code: 
  
  
  
  # 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_     :      Tuple    ,lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      Tuple=0       )   ->				str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if name is None:
  												UpperCAmelCase_=     None
  						else:
  												UpperCAmelCase_=     """.""" * max(0    ,spaces - 2       ) + """# {:""" + str(50 - spaces       ) + """s}"""
  												UpperCAmelCase_=     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_     :      Union[str, Any]    ,lowerCAmelCase_     :      Any    ,lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      Union[str, Any]       )   ->				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(*__lowerCamelCase       )
  												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(*__lowerCamelCase       )
  												UpperCAmelCase_=     param.transpose(0    ,1       ).contiguous()
  						UpperCAmelCase_=     param.view(*__lowerCamelCase       )
  						return param
  
  
  
  def 		__a  (					lowerCAmelCase_     :      Any    ,lowerCAmelCase_     :      List[Any]    ,lowerCAmelCase_     :      Any       )   ->				int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     {}
  
  						# old versions did not store training args
  						UpperCAmelCase_=     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))
  
  												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(__lowerCamelCase       )
  
  												# 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(__lowerCamelCase    ,__lowerCamelCase    ,3    ,__lowerCamelCase    ,__lowerCamelCase       )
  																		# 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(__lowerCamelCase    ,__lowerCamelCase    ,3    ,__lowerCamelCase    ,__lowerCamelCase       )
  																		# 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 		__a  (					)   ->				Union[str, Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     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."""    ,)
  						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(__lowerCamelCase    ,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(__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.
  						UpperCAmelCase_=     os.path.join(__lowerCamelCase    ,"""pytorch_model.bin"""       )
  						print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""       )
  						torch.save(__lowerCamelCase    ,__lowerCamelCase       )
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
  	main()
  
  ####################################################################################################
  
  
  
  
  
style_context: 
  
  
  
  from __future__ import annotations
  
  
  
  def  __a  (					lowerCAmelCase_     :      int       )   ->				int | None:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						for r in range(1    ,lowerCAmelCase_       ):
  												UpperCAmelCase_=     []
  												for x in range(n - 1       ):
  																		UpperCAmelCase_=     pow(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )
  																		if val in li:
  																								break
  																		li.append(lowerCAmelCase_       )
  												else:
  																		return r
  						return None
  
  
  if __name__ == "__main__":
  	__A       					=		int(input('''Enter a prime number q: '''))
  	__A       					=		find_primitive(q)
  	if a is None:
  		print(f'Cannot find the primitive for the value: {a!r}')
  	else:
  		__A       					=		int(input('''Enter private key of A: '''))
  		__A       					=		pow(a, a_private, q)
  		__A       					=		int(input('''Enter private key of B: '''))
  		__A       					=		pow(a, b_private, q)
  
  		__A       					=		pow(b_public, a_private, q)
  		__A       					=		pow(a_public, b_private, q)
  
  		print('''The key value generated by A is: ''', a_secret)
  		print('''The key value generated by B is: ''', b_secret)
  
  
  
  
  