



# 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     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase=0      )			->			int:



						'''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 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			List[Any]:



						'''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 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			Optional[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 							A     (						)			->			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()

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










import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase_      			=  16
UpperCamelCase_      			=  32







def 							A     (						__UpperCAmelCase      )			->			Optional[int]:



						'''simple docstring'''






						return int(x / 2**20      )







class 				a_    :

						def __enter__(       self      :Any)						->   Optional[Any]:
												gc.collect()
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
												UpperCAmelCase_					=	torch.cuda.memory_allocated()
												UpperCAmelCase_					=	psutil.Process()

												UpperCAmelCase_					=	self.cpu_mem_used()
												UpperCAmelCase_					=	True
												UpperCAmelCase_					=	threading.Thread(target=self.peak_monitor_func)
												UpperCAmelCase_					=	True
												peak_monitor_thread.start()
												return self

						def       __a			(       self      :Tuple)						->   int:
												return self.process.memory_info().rss

						def       __a			(       self      :str)						->   Dict:
												UpperCAmelCase_					=	-1

												while True:
																		UpperCAmelCase_					=	max(self.cpu_mem_used()  ,		self.cpu_peak)

																		# can't sleep or will not catch the peak right (this comment is here on purpose)
																		# time.sleep(0.001) # 1msec

																		if not self.peak_monitoring:
																								break




						def __exit__(       self      :Optional[Any]  ,		*_lowercase      :Optional[Any])						->   List[str]:
												UpperCAmelCase_					=	False

												gc.collect()
												torch.cuda.empty_cache()
												UpperCAmelCase_					=	torch.cuda.memory_allocated()
												UpperCAmelCase_					=	torch.cuda.max_memory_allocated()
												UpperCAmelCase_					=	bamb(self.end - self.begin)
												UpperCAmelCase_					=	bamb(self.peak - self.begin)

												UpperCAmelCase_					=	self.cpu_mem_used()
												UpperCAmelCase_					=	bamb(self.cpu_end - self.cpu_begin)
												UpperCAmelCase_					=	bamb(self.cpu_peak - self.cpu_begin)
												# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							UpperCamelCase_      			=  mocked_dataloaders  # noqa: F811







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,   __UpperCAmelCase      ) == "1":
												UpperCAmelCase_					=	2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						UpperCAmelCase_					=	FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=__UpperCAmelCase				,   ranka_only=__UpperCAmelCase      )				,   optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__UpperCAmelCase				,   ranka_only=__UpperCAmelCase      )				,   )

						# Initialize accelerator
						if args.with_tracking:
												UpperCAmelCase_					=	Accelerator(
												    cpu=args.cpu				,   mixed_precision=args.mixed_precision				,   log_with='''wandb'''				,   project_dir=args.logging_dir				,   fsdp_plugin=__UpperCAmelCase				,   )
						else:
												UpperCAmelCase_					=	Accelerator(fsdp_plugin=__UpperCAmelCase      )
						accelerator.print(accelerator.distributed_type      )

						if hasattr(args.checkpointing_steps				,   '''isdigit'''      ):
												if args.checkpointing_steps == "epoch":
																		UpperCAmelCase_					=	args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		UpperCAmelCase_					=	int(args.checkpointing_steps      )
												else:
																		raise ValueError(
																		    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."      )
						else:
												UpperCAmelCase_					=	None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_					=	config['''lr''']
						UpperCAmelCase_					=	int(config['''num_epochs''']      )
						UpperCAmelCase_					=	int(config['''seed''']      )
						UpperCAmelCase_					=	int(config['''batch_size''']      )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												UpperCAmelCase_					=	vars(__UpperCAmelCase      )
												accelerator.init_trackers('''fsdp_glue_no_trainer'''				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	AutoTokenizer.from_pretrained(args.model_name_or_path      )
						UpperCAmelCase_					=	load_dataset('''glue'''				,   '''mrpc'''      )
						UpperCAmelCase_					=	evaluate.load('''glue'''				,   '''mrpc'''      )

						def tokenize_function(__UpperCAmelCase      ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_					=	tokenizer(examples['''sentence1''']				,   examples['''sentence2''']				,   truncation=__UpperCAmelCase				,   max_length=__UpperCAmelCase      )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												UpperCAmelCase_					=	datasets.map(
												    __UpperCAmelCase				,   batched=__UpperCAmelCase				,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,   )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						UpperCAmelCase_					=	tokenized_datasets.rename_column('''label'''				,   '''labels'''      )

						# If the batch size is too big we use gradient accumulation
						UpperCAmelCase_					=	1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCAmelCase_					=	batch_size // MAX_GPU_BATCH_SIZE
												UpperCAmelCase_					=	MAX_GPU_BATCH_SIZE

						def collate_fn(__UpperCAmelCase      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCAmelCase_					=	128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		UpperCAmelCase_					=	16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase_					=	8
												else:
																		UpperCAmelCase_					=	None

												return tokenizer.pad(
												    __UpperCAmelCase				,   padding='''longest'''				,   max_length=__UpperCAmelCase				,   pad_to_multiple_of=__UpperCAmelCase				,   return_tensors='''pt'''				,   )

						# Instantiate dataloaders.
						UpperCAmelCase_					=	DataLoader(
						    tokenized_datasets['''train''']				,   shuffle=__UpperCAmelCase				,   collate_fn=__UpperCAmelCase				,   batch_size=__UpperCAmelCase      )
						UpperCAmelCase_					=	DataLoader(
						    tokenized_datasets['''validation''']				,   shuffle=__UpperCAmelCase				,   collate_fn=__UpperCAmelCase				,   batch_size=__UpperCAmelCase      )

						set_seed(__UpperCAmelCase      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_					=	AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path				,   return_dict=__UpperCAmelCase				,   low_cpu_mem_usage=__UpperCAmelCase      )

