





import argparse
import os

import evaluate
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a__: str							   =						16
a__: int							   =						32


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			List[str] = 16    )->Optional[Any]:
       A__		   =		AutoTokenizer.from_pretrained('''bert-base-cased'''    )
       A__		   =		load_dataset('''glue'''					,	'''mrpc'''    )

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

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              A__		   =		datasets.map(
                  _A					,	batched=_A					,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']					,	)

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

       def collate_fn(UpperCamelCase__     :			Optional[int]    ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              A__		   =		1_28 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     A__		   =		16
              elif accelerator.mixed_precision != "no":
                     A__		   =		8
              else:
                     A__		   =		None

              return tokenizer.pad(
                  _A					,	padding='''longest'''					,	max_length=_A					,	pad_to_multiple_of=_A					,	return_tensors='''pt'''					,	)

       # Instantiate dataloaders.
       A__		   =		DataLoader(
           tokenized_datasets['''train''']					,	shuffle=_A					,	collate_fn=_A					,	batch_size=_A    )
       A__		   =		DataLoader(
           tokenized_datasets['''validation''']					,	shuffle=_A					,	collate_fn=_A					,	batch_size=_A    )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       a__: str							   =						mocked_dataloaders  # noqa: F811


def       UpperCamelCase__(			UpperCamelCase__     :			Tuple					,	UpperCamelCase__     :			Dict    )->Optional[Any]:
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,	_A    ) == "1":
              A__		   =		2
       # New Code #
       A__		   =		int(args.gradient_accumulation_steps    )
       # Initialize accelerator
       A__		   =		Accelerator(
           cpu=args.cpu					,	mixed_precision=args.mixed_precision					,	gradient_accumulation_steps=_A    )
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''    )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A__		   =		config["""lr"""]
       A__		   =		int(config['''num_epochs''']    )
       A__		   =		int(config['''seed''']    )
       A__		   =		int(config['''batch_size''']    )

       A__		   =		evaluate.load('''glue'''					,	'''mrpc'''    )

       set_seed(_A    )
       A__		   =		get_dataloaders(_A					,	_A    )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A__		   =		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,	return_dict=_A    )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       A__		   =		model.to(accelerator.device    )

       # Instantiate optimizer
       A__		   =		AdamW(params=model.parameters()					,	lr=_A    )

       # Instantiate scheduler
       A__		   =		get_linear_schedule_with_warmup(
           optimizer=_A					,	num_warmup_steps=1_00					,	num_training_steps=(len(_A    ) * num_epochs)					,	)

       # 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__		   =		accelerator.prepare(
           _A					,	_A					,	_A					,	_A					,	_A    )

       # Now we train the model
       for epoch in range(_A    ):
              model.train()
              for step, batch in enumerate(_A    ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device    )
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(_A    ):
                            A__		   =		model(**_A    )
                            A__		   =		output.loss
                            accelerator.backward(_A    )
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(_A    ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device    )
                     with torch.no_grad():
                            A__		   =		model(**_A    )
                     A__		   =		outputs.logits.argmax(dim=-1    )
                     A__		   =		accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
                     metric.add_batch(
                         predictions=_A					,	references=_A					,	)

              A__		   =		metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:"					,	_A    )


def       UpperCamelCase__(			)->Tuple:
       A__		   =		argparse.ArgumentParser(description='''Simple example of training script.'''    )
       parser.add_argument(
           '''--mixed_precision'''					,	type=_A					,	default=_A					,	choices=['''no''', '''fp16''', '''bf16''', '''fp8''']					,	help='''Whether to use mixed precision. Choose'''
           '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
           '''and an Nvidia Ampere GPU.'''					,	)
       # New Code #
       parser.add_argument(
           '''--gradient_accumulation_steps'''					,	type=_A					,	default=1					,	help='''The number of minibatches to be ran before gradients are accumulated.'''					,	)
       parser.add_argument('''--cpu'''					,	action='''store_true'''					,	help='''If passed, will train on the CPU.'''    )
       A__		   =		parser.parse_args()
       A__		   =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(_A					,	_A    )


if __name__ == "__main__":
       main()













"""simple docstring"""







import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase            =	get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		XLMProphetNetTokenizer
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		False
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		True






						def      __snake_case      (		self  :Optional[int]				)      ->List[str]:
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowercase    :      List[str]       = XLMProphetNetTokenizer(__magic_name__			, keep_accents=__magic_name__				)
										tokenizer.save_pretrained(self.tmpdirname				)






						def      __snake_case      (		self  :int				)      ->Any:
										lowercase    :      Dict       = """[PAD]"""
										lowercase    :      Tuple       = 0

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






						def      __snake_case      (		self  :Any				)      ->Union[str, Any]:
										lowercase    :      Optional[int]       = list(self.get_tokenizer().get_vocab().keys()				)

										self.assertEqual(vocab_keys[0]			, """[PAD]"""				)
										self.assertEqual(vocab_keys[1]			, """[CLS]"""				)
										self.assertEqual(vocab_keys[-1]			, """j"""				)
										self.assertEqual(len(__magic_name__				)			, 1_012				)






						def      __snake_case      (		self  :int				)      ->int:
										self.assertEqual(self.get_tokenizer().vocab_size			, 1_012				)






						def      __snake_case      (		self  :Optional[Any]				)      ->List[Any]:
										lowercase    :      Optional[Any]       = XLMProphetNetTokenizer(__magic_name__			, keep_accents=__magic_name__				)

										lowercase    :      Union[str, Any]       = tokenizer.tokenize("""This is a test"""				)
										self.assertListEqual(__magic_name__			, ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__magic_name__				)			, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			, )

										lowercase    :      Optional[Any]       = tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
										self.assertListEqual(
										    __magic_name__			, [
										        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""",
										        """.""",
										    ]			, )
										lowercase    :      Optional[int]       = tokenizer.convert_tokens_to_ids(__magic_name__				)
										self.assertListEqual(
										    __magic_name__			, [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
										    ]			, )

										lowercase    :      Union[str, Any]       = tokenizer.convert_ids_to_tokens(__magic_name__				)
										self.assertListEqual(
										    __magic_name__			, [
										        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  :List[Any]				)      ->str:
										return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""				)






						@slow
						def      __snake_case      (		self  :int				)      ->Optional[Any]:
										lowercase    :      str       = """Hello World!"""
										lowercase    :      Optional[Any]       = [35_389, 6_672, 49, 2]
										self.assertListEqual(__magic_name__			, self.big_tokenizer.encode(__magic_name__				)				)






						@slow
						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										# fmt: off
										lowercase    :      Optional[int]       = {"""input_ids""": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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], [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, 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=__magic_name__			, model_name="""microsoft/xprophetnet-large-wiki100-cased"""			, revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""			, )
