

"""simple docstring"""

import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						List[str]:
 lowerCAmelCase__   :				Dict 			=				test_file.split(os.path.sep   )
 if components[0:2] != ["tests", "models"]:
  raise ValueError(
      "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
      F"""{test_file} instead."""   )
 lowerCAmelCase__   :				Union[str, Any] 			=				components[-1]
 if not test_fn.endswith("py"   ):
  raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""   )
 if not test_fn.startswith("test_modeling_"   ):
  raise ValueError(
      F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""   )

 lowerCAmelCase__   :				Union[str, Any] 			=				components[:-1] + [test_fn.replace(".py"					,					""   )]
 lowerCAmelCase__   :				int 			=				".".join(lowercase__   )

 return test_module_path



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[Any]:
 lowerCAmelCase__   :				List[str] 			=				get_module_path(lowercase__   )
 lowerCAmelCase__   :				Tuple 			=				importlib.import_module(lowercase__   )

 return test_module



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						List[Any]:
 lowerCAmelCase__   :				Optional[int] 			=				[]
 lowerCAmelCase__   :				int 			=				get_test_module(lowercase__   )
 for attr in dir(lowercase__   ):
  if attr.endswith("ModelTester"   ):
   tester_classes.append(getattr(lowercase__					,					lowercase__   )   )

    # sort with class names
 return sorted(lowercase__					,					key=lambda lowercase__   : x.__name__   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Dict:
 lowerCAmelCase__   :				Dict 			=				[]
 lowerCAmelCase__   :				Optional[int] 			=				get_test_module(lowercase__   )
 for attr in dir(lowercase__   ):
  lowerCAmelCase__   :				int 			=				getattr(lowercase__					,					lowercase__   )
  # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
  # `all_model_classes` is not empty (which also excludes other special classes).
  lowerCAmelCase__   :				Optional[Any] 			=				getattr(lowercase__					,					"all_model_classes"					,					[]   )
  if len(lowercase__   ) > 0:
   test_classes.append(lowercase__   )

    # sort with class names
 return sorted(lowercase__					,					key=lambda lowercase__   : x.__name__   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[int]:
 lowerCAmelCase__   :				Tuple 			=				get_test_classes(lowercase__   )
 lowerCAmelCase__   :				Dict 			=				set()
 for test_class in test_classes:
  model_classes.update(test_class.all_model_classes   )

 # sort with class names
 return sorted(lowercase__					,					key=lambda lowercase__   : x.__name__   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Dict:
 lowerCAmelCase__   :				Optional[int] 			=				test_class()
 if hasattr(lowercase__					,					"setUp"   ):
  test.setUp()

 lowerCAmelCase__   :				Optional[Any] 			=				None
 if hasattr(lowercase__					,					"model_tester"   ):
  # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
  if test.model_tester is not None:
   lowerCAmelCase__   :				List[str] 			=				test.model_tester.__class__

 return model_tester



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						List[Any]:
 lowerCAmelCase__   :				str 			=				get_test_classes(lowercase__   )

 lowerCAmelCase__   :				Any 			=				[]
 for test_class in test_classes:
  if model_class in test_class.all_model_classes:
   target_test_classes.append(lowercase__   )

    # sort with class names
 return sorted(lowercase__					,					key=lambda lowercase__   : x.__name__   )



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Any:
 lowerCAmelCase__   :				int 			=				get_test_classes_for_model(lowercase__					,					lowercase__   )

 lowerCAmelCase__   :				Union[str, Any] 			=				[]
 for test_class in test_classes:
  lowerCAmelCase__   :				Any 			=				get_model_tester_from_test_class(lowercase__   )
  if tester_class is not None:
   tester_classes.append(lowercase__   )

    # sort with class names
 return sorted(lowercase__					,					key=lambda lowercase__   : x.__name__   )



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 lowerCAmelCase__   :				int 			=				get_test_classes(lowercase__   )
 lowerCAmelCase__   :				Dict 			=				{test_class: get_model_tester_from_test_class(lowercase__   ) for test_class in test_classes}
 return test_tester_mapping