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						UpperCAmelCase_					=	accelerator.prepare(__UpperCAmelCase      )
						accelerator.print(__UpperCAmelCase      )

						# Instantiate optimizer
						# New Code #
						# For FSDP feature, at present it doesn't support multiple parameter groups,
						# so we need to create a single parameter group for the whole model
						UpperCAmelCase_					=	torch.optim.AdamW(params=model.parameters()				,   lr=__UpperCAmelCase				,   weight_decay=2e-4      )

						# Instantiate scheduler
						UpperCAmelCase_					=	get_linear_schedule_with_warmup(
						    optimizer=__UpperCAmelCase				,   num_warmup_steps=10				,   num_training_steps=(len(__UpperCAmelCase      ) * num_epochs) // gradient_accumulation_steps				,   )

						# New Code #
						# For FSDP feature, prepare everything except the model as we have already prepared the model
						# before creating the optimizer
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	accelerator.prepare(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"      )
																		accelerator.load_state(args.resume_from_checkpoint      )
																		UpperCAmelCase_					=	os.path.basename(args.resume_from_checkpoint      )
												else:
																		# Get the most recent checkpoint
																		UpperCAmelCase_					=	[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime      )
																		UpperCAmelCase_					=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												UpperCAmelCase_					=	os.path.splitext(__UpperCAmelCase      )[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('''epoch_'''				,   ''''''      )      )
																		UpperCAmelCase_					=	None
												else:
																		UpperCAmelCase_					=	int(training_difference.replace('''step_'''				,   ''''''      )      )
																		num_epochs -= resume_step // len(__UpperCAmelCase      )
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		UpperCAmelCase_					=	(num_epochs * len(__UpperCAmelCase      )) - resume_step

    # Now we train the model
						for epoch in range(__UpperCAmelCase      ):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								UpperCAmelCase_					=	0
																		for step, batch in enumerate(__UpperCAmelCase      ):
																								# We need to skip steps until we reach the resumed step
																								if args.resume_from_checkpoint and epoch == 0:
																														if resume_step is not None and step < resume_step:
																																				pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device      )
																								UpperCAmelCase_					=	model(**__UpperCAmelCase      )
																								UpperCAmelCase_					=	outputs.loss
																								UpperCAmelCase_					=	loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(__UpperCAmelCase      )
																								if step % gradient_accumulation_steps == 0:
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# accelerator.print(lr_scheduler.get_lr())

																								overall_step += 1

																								if isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ):
																														UpperCAmelCase_					=	f"step_{overall_step}"
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																																				accelerator.save_state(__UpperCAmelCase      )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin      )      )      )
												accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used      )      )
												accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked      )      )
												accelerator.print(
												    '''Total Peak Memory consumed during the train (max): {}'''.format(
												        tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin      ),
																		    }				,   step=__UpperCAmelCase				,   )

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		model.eval()
																		for step, batch in enumerate(__UpperCAmelCase      ):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device      )
																								with torch.no_grad():
																														UpperCAmelCase_					=	model(**__UpperCAmelCase      )
																								UpperCAmelCase_					=	outputs.logits.argmax(dim=-1      )
																								UpperCAmelCase_				, UpperCAmelCase_					=	accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
																								metric.add_batch(
																								    predictions=__UpperCAmelCase				,   references=__UpperCAmelCase				,   )

																		UpperCAmelCase_					=	metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f"epoch {epoch}:"				,   __UpperCAmelCase      )
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        '''accuracy''': eval_metric['''accuracy'''],
																								        '''f1''': eval_metric['''f1'''],
																								        '''train_loss''': total_loss.item() / len(__UpperCAmelCase      ),
																								    }				,   step=__UpperCAmelCase				,   )

																		if checkpointing_steps == "epoch":
																								UpperCAmelCase_					=	f"epoch_{epoch}"
																								if args.output_dir is not None:
																														UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																								accelerator.save_state(__UpperCAmelCase      )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin      )      )      )
												accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used      )      )
												accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked      )      )
												accelerator.print(
												    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin      ),
																		    }				,   step=__UpperCAmelCase				,   )

						if args.with_tracking:
												accelerator.end_training()







def 							A     (						)			->			Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	argparse.ArgumentParser(description='''Simple example of training script.'''      )
						parser.add_argument(
						    '''--mixed_precision'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,   help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''				,   )
						parser.add_argument('''--cpu'''				,   action='''store_true'''				,   help='''If passed, will train on the CPU.'''      )
						parser.add_argument(
						    '''--checkpointing_steps'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''				,   )
						parser.add_argument(
						    '''--resume_from_checkpoint'''				,   type=__UpperCAmelCase				,   default=__UpperCAmelCase				,   help='''If the training should continue from a checkpoint folder.'''				,   )
						parser.add_argument(
						    '''--with_tracking'''				,   action='''store_true'''				,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''				,   )
						parser.add_argument(
						    '''--output_dir'''				,   type=__UpperCAmelCase				,   default='''.'''				,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,   )
						parser.add_argument(
						    '''--logging_dir'''				,   type=__UpperCAmelCase				,   default='''logs'''				,   help='''Location on where to store experiment tracking logs`'''				,   )
						parser.add_argument(
						    '''--model_name_or_path'''				,   type=__UpperCAmelCase				,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,   required=__UpperCAmelCase				,   )
						UpperCAmelCase_					=	parser.parse_args()
						UpperCAmelCase_					=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(__UpperCAmelCase				,   __UpperCAmelCase      )


if __name__ == "__main__":
							main()





