


"""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  (   lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   Tuple    ,       lowerCamelCase_	:   int=0	):
				# Format the message.
				if name is None:
								SCREAMING_SNAKE_CASE__  				=					None
				else:
								SCREAMING_SNAKE_CASE__  				=					"." * max(0    ,       spaces - 2	) + "# {:" + str(50 - spaces	) + "s}"
								SCREAMING_SNAKE_CASE__  				=					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 __lowercase  (   lowerCamelCase_	:   Any    ,       lowerCamelCase_	:   List[str]    ,       lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   Union[str, Any]	):
				# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
				# for compatibility with later versions of NVIDIA Megatron-LM.
				# The inverse operation is performed inside Megatron-LM to read checkpoints:
				# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
				# If param is the weight tensor of the self-attention block, the returned tensor
				# will have to be transposed one more time to be read by HuggingFace BERT.
				SCREAMING_SNAKE_CASE__  				=					param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								SCREAMING_SNAKE_CASE__  				=					(num_heads, hidden_size, num_splits) + input_shape[1:]
								SCREAMING_SNAKE_CASE__  				=					param.view(*lowerCamelCase_	)
								SCREAMING_SNAKE_CASE__  				=					param.transpose(0    ,       2	)
								SCREAMING_SNAKE_CASE__  				=					param.transpose(1    ,       2	).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								SCREAMING_SNAKE_CASE__  				=					(num_heads, num_splits, hidden_size) + input_shape[1:]
								SCREAMING_SNAKE_CASE__  				=					param.view(*lowerCamelCase_	)
								SCREAMING_SNAKE_CASE__  				=					param.transpose(0    ,       1	).contiguous()
				SCREAMING_SNAKE_CASE__  				=					param.view(*lowerCamelCase_	)
				return param


def __lowercase  (   lowerCamelCase_	:   Any    ,       lowerCamelCase_	:   Union[str, Any]    ,       lowerCamelCase_	:   List[Any]	):
				# The converted output model.
				SCREAMING_SNAKE_CASE__  				=					{}

				# old versions did not store training args
				SCREAMING_SNAKE_CASE__  				=					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__  				=					ds_args.tokenizer_type
								SCREAMING_SNAKE_CASE__  				=					ds_args.padded_vocab_size
								SCREAMING_SNAKE_CASE__  				=					ds_args.max_position_embeddings
								SCREAMING_SNAKE_CASE__  				=					ds_args.hidden_size
								SCREAMING_SNAKE_CASE__  				=					ds_args.num_layers
								SCREAMING_SNAKE_CASE__  				=					ds_args.num_attention_heads
								SCREAMING_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.
				SCREAMING_SNAKE_CASE__  				=					config.num_attention_heads
				# The hidden_size per head.
				SCREAMING_SNAKE_CASE__  				=					config.hidden_size // heads
				# Megatron-LM checkpoint version
				if "checkpoint_version" in input_state_dict.keys():
								SCREAMING_SNAKE_CASE__  				=					input_state_dict["checkpoint_version"]
				else:
								SCREAMING_SNAKE_CASE__  				=					0.0

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

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

				# The position embeddings.
				SCREAMING_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.
				SCREAMING_SNAKE_CASE__  				=					pos_embeddings

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

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

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

				# The simple map of names for "automated" rules.
				SCREAMING_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.
				SCREAMING_SNAKE_CASE__  				=					None

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

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

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

								# The name of the layer.
								SCREAMING_SNAKE_CASE__  				=					F'''bert.encoder.layer.{layer_idx}'''

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

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

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

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

												# Clear the stored tensor.
												SCREAMING_SNAKE_CASE__  				=					None

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

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

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

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

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

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

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

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

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

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

				# It should be done!
				return output_state_dict


def __lowercase  (   ):
				# Create the argument parser.
				SCREAMING_SNAKE_CASE__  				=					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__  				=					parser.parse_args()

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

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

				# Convert.
				print("Converting"	)
				SCREAMING_SNAKE_CASE__  				=					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__  				=					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"""






class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self  ):
								SCREAMING_SNAKE_CASE__  				=					[0]
								SCREAMING_SNAKE_CASE__  				=					0



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					self.__heap[i]
								while i // 2 > 0:
												if self.__heap[i] > self.__heap[i // 2]:
																SCREAMING_SNAKE_CASE__  				=					self.__heap[i // 2]
																SCREAMING_SNAKE_CASE__  				=					temporary
												i //= 2



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								self.__heap.append(UpperCAmelCase__  )
								self.__size += 1
								self.__swap_up(self.__size  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								while self.__size >= 2 * i:
												if 2 * i + 1 > self.__size:
																SCREAMING_SNAKE_CASE__  				=					2 * i
												else:
																if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																				SCREAMING_SNAKE_CASE__  				=					2 * i
																else:
																				SCREAMING_SNAKE_CASE__  				=					2 * i + 1
												SCREAMING_SNAKE_CASE__  				=					self.__heap[i]
												if self.__heap[i] < self.__heap[bigger_child]:
																SCREAMING_SNAKE_CASE__  				=					self.__heap[bigger_child]
																SCREAMING_SNAKE_CASE__  				=					temporary
												SCREAMING_SNAKE_CASE__  				=					bigger_child



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.__heap[1]
								SCREAMING_SNAKE_CASE__  				=					self.__heap[self.__size]
								self.__size -= 1
								self.__heap.pop()
								self.__swap_down(1  )
								return max_value



				@property
				def 	lowerCAmelCase__		(    self  ):
								return self.__heap[1:]






				def __len__(    self  ):
								return self.__size


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# create an instance of BinaryHeap
	_lowerCamelCase  =	BinaryHeap()
	binary_heap.insert(6)
	binary_heap.insert(10)
	binary_heap.insert(15)
	binary_heap.insert(12)
	# pop root(max-values because it is max heap)
	print(binary_heap.pop())  # 15
	print(binary_heap.pop())  # 12
	# get the list and size after operations
	print(binary_heap.get_list)
	print(len(binary_heap))
