				SCREAMING_SNAKE_CASE				:							Tuple						=	[int(number_str.replace(lowerCamelCase_	,	lowerCamelCase_					)					) for digit in digits]
				replacements.append(lowerCamelCase_					)

		return replacements



def 						__A						(       lowerCamelCase_ = 8					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	set()

		# Filter primes with less than 3 replaceable digits
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
		    x for x in set(prime_sieve(1_00_00_00					)					) if len(str(lowerCamelCase_					)					) - len(set(str(lowerCamelCase_					)					)					) >= 3
		}

		for prime in primes:
				if prime in numbers_checked:
						continue

				SCREAMING_SNAKE_CASE				:							int						=	digit_replacements(lowerCamelCase_					)

				for family in replacements:
						numbers_checked.update(lowerCamelCase_					)
						SCREAMING_SNAKE_CASE				:							Dict						=	primes.intersection(lowerCamelCase_					)

						if len(lowerCamelCase_					) != family_length:
								continue

						return min(lowerCamelCase_					)

		return -1


if __name__ == "__main__":
							print(solution())



