




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


__A        =    16
__A        =    32







def 			lowerCamelCase_		(       UpperCamelCase__				:      Any      )     ->       List[str]:





				"""simple docstring"""


				return int(x / 2**20      )







class     __lowerCAmelCase :

			"""simple docstring"""







			def __enter__(      self							)    ->      int:



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






			def __exit__(      self			,						*lowerCamelCase__							)    ->      Dict:



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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Accelerator ,     UpperCamelCase__				:      int = 16 ,     UpperCamelCase__				:      str = "bert-base-cased" ,     UpperCamelCase__				:      int = 320 ,     UpperCamelCase__				:      int = 160 ,     )     ->       List[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(UpperCamelCase__      )
				__lowerCamelCase		       =    load_dataset(
				    'glue' ,     'mrpc' ,     split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}      )

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

				def collate_fn(UpperCamelCase__				:      Dict      ):
								# 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.
				__lowerCamelCase		       =    DataLoader(
				    tokenized_datasets['train'] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__      )
				__lowerCamelCase		       =    DataLoader(
				    tokenized_datasets['validation'] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__      )

				return train_dataloader, eval_dataloader







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    Accelerator()

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

				set_seed(UpperCamelCase__      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    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)
				__lowerCamelCase		       =    AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__ ,     return_dict=UpperCamelCase__      )

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

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

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

				# Now we train the model
				__lowerCamelCase		       =    {}
				for epoch in range(UpperCamelCase__ ,     UpperCamelCase__      ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(UpperCamelCase__      ):
																__lowerCamelCase		       =    model(**UpperCamelCase__      )
																__lowerCamelCase		       =    outputs.loss
																__lowerCamelCase		       =    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      )      )      )
								__lowerCamelCase		       =    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 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    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.' ,     )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
				training_function(UpperCamelCase__ ,     UpperCamelCase__      )


if __name__ == "__main__":
					main()









"""simple docstring"""





import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__					=   "▁"

SCREAMING_SNAKE_CASE__					=   get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class 		lowercase					(					_UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      BigBirdTokenizer
      _SCREAMING_SNAKE_CASE					=      BigBirdTokenizerFast
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      True





      def     _snake_case			(       self )		->     List[str]:
            super().setUp()

            lowerCAmelCase  						=       self.tokenizer_class(lowercase      ,						keep_accents=lowercase )
            tokenizer.save_pretrained(self.tmpdirname )





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       """<s>"""
            lowerCAmelCase  						=       1

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





      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       list(self.get_tokenizer().get_vocab().keys() )

            self.assertEqual(vocab_keys[0]      ,						"""<unk>""" )
            self.assertEqual(vocab_keys[1]      ,						"""<s>""" )
            self.assertEqual(vocab_keys[-1]      ,						"""[MASK]""" )
            self.assertEqual(len(lowercase )      ,						1_004 )





      def     _snake_case			(       self )		->     Union[str, Any]:
            self.assertEqual(self.get_tokenizer().vocab_size      ,						1_000 )





      def     _snake_case			(       self )		->     List[str]:
            if not self.test_rust_tokenizer:
                  return

            lowerCAmelCase  						=       self.get_tokenizer()
            lowerCAmelCase  						=       self.get_rust_tokenizer()

            lowerCAmelCase  						=       """I was born in 92000, and this is falsé."""

            lowerCAmelCase  						=       tokenizer.tokenize(lowercase )
            lowerCAmelCase  						=       rust_tokenizer.tokenize(lowercase )
            self.assertListEqual(lowercase      ,						lowercase )

            lowerCAmelCase  						=       tokenizer.encode(lowercase      ,						add_special_tokens=lowercase )
            lowerCAmelCase  						=       rust_tokenizer.encode(lowercase      ,						add_special_tokens=lowercase )
            self.assertListEqual(lowercase      ,						lowercase )

