



'''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							:Tuple				    =					1_6
lowerCamelCase							:List[str]				    =					3_2



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return int(x / 2**20    )







class 	_lowerCAmelCase							:


	def __enter__(self							):
				gc.collect()
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
				A_	:						int	=     torch.cuda.memory_allocated()
				return self


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






def 	a			( lowerCamelCase__						,      lowerCamelCase__ = 16						,      lowerCamelCase__ = "bert-base-cased"						,      lowerCamelCase__ = 3_20						,      lowerCamelCase__ = 1_60						,      ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						List[str]	=     load_dataset(
			    """glue"""						,      """mrpc"""						,      split={"""train""": f'train[:{n_train}]', """validation""": f'validation[:{n_val}]'}    )

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

			def collate_fn(lowerCamelCase__    ):
						# 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_28						,      return_tensors="""pt"""    )
						return tokenizer.pad(lowerCamelCase__						,      padding="""longest"""						,      return_tensors="""pt"""    )

			# Instantiate dataloaders.
			A_	:						List[str]	=     DataLoader(
			    tokenized_datasets["""train"""]						,      shuffle=lowerCamelCase__						,      collate_fn=lowerCamelCase__						,      batch_size=lowerCamelCase__    )
			A_	:						Dict	=     DataLoader(
			    tokenized_datasets["""validation"""]						,      shuffle=lowerCamelCase__						,      collate_fn=lowerCamelCase__						,      batch_size=lowerCamelCase__    )

			return train_dataloader, eval_dataloader



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						str	=     Accelerator()

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

			set_seed(lowerCamelCase__    )
			A_,    A_	:						str	=     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)
			A_	:						Dict	=     AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__						,      return_dict=lowerCamelCase__    )

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

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

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

			# Now we train the model
			A_	:						List[Any]	=     {}
			for epoch in range(lowerCamelCase__						,      lowerCamelCase__    ):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(lowerCamelCase__    ):
												A_	:						List[str]	=     model(**lowerCamelCase__    )
												A_	:						List[Any]	=     outputs.loss
												A_	:						Dict	=     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    )    )    )
						A_	:						List[str]	=     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 	a			( ):
			'''simple docstring'''





			A_	:						Tuple	=     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_20						,      help="""Number of training examples to use."""						,      )
			parser.add_argument(
			    """--n_val"""						,      type=lowerCamelCase__						,      default=1_60						,      help="""Number of validation examples to use."""						,      )
			parser.add_argument(
			    """--num_epochs"""						,      type=lowerCamelCase__						,      default=1						,      help="""Number of train epochs."""						,      )
			A_	:						List[Any]	=     parser.parse_args()
			A_	:						List[Any]	=     {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase__						,      lowerCamelCase__    )


if __name__ == "__main__":
						main()




'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 	_lowerCAmelCase							(			unittest.TestCase ):


	def     _a	(self							):
				A_	:						Optional[Any]	=     10


	def     _a	(self							):
				A_	:						Dict	=     [1, 2, 3, 4]
				A_	:						List[Any]	=     [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_	:						Tuple	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_	:						Any	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						List[str]	=     """It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,    A_	:						Dict	=     process_story(lowercase							)
				self.assertEqual(lowercase       ,      []							)


	def     _a	(self							):
				A_	:						Optional[int]	=     """"""
				A_,    A_	:						List[str]	=     process_story(lowercase							)
				self.assertEqual(lowercase       ,      []							)
				self.assertEqual(lowercase       ,      []							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     (
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,    A_	:						int	=     process_story(lowercase							)

				A_	:						Optional[Any]	=     [
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase       ,      lowercase							)

				A_	:						Dict	=     ["""It was the best of times."""]
				self.assertEqual(lowercase       ,      lowercase							)


	def     _a	(self							):
				A_	:						Optional[int]	=     torch.tensor([1, 2, 3, 4]							)
				A_	:						Dict	=     torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      0							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						str	=     torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_	:						str	=     torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      23							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						Any	=     torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_	:						List[str]	=     torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      1							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						List[Any]	=     101
				A_	:						List[Any]	=     torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_	:						List[str]	=     torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_	:						Dict	=     compute_token_type_ids(lowercase       ,      lowercase							)
				np.testing.assert_array_equal(lowercase       ,      lowercase							)