

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


SCREAMING_SNAKE_CASE_            = 16
SCREAMING_SNAKE_CASE_            = 32

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    str:
				return int(x / 2**20	)

class 			a					:


						def __enter__(    self		):







										'''simple docstring'''


										gc.collect()
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
										_UpperCAmelCase    :						Union[str, Any]							=      torch.cuda.memory_allocated()
										return self





						def __exit__(    self  ,   *A_		):







										'''simple docstring'''


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







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Accelerator					, lowerCAmelCase:					int = 16					, lowerCAmelCase:					str = "bert-base-cased"					, lowerCAmelCase:					int = 320					, lowerCAmelCase:					int = 160					, )				->    Any:
				_UpperCAmelCase    :						Union[str, Any]							=      AutoTokenizer.from_pretrained(lowerCAmelCase	)
				_UpperCAmelCase    :						List[Any]							=      load_dataset(
				    "glue"					, "mrpc"					, split={"train": F'train[:{n_train}]', "validation": F'validation[:{n_val}]'}	)

				def tokenize_function(lowerCAmelCase:					Optional[int]	):
								# max_length=None => use the model max length (it's actually the default)
								_UpperCAmelCase    :						Tuple							=      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
				_UpperCAmelCase    :						int							=      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
				_UpperCAmelCase    :						List[Any]							=      tokenized_datasets.rename_column("label"					, "labels"	)

				def collate_fn(lowerCAmelCase:					Dict	):
								# 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.
				_UpperCAmelCase    :						Dict							=      DataLoader(
				    tokenized_datasets["train"]					, shuffle=lowerCAmelCase					, collate_fn=lowerCAmelCase					, batch_size=lowerCAmelCase	)
				_UpperCAmelCase    :						Union[str, Any]							=      DataLoader(
				    tokenized_datasets["validation"]					, shuffle=lowerCAmelCase					, collate_fn=lowerCAmelCase					, batch_size=lowerCAmelCase	)

				return train_dataloader, eval_dataloader

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					Optional[int]	)				->    str:
				# Initialize accelerator
				_UpperCAmelCase    :						List[str]							=      Accelerator()

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

				set_seed(lowerCAmelCase	)
				_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, 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)
				_UpperCAmelCase    :						Any							=      AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase					, return_dict=lowerCAmelCase	)

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

				if accelerator.state.deepspeed_plugin is not None:
								_UpperCAmelCase    :						Tuple							=      accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								_UpperCAmelCase    :						List[str]							=      1
				_UpperCAmelCase    :						Optional[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
				):
								_UpperCAmelCase    :						Optional[int]							=      get_linear_schedule_with_warmup(
								    optimizer=lowerCAmelCase					, num_warmup_steps=0					, num_training_steps=lowerCAmelCase					, )
				else:
								_UpperCAmelCase    :						int							=      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.
				_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      accelerator.prepare(
				    lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)

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

				# Now we train the model
				_UpperCAmelCase    :						int							=      {}
				for epoch in range(lowerCAmelCase					, lowerCAmelCase	):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCAmelCase	):
																_UpperCAmelCase    :						List[Any]							=      model(**lowerCAmelCase	)
																_UpperCAmelCase    :						Optional[Any]							=      outputs.loss
																_UpperCAmelCase    :						Union[str, Any]							=      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	)	)	)
								_UpperCAmelCase    :						Tuple							=      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 							__SCREAMING_SNAKE_CASE							(			)				->    int:
				_UpperCAmelCase    :						List[Any]							=      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."					, )
				_UpperCAmelCase    :						List[Any]							=      parser.parse_args()
				_UpperCAmelCase    :						Optional[int]							=      {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
				training_function(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					main()





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    bool:
				_UpperCAmelCase    :						Optional[Any]							=      len(lowerCAmelCase	) + 1
				_UpperCAmelCase    :						Optional[int]							=      len(lowerCAmelCase	) + 1

				# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
				# length i of input_string matches with prefix string of length j of
				# given pattern.
				# "dp" stands for dynamic programming.
				_UpperCAmelCase    :						List[str]							=      [[0 for i in range(lowerCAmelCase	)] for j in range(lowerCAmelCase	)]

				# since string of zero length match pattern of zero length
				_UpperCAmelCase    :						List[Any]							=      1

				# since pattern of zero length will never match with string of non-zero length
				for i in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Dict							=      0

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Tuple							=      dp[0][j - 2] if pattern[j - 1] == "*" else 0

				# now using bottom-up approach to find for all remaining lengths
				for i in range(1					, lowerCAmelCase	):
								for j in range(1					, lowerCAmelCase	):
												if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																_UpperCAmelCase    :						Optional[Any]							=      dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				_UpperCAmelCase    :						List[str]							=      1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				_UpperCAmelCase    :						str							=      dp[i - 1][j]
																else:
																				_UpperCAmelCase    :						int							=      0
												else:
																_UpperCAmelCase    :						List[Any]							=      0

				return bool(dp[-1][-1]	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# inputing the strings
					# input_string = input("input a string :")
					# pattern = input("input a pattern :")

					SCREAMING_SNAKE_CASE_            = 'aab'
					SCREAMING_SNAKE_CASE_            = 'c*a*b'

					# using function to check whether given string matches the given pattern
					if match_pattern(input_string, pattern):
										print(F'''{input_string} matches the given pattern {pattern}''')
					else:
										print(F'''{input_string} does not match with the given pattern {pattern}''')


