



import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case				:		str										=1_6
__snake_case				:		str										=3_2






def 	lowerCAmelCase__		(    lowerCamelCase_		:		int):





				'''simple docstring'''

				return int(x / 2**20)



class        lowerCamelCase__			:




		'''simple docstring'''



		def __enter__(self )     ->			str:

						"""simple docstring"""






						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						lowerCAmelCase__     :				int												=     torch.cuda.memory_allocated()
						return self



		def __exit__(self		,*__lowerCamelCase )     ->			int:

						"""simple docstring"""






						gc.collect()
						torch.cuda.empty_cache()
						lowerCAmelCase__     :				str												=     torch.cuda.memory_allocated()
						lowerCAmelCase__     :				int												=     torch.cuda.max_memory_allocated()
						lowerCAmelCase__     :				Dict												=     bamb(self.end - self.begin )
						lowerCAmelCase__     :				Dict												=     bamb(self.peak - self.begin )
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 	lowerCAmelCase__		(    lowerCamelCase_		:		List[Any]       ,lowerCamelCase_		:		str = 16       ,lowerCamelCase_		:		Optional[Any] = "bert-base-cased"       ,lowerCamelCase_		:		List[str] = 320       ,lowerCamelCase_		:		List[Any] = 160       ,):





				'''simple docstring'''

				lowerCAmelCase__     :				int												=     AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__     :				Any												=     load_dataset(
				    '''glue'''       ,'''mrpc'''       ,split={'''train''': f"""train[:{n_train}]""", '''validation''': f"""validation[:{n_val}]"""})

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				lowerCAmelCase__     :				str												=     datasets.map(
				    lowerCamelCase_       ,batched=lowerCamelCase_       ,remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,load_from_cache_file=lowerCamelCase_)

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

				def collate_fn(lowerCamelCase_		:		int):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowerCamelCase_       ,padding='''max_length'''       ,max_length=128       ,return_tensors='''pt''')
								return tokenizer.pad(lowerCamelCase_       ,padding='''longest'''       ,return_tensors='''pt''')

				# Instantiate dataloaders.
				lowerCAmelCase__     :				Union[str, Any]												=     DataLoader(
				    tokenized_datasets['''train''']       ,shuffle=lowerCamelCase_       ,collate_fn=lowerCamelCase_       ,batch_size=lowerCamelCase_)
				lowerCAmelCase__     :				str												=     DataLoader(
				    tokenized_datasets['''validation''']       ,shuffle=lowerCamelCase_       ,collate_fn=lowerCamelCase_       ,batch_size=lowerCamelCase_)

				return train_dataloader, eval_dataloader






def 	lowerCAmelCase__		(    lowerCamelCase_		:		Union[str, Any]       ,lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__     :				Union[str, Any]												=     Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase__     :				Any												=     config['''lr''']
				lowerCAmelCase__     :				Optional[Any]												=     int(config['''num_epochs'''])
				lowerCAmelCase__     :				Any												=     int(config['''seed'''])
				lowerCAmelCase__     :				Any												=     int(config['''batch_size'''])
				lowerCAmelCase__     :				Optional[Any]												=     args.model_name_or_path

				set_seed(lowerCamelCase_)
				lowerCAmelCase__						,		lowerCAmelCase__     :				Any												=     get_dataloaders(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,args.n_train       ,args.n_val)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase__     :				Dict												=     AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_       ,return_dict=lowerCamelCase_)

				# Instantiate optimizer
				lowerCAmelCase__     :				Any												=     (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				lowerCAmelCase__     :				Tuple												=     optimizer_cls(params=model.parameters()       ,lr=lowerCamelCase_)

				if accelerator.state.deepspeed_plugin is not None:
								lowerCAmelCase__     :				Dict												=     accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								lowerCAmelCase__     :				Optional[int]												=     1
				lowerCAmelCase__     :				Union[str, Any]												=     (len(lowerCamelCase_) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								lowerCAmelCase__     :				str												=     get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase_       ,num_warmup_steps=0       ,num_training_steps=lowerCamelCase_       ,)
				else:
								lowerCAmelCase__     :				Optional[Any]												=     DummyScheduler(lowerCamelCase_       ,total_num_steps=lowerCamelCase_       ,warmup_num_steps=0)

