

"""simple docstring"""





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


__UpperCamelCase    : int				 =						1_6
__UpperCamelCase    : List[str]				 =						3_2

def     __SCREAMING_SNAKE_CASE    ( A_					):
	return int(x / 2**20					)







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __enter__(			self     :							str						):
						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						lowerCAmelCase__      :		Optional[Any]    							=      torch.cuda.memory_allocated()
						return self







					def __exit__(			self     :							Optional[Any]  ,*lowercase_     :							str						):
						gc.collect()
						torch.cuda.empty_cache()
						lowerCAmelCase__      :		Optional[int]    							=      torch.cuda.memory_allocated()
						lowerCAmelCase__      :		List[Any]    							=      torch.cuda.max_memory_allocated()
						lowerCAmelCase__      :		Dict    							=      bamb(self.end - self.begin						)
						lowerCAmelCase__      :		Optional[Any]    							=      bamb(self.peak - self.begin						)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_ = 16		,	A_ = "bert-base-cased"		,	A_ = 3_20		,	A_ = 1_60		,	):
	lowerCAmelCase__      :		Union[str, Any]    							=      AutoTokenizer.from_pretrained(A_					)
	lowerCAmelCase__      :		Optional[Any]    							=      load_dataset(
	    '''glue'''		,	'''mrpc'''		,	split={'''train''': f'train[:{n_train}]', '''validation''': f'validation[:{n_val}]'}					)

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

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

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

	def collate_fn(A_					):
		# 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(A_		,	padding='''max_length'''		,	max_length=1_28		,	return_tensors='''pt'''					)
		return tokenizer.pad(A_		,	padding='''longest'''		,	return_tensors='''pt'''					)

	# Instantiate dataloaders.
	lowerCAmelCase__      :		Optional[int]    							=      DataLoader(
	    tokenized_datasets['''train''']		,	shuffle=A_		,	collate_fn=A_		,	batch_size=A_					)
	lowerCAmelCase__      :		Any    							=      DataLoader(
	    tokenized_datasets['''validation''']		,	shuffle=A_		,	collate_fn=A_		,	batch_size=A_					)

	return train_dataloader, eval_dataloader

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	# Initialize accelerator
	lowerCAmelCase__      :		List[Any]    							=      Accelerator()

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

	set_seed(A_					)
	lowerCAmelCase__							,lowerCAmelCase__      :		str    							=      get_dataloaders(A_		,	A_		,	A_		,	args.n_train		,	args.n_val					)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCAmelCase__      :		Optional[int]    							=      AutoModelForSequenceClassification.from_pretrained(A_		,	return_dict=A_					)

	# Instantiate optimizer
	lowerCAmelCase__      :		int    							=      (
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	lowerCAmelCase__      :		List[str]    							=      optimizer_cls(params=model.parameters()		,	lr=A_					)

	if accelerator.state.deepspeed_plugin is not None:
		lowerCAmelCase__      :		Tuple    							=      accelerator.state.deepspeed_plugin.deepspeed_config[
		    '''gradient_accumulation_steps'''
		]
	else:
		lowerCAmelCase__      :		Dict    							=      1
	lowerCAmelCase__      :		List[Any]    							=      (len(A_					) * 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=A_		,	num_warmup_steps=0		,	num_training_steps=A_		,	)
	else:
		lowerCAmelCase__      :		Optional[Any]    							=      DummyScheduler(A_		,	total_num_steps=A_		,	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__      :		Optional[int]    							=      accelerator.prepare(
	    A_		,	A_		,	A_		,	A_		,	A_					)

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

	# Now we train the model
	lowerCAmelCase__      :		List[Any]    							=      {}
	for epoch in range(A_		,	A_					):
		with TorchTracemalloc() as tracemalloc:
			model.train()
			for step, batch in enumerate(A_					):
				lowerCAmelCase__      :		int    							=      model(**A_					)
				lowerCAmelCase__      :		Tuple    							=      outputs.loss
				lowerCAmelCase__      :		Tuple    							=      loss / gradient_accumulation_steps
				accelerator.backward(A_					)
				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__      :		Dict    							=      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(A_		,	A_					)

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		str    							=      argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''					)
	parser.add_argument(
	    '''--model_name_or_path'''		,	type=A_		,	default='''bert-base-cased'''		,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''		,	required=A_		,	)
	parser.add_argument(
	    '''--output_dir'''		,	type=A_		,	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=A_		,	default=A_		,	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=A_		,	default=3_20		,	help='''Number of training examples to use.'''		,	)
	parser.add_argument(
	    '''--n_val'''		,	type=A_		,	default=1_60		,	help='''Number of validation examples to use.'''		,	)
	parser.add_argument(
	    '''--num_epochs'''		,	type=A_		,	default=1		,	help='''Number of train epochs.'''		,	)
	lowerCAmelCase__      :		List[Any]    							=      parser.parse_args()
	lowerCAmelCase__      :		List[str]    							=      {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
	training_function(A_		,	A_					)


if __name__ == "__main__":
							main()







"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      analyze_text(A_					)
	lowerCAmelCase__      :		Any    							=      list(''' ''' + ascii_lowercase					)
	# what is our total sum of probabilities.
	lowerCAmelCase__      :		Optional[Any]    							=      sum(single_char_strings.values()					)

	# one length string
	lowerCAmelCase__      :		int    							=      0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			lowerCAmelCase__      :		Union[str, Any]    							=      single_char_strings[ch]
			lowerCAmelCase__      :		Dict    							=      my_str / all_sum
			my_fir_sum += prob * math.loga(A_					)  # entropy formula.

    # print entropy
	print(f'{round(-1 * my_fir_sum					):.1f}'					)

	# two len string
	lowerCAmelCase__      :		List[Any]    							=      sum(two_char_strings.values()					)
	lowerCAmelCase__      :		Optional[int]    							=      0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			lowerCAmelCase__      :		int    							=      cha + cha
			if sequence in two_char_strings:
				lowerCAmelCase__      :		Optional[Any]    							=      two_char_strings[sequence]
				lowerCAmelCase__      :		int    							=      int(A_					) / all_sum
				my_sec_sum += prob * math.loga(A_					)

    # print second entropy
	print(f'{round(-1 * my_sec_sum					):.1f}'					)

	# print the difference between them
	print(f'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		List[str]    							=      Counter()  # type: ignore
	lowerCAmelCase__      :		Optional[int]    							=      Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0		,	len(A_					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings

def     __SCREAMING_SNAKE_CASE    ( ):
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
							main()




