





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   				=			16
_lowerCamelCase   				=			32






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str    ):
		return int(x / 2**20    )




class   UpperCamelCase_       :


							def __enter__(							self    :int		)   ->   Tuple:



									"""simple docstring"""



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


							def __exit__(							self    :int							,				*__A    :Any		)   ->   List[str]:



									"""simple docstring"""



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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Accelerator					,   UpperCamelCase__:							int = 16					,   UpperCamelCase__:							str = "bert-base-cased"					,   UpperCamelCase__:							int = 320					,   UpperCamelCase__:							int = 160					,   ):
		SCREAMING_SNAKE_CASE__			=      AutoTokenizer.from_pretrained(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      load_dataset(
		    """glue"""					,   """mrpc"""					,   split={"""train""": f'''train[:{n_train}]''', """validation""": f'''validation[:{n_val}]'''}    )

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

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

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

		def collate_fn(UpperCamelCase__:							str    ):
				# 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(UpperCamelCase__					,   padding="""max_length"""					,   max_length=128					,   return_tensors="""pt"""    )
				return tokenizer.pad(UpperCamelCase__					,   padding="""longest"""					,   return_tensors="""pt"""    )

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

		return train_dataloader, eval_dataloader






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							Tuple    ):
		# Initialize accelerator
		SCREAMING_SNAKE_CASE__			=      Accelerator()

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

		set_seed(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      get_dataloaders(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__					,   args.n_train					,   args.n_val    )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		SCREAMING_SNAKE_CASE__			=      AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__					,   return_dict=UpperCamelCase__    )

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

		if accelerator.state.deepspeed_plugin is not None:
				SCREAMING_SNAKE_CASE__			=      accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				SCREAMING_SNAKE_CASE__			=      1
		SCREAMING_SNAKE_CASE__			=      (len(UpperCamelCase__    ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				SCREAMING_SNAKE_CASE__			=      get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase__					,   num_warmup_steps=0					,   num_training_steps=UpperCamelCase__					,   )
		else:
				SCREAMING_SNAKE_CASE__			=      DummyScheduler(UpperCamelCase__					,   total_num_steps=UpperCamelCase__					,   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.
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      accelerator.prepare(
		    UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )

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

		# Now we train the model
		SCREAMING_SNAKE_CASE__			=      {}
		for epoch in range(UpperCamelCase__					,   UpperCamelCase__    ):
				with TorchTracemalloc() as tracemalloc:
						model.train()
						for step, batch in enumerate(UpperCamelCase__    ):
								SCREAMING_SNAKE_CASE__			=      model(**UpperCamelCase__    )
								SCREAMING_SNAKE_CASE__			=      outputs.loss
								SCREAMING_SNAKE_CASE__			=      loss / gradient_accumulation_steps
								accelerator.backward(UpperCamelCase__    )
								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    )    )    )
				SCREAMING_SNAKE_CASE__			=      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(UpperCamelCase__					,   UpperCamelCase__    )






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
		parser.add_argument(
		    """--model_name_or_path"""					,   type=UpperCamelCase__					,   default="""bert-base-cased"""					,   help="""Path to pretrained model or model identifier from huggingface.co/models."""					,   required=UpperCamelCase__					,   )
		parser.add_argument(
		    """--output_dir"""					,   type=UpperCamelCase__					,   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=UpperCamelCase__					,   default=UpperCamelCase__					,   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=UpperCamelCase__					,   default=320					,   help="""Number of training examples to use."""					,   )
		parser.add_argument(
		    """--n_val"""					,   type=UpperCamelCase__					,   default=160					,   help="""Number of validation examples to use."""					,   )
		parser.add_argument(
		    """--num_epochs"""					,   type=UpperCamelCase__					,   default=1					,   help="""Number of train epochs."""					,   )
		SCREAMING_SNAKE_CASE__			=      parser.parse_args()
		SCREAMING_SNAKE_CASE__			=      {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
		training_function(UpperCamelCase__					,   UpperCamelCase__    )


if __name__ == "__main__":
	main()






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase__    )
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					field(default="image-classification"				,  metadata={"include_in_asdict_even_if_is_default": True}    )
							lowerCamelCase_           =					Features({"image": Image()}    )
							lowerCamelCase_           =					Features({"labels": ClassLabel}    )
							lowerCamelCase_           =					"image"
							lowerCamelCase_           =					"labels"


							def      _snake_case  (							self    :List[str]							,				__A    :Tuple		)   ->   Tuple:



									"""simple docstring"""



									if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.'''		)
									if not isinstance(features[self.label_column]							,				__A		):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self		)
									SCREAMING_SNAKE_CASE__			=      self.label_schema.copy()
									SCREAMING_SNAKE_CASE__			=      features[self.label_column]
									SCREAMING_SNAKE_CASE__			=      label_schema
									return task_template


							@property
							def      _snake_case  (							self    :Dict		)   ->   Dict[str, str]:



									"""simple docstring"""



									return {
									    self.image_column: "image",
									    self.label_column: "labels",
									}