            lowerCAmelCase  						=       self.get_rust_tokenizer()
            lowerCAmelCase  						=       tokenizer.encode(lowercase )
            lowerCAmelCase  						=       rust_tokenizer.encode(lowercase )
            self.assertListEqual(lowercase      ,						lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       BigBirdTokenizer(lowercase      ,						keep_accents=lowercase )

            lowerCAmelCase  						=       tokenizer.tokenize("""This is a test""" )
            self.assertListEqual(lowercase      ,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(lowercase )      ,						[285, 46, 10, 170, 382]      ,						)

            lowerCAmelCase  						=       tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
            self.assertListEqual(
                lowercase      ,						[
                    SPIECE_UNDERLINE + """I""",
                    SPIECE_UNDERLINE + """was""",
                    SPIECE_UNDERLINE + """b""",
                    """or""",
                    """n""",
                    SPIECE_UNDERLINE + """in""",
                    SPIECE_UNDERLINE + """""",
                    """9""",
                    """2""",
                    """0""",
                    """0""",
                    """0""",
                    """,""",
                    SPIECE_UNDERLINE + """and""",
                    SPIECE_UNDERLINE + """this""",
                    SPIECE_UNDERLINE + """is""",
                    SPIECE_UNDERLINE + """f""",
                    """al""",
                    """s""",
                    """é""",
                    """.""",
                ]      ,						)
            lowerCAmelCase  						=       tokenizer.convert_tokens_to_ids(lowercase )
            self.assertListEqual(
                lowercase      ,						[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,						)

            lowerCAmelCase  						=       tokenizer.convert_ids_to_tokens(lowercase )
            self.assertListEqual(
                lowercase      ,						[
                    SPIECE_UNDERLINE + """I""",
                    SPIECE_UNDERLINE + """was""",
                    SPIECE_UNDERLINE + """b""",
                    """or""",
                    """n""",
                    SPIECE_UNDERLINE + """in""",
                    SPIECE_UNDERLINE + """""",
                    """<unk>""",
                    """2""",
                    """0""",
                    """0""",
                    """0""",
                    """,""",
                    SPIECE_UNDERLINE + """and""",
                    SPIECE_UNDERLINE + """this""",
                    SPIECE_UNDERLINE + """is""",
                    SPIECE_UNDERLINE + """f""",
                    """al""",
                    """s""",
                    """<unk>""",
                    """.""",
                ]      ,						)





      @cached_property
      def     _snake_case			(       self )		->     Tuple:
            return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""" )





      @slow
      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       """Hello World!"""
            lowerCAmelCase  						=       [65, 18_536, 2_260, 101, 66]

            self.assertListEqual(lowercase      ,						self.big_tokenizer.encode(lowercase ) )





      @slow
      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       (
                """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
                """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
            )
            # fmt: off
            lowerCAmelCase  						=       [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
            # fmt: on
            self.assertListEqual(lowercase      ,						self.big_tokenizer.encode(lowercase ) )





      @require_torch
      @slow
      def     _snake_case			(       self )		->     Tuple:
            import torch

            from transformers import BigBirdConfig, BigBirdModel

            # Build sequence
            lowerCAmelCase  						=       list(self.big_tokenizer.get_vocab().keys() )[:10]
            lowerCAmelCase  						=       """ """.join(lowercase )
            lowerCAmelCase  						=       self.big_tokenizer.encode_plus(lowercase      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowercase )
            lowerCAmelCase  						=       self.big_tokenizer.batch_encode_plus(
                [sequence + """ """ + sequence]      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowercase )

            lowerCAmelCase  						=       BigBirdConfig(attention_type="""original_full""" )
            lowerCAmelCase  						=       BigBirdModel(lowercase )

            assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

            with torch.no_grad():
                  model(**lowercase )
                  model(**lowercase )





      @slow
      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""" )
            lowerCAmelCase  						=       tokenizer.decode(tokenizer("""Paris is the [MASK].""" ).input_ids )

            self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]""" )





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            # fmt: off
            lowerCAmelCase  						=       {"""input_ids""": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=lowercase      ,						model_name="""google/bigbird-roberta-base"""      ,						revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""      ,						)