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Dict:
 lowerCAmelCase__   :				str 			=				get_model_classes(lowercase__   )
 lowerCAmelCase__   :				Any 			=				{
     model_class: get_test_classes_for_model(lowercase__					,					lowercase__   ) for model_class in model_classes
 }
 return model_test_mapping



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Any:
 lowerCAmelCase__   :				int 			=				get_model_classes(lowercase__   )
 lowerCAmelCase__   :				List[str] 			=				{
     model_class: get_tester_classes_for_model(lowercase__					,					lowercase__   ) for model_class in model_classes
 }
 return model_to_tester_mapping






def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Dict:
 if isinstance(lowercase__					,					lowercase__   ):
  return o
 elif isinstance(lowercase__					,					lowercase__   ):
  return o.__name__
 elif isinstance(lowercase__					,					(list, tuple)   ):
  return [to_json(lowercase__   ) for x in o]
 elif isinstance(lowercase__					,					lowercase__   ):
  return {to_json(lowercase__   ): to_json(lowercase__   ) for k, v in o.items()}
 else:
  return o









"""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		     =    1_6
_UpperCamelCase		     =    3_2



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 return int(x / 2**2_0   )






class 			__a					:





       """simple docstring"""


       def __enter__(   self   ):





        """simple docstring"""

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






       def __exit__(   self			,					*snake_case   ):





        """simple docstring"""

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




def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__ = 1_6					,					lowercase__ = "bert-base-cased"					,					lowercase__ = 3_2_0					,					lowercase__ = 1_6_0					,					)			->						str:
 lowerCAmelCase__   :				Union[str, Any] 			=				AutoTokenizer.from_pretrained(lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				load_dataset(
     "glue"					,					"mrpc"					,					split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

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

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

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

 def collate_fn(lowercase__   ):
  # 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(lowercase__					,					padding="max_length"					,					max_length=1_2_8					,					return_tensors="pt"   )
  return tokenizer.pad(lowercase__					,					padding="longest"					,					return_tensors="pt"   )

 # Instantiate dataloaders.
 lowerCAmelCase__   :				Tuple 			=				DataLoader(
     tokenized_datasets["train"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				DataLoader(
     tokenized_datasets["validation"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )

 return train_dataloader, eval_dataloader



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Dict:
 # Initialize accelerator
 lowerCAmelCase__   :				Optional[Any] 			=				Accelerator()

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

 set_seed(lowercase__   )
 lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[Any] 			=				get_dataloaders(lowercase__					,					lowercase__					,					lowercase__					,					args.n_train					,					args.n_val   )

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

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

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

 # We need to keep track of how many total steps we have iterated over
 lowerCAmelCase__   :				str 			=				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__   :				List[Any] 			=				{}
 for epoch in range(lowercase__					,					lowercase__   ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(lowercase__   ):
    lowerCAmelCase__   :				Optional[Any] 			=				model(**lowercase__   )
    lowerCAmelCase__   :				Optional[int] 			=				outputs.loss
    lowerCAmelCase__   :				Any 			=				loss / gradient_accumulation_steps
    accelerator.backward(lowercase__   )
    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[int] 			=				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(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( )			->						Optional[Any]:
 lowerCAmelCase__   :				int 			=				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
 parser.add_argument(
     "--model_name_or_path"					,					type=lowercase__					,					default="bert-base-cased"					,					help="Path to pretrained model or model identifier from huggingface.co/models."					,					required=lowercase__					,					)
 parser.add_argument(
     "--output_dir"					,					type=lowercase__					,					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=lowercase__					,					default=lowercase__					,					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=lowercase__					,					default=3_2_0					,					help="Number of training examples to use."					,					)
 parser.add_argument(
     "--n_val"					,					type=lowercase__					,					default=1_6_0					,					help="Number of validation examples to use."					,					)
 parser.add_argument(
     "--num_epochs"					,					type=lowercase__					,					default=1					,					help="Number of train epochs."					,					)
 lowerCAmelCase__   :				Optional[int] 			=				parser.parse_args()
 lowerCAmelCase__   :				Dict 			=				{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
 training_function(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    main()






