


from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

_lowercase       :      Any	=	_symbol_database.Default()


_lowercase       :      Tuple	=	_descriptor_pool.Default().AddSerializedFile(
    b"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)

_lowercase       :      Tuple	=	globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
				_lowercase       :      Tuple	=	None
				_lowercase       :      Tuple	=	b"H\003"
				# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
				# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
				# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
				# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
				# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
				_lowercase       :      Union[str, Any]	=	45
				_lowercase       :      Optional[Any]	=	1581
				_lowercase       :      Optional[int]	=	1517
				_lowercase       :      List[str]	=	1570
				_lowercase       :      int	=	1584
				_lowercase       :      Optional[Any]	=	1793
				_lowercase       :      Union[str, Any]	=	1795
				_lowercase       :      str	=	1916
				_lowercase       :      Optional[Any]	=	1864
				_lowercase       :      str	=	1905
				_lowercase       :      Any	=	1919
				_lowercase       :      str	=	2429
				_lowercase       :      Dict	=	2208
				_lowercase       :      Optional[Any]	=	2418
				_lowercase       :      List[str]	=	2323
				_lowercase       :      Any	=	2407
# @@protoc_insertion_point(module_scope)







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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


_lowercase       :      Optional[Any]	=	16
_lowercase       :      List[Any]	=	32





def   _lowerCAmelCase				(   UpperCamelCase__:     Accelerator							,					UpperCamelCase__:     int = 16						) ->   Tuple:





	"""simple docstring"""







	A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
	A								=				load_dataset("""glue"""							,					"""mrpc"""						)

	def tokenize_function(UpperCamelCase__:     List[Any]						):
		# max_length=None => use the model max length (it's actually the default)
		A								=				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
	# starting with the main process first:
	with accelerator.main_process_first():
		A								=				datasets.map(
		    UpperCamelCase__							,					batched=UpperCamelCase__							,					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__:     Union[str, Any]						):
		# 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(
		    UpperCamelCase__							,					padding="""longest"""							,					max_length=UpperCamelCase__							,					pad_to_multiple_of=UpperCamelCase__							,					return_tensors="""pt"""							,					)

	# Instantiate dataloaders.
	A								=				DataLoader(
	    tokenized_datasets["""train"""]							,					shuffle=UpperCamelCase__							,					collate_fn=UpperCamelCase__							,					batch_size=UpperCamelCase__						)
	A								=				DataLoader(
	    tokenized_datasets["""validation"""]							,					shuffle=UpperCamelCase__							,					collate_fn=UpperCamelCase__							,					batch_size=UpperCamelCase__						)

	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

				_lowercase       :      List[Any]	=	mocked_dataloaders  # noqa: F811





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,					UpperCamelCase__						) == "1":
		A								=				2
	# Initialize accelerator
	A								=				Accelerator(cpu=args.cpu							,					mixed_precision=args.mixed_precision						)
	# 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"""						)

	# If the batch size is too big we use gradient accumulation
	A								=				1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A								=				batch_size // MAX_GPU_BATCH_SIZE
		A								=				MAX_GPU_BATCH_SIZE

	set_seed(UpperCamelCase__						)
	A					,			A								=				get_dataloaders(UpperCamelCase__							,					UpperCamelCase__						)
	# 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=UpperCamelCase__						)

	# 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=UpperCamelCase__						)

	# Instantiate scheduler
	A								=				get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__							,					num_warmup_steps=1_00							,					num_training_steps=(len(UpperCamelCase__						) * num_epochs) // gradient_accumulation_steps							,					)

	# 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								=				accelerator.prepare(
	    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)

	# Now we train the model
	for epoch in range(UpperCamelCase__						):
		model.train()
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			A								=				model(**UpperCamelCase__						)
			A								=				outputs.loss
			A								=				loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase__						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		A								=				0
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			with torch.no_grad():
				A								=				model(**UpperCamelCase__						)
			A								=				outputs.logits.argmax(dim=-1						)
			A					,			A								=				accelerator.gather((predictions, batch["""labels"""])						)
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(UpperCamelCase__						) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					A								=				predictions[: len(eval_dataloader.dataset						) - samples_seen]
					A								=				references[: len(eval_dataloader.dataset						) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=UpperCamelCase__							,					references=UpperCamelCase__							,					)

		A								=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   ) ->   Dict:





	"""simple docstring"""







	A								=				argparse.ArgumentParser(description="""Simple example of training script."""						)
	parser.add_argument(
	    """--mixed_precision"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					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."""						)
	A								=				parser.parse_args()
	A								=				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(UpperCamelCase__							,					UpperCamelCase__						)


if __name__ == "__main__":
				main()



