





'''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__(		_UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Any=0				)->   Any:






					"""simple docstring"""

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

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



def  lowercase__(		_UpperCamelCase  : str ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : str				)->   Tuple:






					"""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(*_UpperCamelCase				)
										_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(*_UpperCamelCase				)
										_UpperCamelCase											=   param.transpose(0 ,   1				).contiguous()
					_UpperCamelCase											=   param.view(*_UpperCamelCase				)
					return param



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : Optional[int]				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   {}

					# old versions did not store training args
					_UpperCamelCase											=   input_state_dict.get("args" ,   _UpperCamelCase				)
					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(_UpperCamelCase				)

										# 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(_UpperCamelCase ,   _UpperCamelCase ,   3 ,   _UpperCamelCase ,   _UpperCamelCase				)
															# 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(_UpperCamelCase ,   _UpperCamelCase ,   3 ,   _UpperCamelCase ,   _UpperCamelCase				)
															# 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  lowercase__(		)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure" ,   action="store_true"				)
					parser.add_argument("path_to_checkpoint" ,   type=_UpperCamelCase ,   help="Path to the ZIP file containing the checkpoint"				)
					parser.add_argument(
					    "--config_file" ,   default="" ,   type=_UpperCamelCase ,   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(_UpperCamelCase ,   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(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)

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

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

					# Store the state_dict to file.
					_UpperCamelCase											=   os.path.join(_UpperCamelCase ,   "pytorch_model.bin"				)
					print(f"Saving checkpoint to \"{output_checkpoint_file}\""				)
					torch.save(_UpperCamelCase ,   _UpperCamelCase				)


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

if __name__ == "__main__":
			main()

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










'''simple docstring'''



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class     A_ (  nn.Linear  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								_UpperCamelCase											=   pruning_method

								if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
													_UpperCamelCase											=   mask_scale
													_UpperCamelCase											=   mask_init
													_UpperCamelCase											=   nn.Parameter(torch.empty(self.weight.size()   )   )
													self.init_mask()




			def       a	(					self   ):
								if self.mask_init == "constant":
													init.constant_(self.mask_scores			,     val=self.mask_scale   )
								elif self.mask_init == "uniform":
													init.uniform_(self.mask_scores			,     a=-self.mask_scale			,     b=self.mask_scale   )
								elif self.mask_init == "kaiming":
													init.kaiming_uniform_(self.mask_scores			,     a=math.sqrt(5   )   )




			def       a	(					self			,     A_			,     A_   ):
								# Get the mask
								if self.pruning_method == "topK":
													_UpperCamelCase											=   TopKBinarizer.apply(self.mask_scores			,     A_   )
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCamelCase											=   "sigmoied" in self.pruning_method
													_UpperCamelCase											=   ThresholdBinarizer.apply(self.mask_scores			,     A_			,     A_   )
								elif self.pruning_method == "magnitude":
													_UpperCamelCase											=   MagnitudeBinarizer.apply(self.weight			,     A_   )
								elif self.pruning_method == "l0":
													_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   -0.1, 1.1, 2 / 3
													if self.training:
																		_UpperCamelCase											=   torch.zeros_like(self.mask_scores   ).uniform_().clamp(0.0001			,     0.9999   )
																		_UpperCamelCase											=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b   )
													else:
																		_UpperCamelCase											=   torch.sigmoid(self.mask_scores   )
													_UpperCamelCase											=   s * (r - l) + l
													_UpperCamelCase											=   s_bar.clamp(min=0.0			,     max=1.0   )
								# Mask weights with computed mask
								_UpperCamelCase											=   mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(A_			,     A_			,     self.bias   )