				# Prepare everything
				# 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.
				lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__     :				int												=     accelerator.prepare(
				    lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_)

				# We need to keep track of how many total steps we have iterated over
				lowerCAmelCase__     :				Union[str, Any]												=     0
				# We also need to keep track of the stating epoch so files are named properly
				lowerCAmelCase__     :				Tuple												=     0

				# Now we train the model
				lowerCAmelCase__     :				Optional[Any]												=     {}
				for epoch in range(lowerCamelCase_       ,lowerCamelCase_):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCamelCase_):
																lowerCAmelCase__     :				List[Any]												=     model(**lowerCamelCase_)
																lowerCAmelCase__     :				List[str]												=     outputs.loss
																lowerCAmelCase__     :				Dict												=     loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase_)
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # 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)))
								lowerCAmelCase__     :				Optional[Any]												=     tracemalloc.peaked + bamb(tracemalloc.begin)
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir       ,'''peak_memory_utilization.json''')       ,'''w''') as f:
												json.dump(lowerCamelCase_       ,lowerCamelCase_)






def 	lowerCAmelCase__		(    ):





				'''simple docstring'''

				lowerCAmelCase__     :				str												=     argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''')
				parser.add_argument(
				    '''--model_name_or_path'''       ,type=lowerCamelCase_       ,default='''bert-base-cased'''       ,help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,required=lowerCamelCase_       ,)
				parser.add_argument(
				    '''--output_dir'''       ,type=lowerCamelCase_       ,default='''.'''       ,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,)
				parser.add_argument(
				    '''--peak_memory_upper_bound'''       ,type=lowerCamelCase_       ,default=lowerCamelCase_       ,help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''       ,)
				parser.add_argument(
				    '''--n_train'''       ,type=lowerCamelCase_       ,default=320       ,help='''Number of training examples to use.'''       ,)
				parser.add_argument(
				    '''--n_val'''       ,type=lowerCamelCase_       ,default=160       ,help='''Number of validation examples to use.'''       ,)
				parser.add_argument(
				    '''--num_epochs'''       ,type=lowerCamelCase_       ,default=1       ,help='''Number of train epochs.'''       ,)
				lowerCAmelCase__     :				Optional[int]												=     parser.parse_args()
				lowerCAmelCase__     :				Optional[int]												=     {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCamelCase_       ,lowerCamelCase_)


if __name__ == "__main__":
							main()


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__a  :     List[Any]       =  logging.getLogger(__name__)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							argparse.ArgumentParser(
				    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''							)
				parser.add_argument(
				    '''--dataset_name'''							,			type=lowercase							,			default='''wikitext'''							,			help='''Name of the training. Explore datasets at: hf.co/datasets.'''							,			)
				parser.add_argument(
				    '''--dataset_config'''							,			type=lowercase							,			default='''wikitext-103-raw-v1'''							,			help='''Configuration name of the dataset.'''							)
				parser.add_argument(
				    '''--tokenizer_name_or_path'''							,			type=lowercase							,			default='''sayakpaul/unigram-tokenizer-wikitext'''							,			help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''							,			)
				parser.add_argument(
				    '''--shard_size'''							,			type=lowercase							,			default=1000							,			help='''Number of entries to go in a single shard.'''							,			)
				parser.add_argument('''--split'''							,			type=lowercase							,			default='''train'''							,			choices=['''train''', '''test''', '''validation''']							)
				parser.add_argument(
				    '''--limit'''							,			default=lowercase							,			type=lowercase							,			help='''Limit the number of shards (used for debugging).'''							,			)
				parser.add_argument(
				    '''--max_length'''							,			type=lowercase							,			default=512							,			help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
				    ''' sequence length that is a multiple of 8.'''							,			)
				parser.add_argument(
				    '''--output_dir'''							,			default='''tf-tpu'''							,			type=lowercase							,			help='''Output directory where the TFRecord shards will be saved. If the'''
				    ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
				    ''' shards will be directly saved to a Google Cloud Storage bucket.'''							,			)

