



import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig
def      A      (			_A,     _A,     _A=0							):




					"""simple docstring"""
					# Format the message.
					if name is None:
										snake_case_						:Union[str, Any]								=						None
					else:
										snake_case_						:int								=						"." * max(0,     spaces - 2							) + "# {:" + str(50 - spaces							) + "s}"
										snake_case_						:Optional[int]								=						fmt.format(_A							)

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




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




					"""simple docstring"""
					snake_case_						:List[Any]								=						{}

					# old versions did not store training args
					snake_case_						:Union[str, Any]								=						input_state_dict.get("args",     _A							)
					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_						:Union[str, Any]								=						ds_args.tokenizer_type
										snake_case_						:List[Any]								=						ds_args.padded_vocab_size
										snake_case_						:str								=						ds_args.max_position_embeddings
										snake_case_						:Any								=						ds_args.hidden_size
										snake_case_						:str								=						ds_args.num_layers
										snake_case_						:List[str]								=						ds_args.num_attention_heads
										snake_case_						:Tuple								=						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_						:Union[str, Any]								=						config.num_attention_heads
					# The hidden_size per head.
					snake_case_						:int								=						config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										snake_case_						:Dict								=						input_state_dict["checkpoint_version"]
					else:
										snake_case_						:Optional[Any]								=						0.0

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

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

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

					# The token-type embeddings.
					snake_case_						:Tuple								=						embeddings["tokentype_embeddings"]["weight"]
					# Store the position embeddings.
					snake_case_						:List[Any]								=						tokentype_embeddings

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

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

					# The simple map of names for "automated" rules.
					snake_case_						:Union[str, Any]								=						{
					    "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_						:Optional[int]								=						None

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

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

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

										# The name of the layer.
										snake_case_						:Optional[Any]								=						F'''bert.encoder.layer.{layer_idx}'''

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

															snake_case_						:int								=						fix_query_key_value_ordering(_A,     _A,     3,     _A,     _A							)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															snake_case_						:str								=						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_						:Optional[int]								=						attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															snake_case_						:str								=						attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															snake_case_						:Any								=						attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

															# Store.
															snake_case_						:Any								=						q
															snake_case_						:int								=						q_bias
															snake_case_						:Dict								=						k
															snake_case_						:Optional[Any]								=						k_bias
															snake_case_						:Union[str, Any]								=						v
															snake_case_						:List[Any]								=						v_bias

															# Clear the stored tensor.
															snake_case_						:int								=						None

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

    # The final layernorm.
					snake_case_						:List[str]								=						transformer["final_layernorm.weight"]
					snake_case_						:int								=						transformer["final_layernorm.bias"]

					# The pooler.
					snake_case_						:List[str]								=						lm["pooler"]

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

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

					# The transform matrix.
					snake_case_						:Any								=						lm_head["dense.weight"]
					snake_case_						:Optional[int]								=						lm_head["dense.bias"]

					# The transform LN.
					snake_case_						:Optional[Any]								=						lm_head["layernorm.weight"]
					snake_case_						:Any								=						lm_head["layernorm.bias"]

					# For the decoder, we replicate the weights.
					snake_case_						:Optional[int]								=						word_embeddings
					snake_case_						:List[Any]								=						lm_head["bias"]

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

					# Store the classifier.
					snake_case_						:List[Any]								=						binary_head["weight"]
					snake_case_						:Optional[Any]								=						binary_head["bias"]

					# It should be done!
					return output_state_dict
def      A      (			):




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

					# Extract the basename.
					snake_case_						:List[str]								=						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_						:Optional[Any]								=						torch.load(_A,     map_location="cpu"							)
					else:
										snake_case_						:Optional[int]								=						torch.load(args.path_to_checkpoint,     map_location="cpu"							)

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

					# Convert.
					print("Converting"							)
					snake_case_						:List[Any]								=						convert_megatron_checkpoint(_A,     _A,     _A							)

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

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

					# Store the state_dict to file.
					snake_case_						:Tuple								=						os.path.join(_A,     "pytorch_model.bin"							)
					print(F'''Saving checkpoint to "{output_checkpoint_file}"'''							)
					torch.save(_A,     _A							)


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

if __name__ == "__main__":
				main()

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






"""simple docstring"""






def      A      (			_A,     _A							):




					"""simple docstring"""
					if n == 0:
										return 1

					elif n % 2 == 1:
										return binary_exponentiation(_A,     n - 1							) * a

					else:
										snake_case_						:Optional[int]								=						binary_exponentiation(_A,     n / 2							)
										return b * b


if __name__ == "__main__":
				try:
								__UpperCAmelCase      :      str   					=					int(input('Enter Base : ').strip())
								__UpperCAmelCase      :      Any   					=					int(input('Enter Power : ').strip())
				except ValueError:
								print('Invalid literal for integer')

				__UpperCAmelCase      :      Optional[int]   					=					binary_exponentiation(BASE, POWER)
				print(F'''{BASE}^({POWER}) : {RESULT}''')

