"""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


__lowerCAmelCase					: Dict        =1_6
__lowerCAmelCase					: Dict        =3_2


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]	)				->					Optional[Any]:
      '''simple docstring'''


      return int(x / 2**2_0	)





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
            lowercase														=			torch.cuda.memory_allocated()
            return self







      def __exit__(		self	, *__lowerCAmelCase		):







            """simple docstring"""



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






def     UpperCAmelCase__ (       lowerCAmelCase__  :Accelerator      ,	lowerCAmelCase__  :int = 1_6      ,	lowerCAmelCase__  :str = "bert-base-cased"      ,	lowerCAmelCase__  :int = 3_2_0      ,	lowerCAmelCase__  :int = 1_6_0      ,	)				->					Any:
      '''simple docstring'''


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

      def tokenize_function(lowerCAmelCase__  :List[str]	):
            # max_length=None => use the model max length (it's actually the default)
            lowercase														=			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
      lowercase														=			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
      lowercase														=			tokenized_datasets.rename_column("""label"""      ,	"""labels"""	)

      def collate_fn(lowerCAmelCase__  :List[Any]	):
            # 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=1_2_8      ,	return_tensors="""pt"""	)
            return tokenizer.pad(lowerCAmelCase__      ,	padding="""longest"""      ,	return_tensors="""pt"""	)

      # Instantiate dataloaders.
      lowercase														=			DataLoader(
          tokenized_datasets["""train"""]      ,	shuffle=lowerCAmelCase__      ,	collate_fn=lowerCAmelCase__      ,	batch_size=lowerCAmelCase__	)
      lowercase														=			DataLoader(
          tokenized_datasets["""validation"""]      ,	shuffle=lowerCAmelCase__      ,	collate_fn=lowerCAmelCase__      ,	batch_size=lowerCAmelCase__	)

      return train_dataloader, eval_dataloader


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :str	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			Accelerator()

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

      set_seed(lowerCAmelCase__	)
      lowercase							,							lowercase														=			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)
      lowercase														=			AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__      ,	return_dict=lowerCAmelCase__	)

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

      if accelerator.state.deepspeed_plugin is not None:
            lowercase														=			accelerator.state.deepspeed_plugin.deepspeed_config[
                """gradient_accumulation_steps"""
            ]
      else:
            lowercase														=			1
      lowercase														=			(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
      ):
            lowercase														=			get_linear_schedule_with_warmup(
                optimizer=lowerCAmelCase__      ,	num_warmup_steps=0      ,	num_training_steps=lowerCAmelCase__      ,	)
      else:
            lowercase														=			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.
      lowercase							,							lowercase							,							lowercase							,							lowercase							,							lowercase														=			accelerator.prepare(
          lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)

      # We need to keep track of how many total steps we have iterated over
      lowercase														=			0
      # We also need to keep track of the stating epoch so files are named properly
      lowercase														=			0

      # Now we train the model
      lowercase														=			{}
      for epoch in range(lowerCAmelCase__      ,	lowerCAmelCase__	):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(lowerCAmelCase__	):
                        lowercase														=			model(**lowerCAmelCase__	)
                        lowercase														=			outputs.loss
                        lowercase														=			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	)	)	)
            lowercase														=			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     UpperCAmelCase__ (       )				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			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=3_2_0      ,	help="""Number of training examples to use."""      ,	)
      parser.add_argument(
          """--n_val"""      ,	type=lowerCAmelCase__      ,	default=1_6_0      ,	help="""Number of validation examples to use."""      ,	)
      parser.add_argument(
          """--num_epochs"""      ,	type=lowerCAmelCase__      ,	default=1      ,	help="""Number of train epochs."""      ,	)
      lowercase														=			parser.parse_args()
      lowercase														=			{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
      training_function(lowerCAmelCase__      ,	lowerCAmelCase__	)


if __name__ == "__main__":
       main()




"""simple docstring"""






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase					: Union[str, Any]        =subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowerCAmelCase					: List[str]        =subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()

__lowerCAmelCase					: List[str]        ="""|""".join(sys.argv[1:])
__lowerCAmelCase					: Dict        =re.compile(RF"""^({joined_dirs}).*?\.py$""")

__lowerCAmelCase					: int        =[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")



