




'''simple docstring'''







import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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


@require_sentencepiece
@require_tokenizers
class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     int   				=					XGLMTokenizer
       __a				:     Any   				=					XGLMTokenizerFast
       __a				:     Any   				=					True
       __a				:     Tuple   				=					True
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           super().setUp()

           # We have a SentencePiece fixture for testing
           UpperCAmelCase			       =		XGLMTokenizer(lowercase   ,				keep_accents=lowercase				)
           tokenizer.save_pretrained(self.tmpdirname				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		'''<pad>'''
           UpperCAmelCase			       =		1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase				)   ,				lowercase				)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase				)   ,				lowercase				)
       def     A      (	self						:	str				):



           '''simple docstring'''

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

           self.assertEqual(vocab_keys[0]   ,				'''<s>'''				)
           self.assertEqual(vocab_keys[1]   ,				'''<pad>'''				)
           self.assertEqual(len(lowercase				)   ,				1_008				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size   ,				1_008				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		XGLMTokenizer(lowercase   ,				keep_accents=lowercase				)

           UpperCAmelCase			       =		tokenizer.tokenize('''This is a test'''				)
           self.assertListEqual(lowercase   ,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

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

           UpperCAmelCase			       =		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''',
                   '''.''',
               ]   ,				)
           UpperCAmelCase			       =		tokenizer.convert_tokens_to_ids(lowercase				)
           self.assertListEqual(
               lowercase   ,				[
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               ]   ,				)

           UpperCAmelCase			       =		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     A      (	self						:	Any				):



           '''simple docstring'''

           return XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           with tempfile.NamedTemporaryFile() as f:
               shutil.copyfile(lowercase   ,				f.name				)
               UpperCAmelCase			       =		XGLMTokenizer(f.name   ,				keep_accents=lowercase				)
               UpperCAmelCase			       =		pickle.dumps(lowercase				)
           pickle.loads(lowercase				)
       def     A      (	self						:	List[str]				):



           '''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(lowercase				)
           UpperCAmelCase			       =		rust_tokenizer.tokenize(lowercase				)
           self.assertListEqual(lowercase   ,				lowercase				)

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

           UpperCAmelCase			       =		self.get_rust_tokenizer()
           UpperCAmelCase			       =		tokenizer.encode(lowercase				)
           UpperCAmelCase			       =		rust_tokenizer.encode(lowercase				)
           self.assertListEqual(lowercase   ,				lowercase				)
       @slow
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		'''Hello World!'''
           UpperCAmelCase			       =		[2, 31_227, 4_447, 35]

           self.assertListEqual(lowercase   ,				self.big_tokenizer.encode(lowercase				)				)
       @slow
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		(
               '''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
           UpperCAmelCase			       =		[2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
           # fmt: on

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







       @slow
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		{
               '''input_ids''': [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
               '''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, 1, 1, 1, 1, 1]]
           }  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=lowercase   ,				model_name='''facebook/xglm-564M'''   ,				padding=lowercase   ,				)









"""simple docstring"""







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


lowerCAmelCase_   						=   16
lowerCAmelCase_   						=   32



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = 16	)							->      Optional[Any]:
		lowercase__   :    Optional[Any]				=			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
		lowercase__   :    int				=			load_dataset('''glue'''						,     '''mrpc'''	)

		def tokenize_function(__lowerCamelCase	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__   :    str				=			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
		# starting with the main process first:
		with accelerator.main_process_first():
				lowercase__   :    str				=			datasets.map(
				    __lowerCamelCase						,     batched=__lowerCamelCase						,     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
		lowercase__   :    Union[str, Any]				=			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.
				lowercase__   :    List[str]				=			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":
						lowercase__   :    Optional[int]				=			16
				elif accelerator.mixed_precision != "no":
						lowercase__   :    List[Any]				=			8
				else:
						lowercase__   :    int				=			None

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

		# Instantiate dataloaders.
		lowercase__   :    List[Any]				=			DataLoader(
		    tokenized_datasets['''train''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)
		lowercase__   :    str				=			DataLoader(
		    tokenized_datasets['''validation''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)

		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

							lowerCAmelCase_   						=   mocked_dataloaders  # noqa: F811



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,     __lowerCamelCase	) == "1":
				lowercase__   :    List[Any]				=			2
		# Initialize accelerator
		lowercase__   :    Optional[int]				=			Accelerator(cpu=args.cpu						,     mixed_precision=args.mixed_precision	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__   :    str				=			config['''lr''']
		lowercase__   :    str				=			int(config['''num_epochs''']	)
		lowercase__   :    Optional[int]				=			int(config['''seed''']	)
		lowercase__   :    Tuple				=			int(config['''batch_size''']	)

		lowercase__   :    List[Any]				=			evaluate.load('''glue'''						,     '''mrpc'''	)

		# New Code #
		# We now can define an inner training loop function. It should take a batch size as the only parameter,
		# and build the dataloaders in there.
		# It also gets our decorator
		@find_executable_batch_size(starting_batch_size=__lowerCamelCase	)
		def inner_training_loop(__lowerCamelCase	):
				# And now just move everything below under this function
				# We need to bring in the Accelerator object from earlier
				nonlocal accelerator
				# And reset all of its attributes that could hold onto any memory:
				accelerator.free_memory()

				# Then we can declare the model, optimizer, and everything else:
				set_seed(__lowerCamelCase	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowercase__   :    List[str]				=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,     return_dict=__lowerCamelCase	)

				# 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).
				lowercase__   :    Tuple				=			model.to(accelerator.device	)

				# Instantiate optimizer
				lowercase__   :    List[str]				=			AdamW(params=model.parameters()						,     lr=__lowerCamelCase	)
				lowercase__		,			lowercase__   :    List[Any]				=			get_dataloaders(__lowerCamelCase						,     __lowerCamelCase	)

				# Instantiate scheduler
				lowercase__   :    Optional[int]				=			get_linear_schedule_with_warmup(
				    optimizer=__lowerCamelCase						,     num_warmup_steps=1_00						,     num_training_steps=(len(__lowerCamelCase	) * 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.
				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Optional[int]				=			accelerator.prepare(
				    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)

				# Now we train the model
				for epoch in range(__lowerCamelCase	):
						model.train()
						for step, batch in enumerate(__lowerCamelCase	):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device	)
								lowercase__   :    Dict				=			model(**__lowerCamelCase	)
								lowercase__   :    List[Any]				=			outputs.loss
								accelerator.backward(__lowerCamelCase	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

						lowercase__   :    List[Any]				=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""						,     __lowerCamelCase	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
		inner_training_loop()



def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    Optional[int]				=			argparse.ArgumentParser(description='''Simple example of training script.'''	)
		parser.add_argument(
		    '''--mixed_precision'''						,     type=__lowerCamelCase						,     default=__lowerCamelCase						,     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.'''						,     )
		parser.add_argument('''--cpu'''						,     action='''store_true'''						,     help='''If passed, will train on the CPU.'''	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Union[str, Any]				=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__lowerCamelCase						,     __lowerCamelCase	)


if __name__ == "__main__":
							main()
