class 				UpperCAmelCase:



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__  :	int        =							data
								lowercase__  :	int
								lowercase__  :	Node

def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			None:
							lowercase__  :	Tuple        =							0
							lowercase__  :	Dict        =							x
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			None:
							lowercase__  :	Optional[Any]        =							find_set(SCREAMING_SNAKE_CASE_    ), find_set(SCREAMING_SNAKE_CASE_    )
							if x == y:
														return

							elif x.rank > y.rank:
														lowercase__  :	Union[str, Any]        =							x
							else:
														lowercase__  :	Tuple        =							y
														if x.rank == y.rank:
																					y.rank += 1
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			Node:
							if x != x.parent:
														lowercase__  :	Tuple        =							find_set(x.parent    )
							return x.parent
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			set:
							lowercase__  :	str        =							({0, 1, 2}, {3, 4, 5})
							for s in sets:
														if node.data in s:
																					return s
							lowercase__  :	List[str]        =							F"""{node.data} is not in {sets}"""
							raise ValueError(SCREAMING_SNAKE_CASE_    )
def  snake_case_					(		)      ->			None:
							lowercase__  :	Tuple        =							[Node(SCREAMING_SNAKE_CASE_    ) for i in range(6    )]
							for v in vertex:
														make_set(SCREAMING_SNAKE_CASE_    )

							union_set(vertex[0]    ,vertex[1]    )
							union_set(vertex[1]    ,vertex[2]    )
							union_set(vertex[3]    ,vertex[4]    )
							union_set(vertex[3]    ,vertex[5]    )

							for nodea in vertex:
														for nodea in vertex:
																					if find_python_set(SCREAMING_SNAKE_CASE_    ).isdisjoint(find_python_set(SCREAMING_SNAKE_CASE_    )    ):
																												assert find_set(SCREAMING_SNAKE_CASE_    ) != find_set(SCREAMING_SNAKE_CASE_    )
																					else:
																												assert find_set(SCREAMING_SNAKE_CASE_    ) == find_set(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
						test_disjoint_set()





# 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  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=0    )      ->			Any:
							# Format the message.
							if name is None:
														lowercase__  :	Any        =							None
							else:
														lowercase__  :	int        =							"." * max(0    ,spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
														lowercase__  :	str        =							fmt.format(SCREAMING_SNAKE_CASE_    )

							# Print and recurse (if needed).
							if isinstance(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ):
														if msg is not None:
																					print(SCREAMING_SNAKE_CASE_    )
														for k in val.keys():
																					recursive_print(SCREAMING_SNAKE_CASE_    ,val[k]    ,spaces + 2    )
							elif isinstance(SCREAMING_SNAKE_CASE_    ,torch.Tensor    ):
														print(SCREAMING_SNAKE_CASE_    ,":"    ,val.size()    )
							else:
														print(SCREAMING_SNAKE_CASE_    ,":"    ,SCREAMING_SNAKE_CASE_    )
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Dict:
							# 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.
							lowercase__  :	Optional[Any]        =							param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														lowercase__  :	List[str]        =							(num_heads, hidden_size, num_splits) + input_shape[1:]
														lowercase__  :	List[Any]        =							param.view(*SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Optional[Any]        =							param.transpose(0    ,2    )
														lowercase__  :	List[str]        =							param.transpose(1    ,2    ).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														lowercase__  :	Any        =							(num_heads, num_splits, hidden_size) + input_shape[1:]
														lowercase__  :	str        =							param.view(*SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Any        =							param.transpose(0    ,1    ).contiguous()
							lowercase__  :	List[Any]        =							param.view(*SCREAMING_SNAKE_CASE_    )
							return param
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Optional[int]:
							# The converted output model.
							lowercase__  :	List[str]        =							{}

							# old versions did not store training args
							lowercase__  :	List[str]        =							input_state_dict.get("args"    ,SCREAMING_SNAKE_CASE_    )
							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))

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

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

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

							# The position embeddings.
							lowercase__  :	Optional[Any]        =							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.
							lowercase__  :	Tuple        =							pos_embeddings

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

							# The transformer.
							lowercase__  :	Optional[int]        =							lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

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

							# The simple map of names for "automated" rules.
							lowercase__  :	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.
							lowercase__  :	int        =							None

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														lowercase__  :	List[str]        =							layer_re.match(SCREAMING_SNAKE_CASE_    )

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

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

														# The name of the layer.
														lowercase__  :	Tuple        =							F"""bert.encoder.layer.{layer_idx}"""

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

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

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

																					# Store.
																					lowercase__  :	int        =							q
																					lowercase__  :	Optional[Any]        =							q_bias
																					lowercase__  :	List[str]        =							k
																					lowercase__  :	str        =							k_bias
																					lowercase__  :	List[str]        =							v
																					lowercase__  :	Optional[Any]        =							v_bias

																					# Clear the stored tensor.
																					lowercase__  :	Union[str, Any]        =							None

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

    # The final layernorm.
							lowercase__  :	str        =							transformer["final_layernorm.weight"]
							lowercase__  :	Dict        =							transformer["final_layernorm.bias"]

							# The pooler.
							lowercase__  :	Union[str, Any]        =							lm["pooler"]

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

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

							# The transform matrix.
							lowercase__  :	Optional[int]        =							lm_head["dense.weight"]
							lowercase__  :	Union[str, Any]        =							lm_head["dense.bias"]

							# The transform LN.
							lowercase__  :	int        =							lm_head["layernorm.weight"]
							lowercase__  :	Union[str, Any]        =							lm_head["layernorm.bias"]

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

							# The classifier from Megatron (for MLNI).
							lowercase__  :	Optional[Any]        =							model["binary_head"]

							# Store the classifier.
							lowercase__  :	Tuple        =							binary_head["weight"]
							lowercase__  :	Any        =							binary_head["bias"]

							# It should be done!
							return output_state_dict
def  snake_case_					(		)      ->			str:
							# Create the argument parser.
							lowercase__  :	Optional[Any]        =							argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure"    ,action="store_true"    )
							parser.add_argument("path_to_checkpoint"    ,type=SCREAMING_SNAKE_CASE_    ,help="Path to the ZIP file containing the checkpoint"    )
							parser.add_argument(
							    "--config_file"    ,default=""    ,type=SCREAMING_SNAKE_CASE_    ,help="An optional config json file describing the pre-trained model."    ,)
							lowercase__  :	List[str]        =							parser.parse_args()

							# Extract the basename.
							lowercase__  :	Optional[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:
																												lowercase__  :	Optional[Any]        =							torch.load(SCREAMING_SNAKE_CASE_    ,map_location="cpu"    )
							else:
														lowercase__  :	str        =							torch.load(args.path_to_checkpoint    ,map_location="cpu"    )

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

							# Convert.
							print("Converting"    )
							lowercase__  :	Optional[int]        =							convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

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

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

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


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

if __name__ == "__main__":
						main()

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