




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


_snake_case										=       16
_snake_case										=       32







def 				lowerCAmelCase_	(     snake_case_     ):
      return int(x / 2**20     )







class 						lowercase							:

     def __enter__(  self				)						->							Any:
           gc.collect()
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
           _A							:     Any			=						torch.cuda.memory_allocated()
           return self



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







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = 16,snake_case_ = "bert-base-cased",snake_case_ = 320,snake_case_ = 160,):
      _A							:     List[Any]			=						AutoTokenizer.from_pretrained(snake_case_     )
      _A							:     List[str]			=						load_dataset(
          """glue""","""mrpc""",split={"""train""": f'''train[:{n_train}]''', """validation""": f'''validation[:{n_val}]'''}     )

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

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

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

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

      # Instantiate dataloaders.
      _A							:     Dict			=						DataLoader(
          tokenized_datasets["""train"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_     )
      _A							:     int			=						DataLoader(
          tokenized_datasets["""validation"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_     )

      return train_dataloader, eval_dataloader







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      # Initialize accelerator
      _A							:     Tuple			=						Accelerator()

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

      set_seed(snake_case_     )
      _A			,   _A							:     Union[str, Any]			=						get_dataloaders(snake_case_,snake_case_,snake_case_,args.n_train,args.n_val     )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _A							:     Optional[int]			=						AutoModelForSequenceClassification.from_pretrained(snake_case_,return_dict=snake_case_     )

      # Instantiate optimizer
      _A							:     Optional[Any]			=						(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      _A							:     Tuple			=						optimizer_cls(params=model.parameters(),lr=snake_case_     )

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

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

      # Now we train the model
      _A							:     Optional[Any]			=						{}
      for epoch in range(snake_case_,snake_case_     ):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(snake_case_     ):
                        _A							:     Dict			=						model(**snake_case_     )
                        _A							:     Dict			=						outputs.loss
                        _A							:     Dict			=						loss / gradient_accumulation_steps
                        accelerator.backward(snake_case_     )
                        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(snake_case_,snake_case_     )







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


if __name__ == "__main__":
  main()









import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_a      	=  get_tests_dir('''fixtures/spiece.model''')





@require_sentencepiece
@require_tokenizers
class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      AlbertTokenizer
	UpperCamelCase__								=      AlbertTokenizerFast
	UpperCamelCase__								=      True
	UpperCamelCase__								=      True
	UpperCamelCase__								=      True

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							# We have a SentencePiece fixture for testing
							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase    )
							tokenizer.save_pretrained(self.tmpdirname    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'this is a test'
							_UpperCAmelCase									=			'this is a test'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<pad>'
							_UpperCAmelCase									=			0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase    )       ,    UpperCAmelCase    )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase    )       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			list(self.get_tokenizer().get_vocab().keys()    )

							self.assertEqual(vocab_keys[0]       ,    '<pad>'    )
							self.assertEqual(vocab_keys[1]       ,    '<unk>'    )
							self.assertEqual(vocab_keys[-1]       ,    '▁eloquent'    )
							self.assertEqual(len(UpperCAmelCase    )       ,    3_0000    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.assertEqual(self.get_tokenizer().vocab_size       ,    3_0000    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							if not self.test_rust_tokenizer:
													return

							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			self.get_rust_tokenizer()

							_UpperCAmelCase									=			'I was born in 92000, and this is falsé.'

							_UpperCAmelCase									=			tokenizer.tokenize(UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.tokenize(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.encode(UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.encode(UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_rust_tokenizer()
							_UpperCAmelCase									=			tokenizer.encode(UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.encode(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase       ,    keep_accents=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('This is a test'    )
							self.assertListEqual(UpperCAmelCase       ,    ['▁this', '▁is', '▁a', '▁test']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [48, 25, 21, 1289]    )

							_UpperCAmelCase									=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
							self.assertListEqual(
							    UpperCAmelCase       ,    ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']    )
							_UpperCAmelCase									=			tokenizer.convert_tokens_to_ids(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

							_UpperCAmelCase									=			tokenizer.convert_ids_to_tokens(UpperCAmelCase    )
							self.assertListEqual(
							    UpperCAmelCase       ,    ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.encode('sequence builders'    )
							_UpperCAmelCase									=			tokenizer.encode('multi-sequence build'    )

							_UpperCAmelCase									=			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase    )
							_UpperCAmelCase									=			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase       ,    UpperCAmelCase    )

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
							    tokenizer.sep_token_id
							]

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=UpperCAmelCase       ,    model_name='albert-base-v2'       ,    revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'       ,    )