				__lowercase 			=							parser.parse_args()
				return args





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				def fn(lowercase							):
								return tokenizer(examples['''text''']							)

				return fn





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							[]
				for i in range(len(tokenized_data['''input_ids''']							)							):
								__lowercase 			=							{
								    '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]							)							),
								    '''attention_mask''': tf.train.Feature(
								        intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]							)							),
								}
								__lowercase 			=							tf.train.Features(feature=lowercase							)
								__lowercase 			=							tf.train.Example(features=lowercase							)
								__lowercase 			=							example.SerializeToString()
								records.append(lowercase							)
				return records





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							datasets.load_dataset(args.dataset_name							,			args.dataset_config							,			split=args.split							)

				if args.limit is not None:
								__lowercase 			=							min(len(lowercase							)							,			args.limit							)
								__lowercase 			=							dataset.select(range(lowercase							)							)
								print(F"Limiting the dataset to {args.limit} entries."							)

				__lowercase 			=							AutoTokenizer.from_pretrained(args.tokenizer_name_or_path							)

				# Handle output directory creation.
				# For serializing into a Google Cloud Storage Bucket, one needs to first
				# create a bucket.
				if "gs" not in args.output_dir:
								if not os.path.exists(args.output_dir							):
												os.makedirs(args.output_dir							)
								__lowercase 			=							os.path.join(args.output_dir							,			args.split							)
								if not os.path.exists(lowercase							):
												os.makedirs(lowercase							)
				else:
								__lowercase 			=							os.path.join(args.output_dir							,			args.split							)

				# Tokenize the whole dataset at once.
				__lowercase 			=							tokenize_function(lowercase							)
				__lowercase 			=							dataset.map(lowercase							,			batched=lowercase							,			num_proc=4							,			remove_columns=['''text''']							)

				# We need to concatenate all our texts together, and then split the result
				# into chunks of a fixed size, which we will call block_size. To do this, we
				# will use the map method again, with the option batched=True. When we use batched=True,
				# the function we pass to map() will be passed multiple inputs at once, allowing us
				# to group them into more or fewer examples than we had in the input.
				# This allows us to create our new fixed-length samples. The advantage of this
				# method is that we don't lose a whole lot of content from the dataset compared to the
				# case where we simply tokenize with a pre-defined max_length.

				def group_texts(lowercase							):
								# Concatenate all texts.
								__lowercase 			=							{k: sum(examples[k]							,			[]							) for k in examples.keys()}
								__lowercase 			=							len(concatenated_examples[list(examples.keys()							)[0]]							)
								# We drop the small remainder, though you could add padding instead if the model supports it
								# In this, as in all things, we advise you to follow your heart 🫀
								__lowercase 			=							(total_length // args.max_length) * args.max_length
								# Split by chunks of max_len.
								__lowercase 			=							{
								    k: [t[i : i + args.max_length] for i in range(0							,			lowercase							,			args.max_length							)]
								    for k, t in concatenated_examples.items()
								}
								return result

				__lowercase 			=							dataset_tokenized.map(lowercase							,			batched=lowercase							,			batch_size=1000							,			num_proc=4							)

				__lowercase 			=							0
				__lowercase 			=							0
				for shard in range(0							,			len(lowercase							)							,			args.shard_size							):
								__lowercase 			=							grouped_dataset[shard : shard + args.shard_size]
								__lowercase 			=							len(dataset_snapshot['''input_ids''']							)
								__lowercase 			=							os.path.join(lowercase							,			F"dataset-{shard_count}-{records_containing}.tfrecord"							)
								__lowercase 			=							get_serialized_examples(lowercase							)

								with tf.io.TFRecordWriter(lowercase							) as out_file:
												for i in range(len(lowercase							)							):
																__lowercase 			=							serialized_examples[i]
																out_file.write(lowercase							)
												print('''Wrote file {} containing {} records'''.format(lowercase							,			lowercase							)							)

								shard_count += 1
								total_records += records_containing

				with open(F"split-{args.split}-records-count.txt"							,			'''w'''							) as f:
								print(F"Total {args.split} records: {total_records}"							,			file=lowercase							)


if __name__ == "__main__":
		__a  :     Optional[Any]       =  parse_args()
		main(args)