




'''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 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_=0  ):



		'''simple docstring'''







		if name is None:
				_snake_case	       =       None
		else:
				_snake_case	       =       "." * max(0						,							spaces - 2  ) + "# {:" + str(50 - spaces  ) + "s}"
				_snake_case	       =       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 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       {}

		# old versions did not store training args
		_snake_case	       =       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))

				_snake_case	       =       ds_args.tokenizer_type
				_snake_case	       =       ds_args.padded_vocab_size
				_snake_case	       =       ds_args.max_position_embeddings
				_snake_case	       =       ds_args.hidden_size
				_snake_case	       =       ds_args.num_layers
				_snake_case	       =       ds_args.num_attention_heads
				_snake_case	       =       ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
				# pprint(config)

		# The number of heads.
		_snake_case	       =       config.num_attention_heads
		# The hidden_size per head.
		_snake_case	       =       config.hidden_size // heads
		# Megatron-LM checkpoint version
		if "checkpoint_version" in input_state_dict.keys():
				_snake_case	       =       input_state_dict["checkpoint_version"]
		else:
				_snake_case	       =       0.0

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

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

		# The position embeddings.
		_snake_case	       =       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.
		_snake_case	       =       pos_embeddings

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

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

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

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

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

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

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

				# The name of the layer.
				_snake_case	       =       f'''bert.encoder.layer.{layer_idx}'''

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

						_snake_case	       =       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.
						_snake_case	       =       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.
						_snake_case	       =       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
						_snake_case	       =       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
						_snake_case	       =       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

						# Clear the stored tensor.
						_snake_case	       =       None

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

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

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

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

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

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

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

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

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

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

		# It should be done!
		return output_state_dict






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       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."						,							)
		_snake_case	       =       parser.parse_args()

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

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

		# Convert.
		print("Converting"  )
		_snake_case	       =       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.
		_snake_case	       =       os.path.join(lowercase_						,							"pytorch_model.bin"  )
		print(f'''Saving checkpoint to "{output_checkpoint_file}"'''  )
		torch.save(lowercase_						,							lowercase_  )


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

if __name__ == "__main__":
						main()

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











'''simple docstring'''







from __future__ import annotations

import math
from itertools import permutations






def 		_lowercase		(	lowercase_  ):



		'''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(lowercase_  ) + 1  )						,							6  ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 		_lowercase		(	lowercase_ = 7  ):



		'''simple docstring'''







		_snake_case	       =       "".join(str(lowercase_  ) for i in range(1						,							n + 1  )  )
		_snake_case	       =       [int("".join(lowercase_  )  ) for i in permutations(lowercase_						,							lowercase_  )]
		_snake_case	       =       [num for num in perm_list if is_prime(lowercase_  )]
		return max(lowercase_  ) if pandigitals else 0


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





