


'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				 =	{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	['''ConvBertTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ConvBertForMaskedLM''',
		    '''ConvBertForMultipleChoice''',
		    '''ConvBertForQuestionAnswering''',
		    '''ConvBertForSequenceClassification''',
		    '''ConvBertForTokenClassification''',
		    '''ConvBertLayer''',
		    '''ConvBertModel''',
		    '''ConvBertPreTrainedModel''',
		    '''load_tf_weights_in_convbert''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFConvBertForMaskedLM''',
		    '''TFConvBertForMultipleChoice''',
		    '''TFConvBertForQuestionAnswering''',
		    '''TFConvBertForSequenceClassification''',
		    '''TFConvBertForTokenClassification''',
		    '''TFConvBertLayer''',
		    '''TFConvBertModel''',
		    '''TFConvBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
		from .tokenization_convbert import ConvBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_convbert_fast import ConvBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convbert import (
				    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvBertForMaskedLM,
				    ConvBertForMultipleChoice,
				    ConvBertForQuestionAnswering,
				    ConvBertForSequenceClassification,
				    ConvBertForTokenClassification,
				    ConvBertLayer,
				    ConvBertModel,
				    ConvBertPreTrainedModel,
				    load_tf_weights_in_convbert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convbert import (
				    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertLayer,
				    TFConvBertModel,
				    TFConvBertPreTrainedModel,
				)


else:
		import sys

		__snake_case				 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''
import argparse

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
#
# 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
#
########################################################################


__snake_case				 =	16
__snake_case				 =	32


def      a (     __a					,   __a = 16  )    ->      Dict:



					'''simple docstring'''






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

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

					def collate_fn(__a  ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCamelCase__  :Union[str, Any]				   =					128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															UpperCamelCase__  :str				   =					16
										elif accelerator.mixed_precision != "no":
															UpperCamelCase__  :Tuple				   =					8
										else:
															UpperCamelCase__  :int				   =					None

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

					# Instantiate dataloaders.
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''train''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=__a  )
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''validation''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=(accelerator.mixed_precision == '''fp8''')					,   )

					return train_dataloader, eval_dataloader


def      a (     __a					,   __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCamelCase__  :List[Any]				   =					config['''lr''']
					UpperCamelCase__  :List[str]				   =					int(config['''num_epochs''']  )
					UpperCamelCase__  :int				   =					int(config['''seed''']  )
					UpperCamelCase__  :str				   =					int(config['''batch_size''']  )

					UpperCamelCase__  :List[str]				   =					evaluate.load('''glue'''					,   '''mrpc'''  )

					# If the batch size is too big we use gradient accumulation
					UpperCamelCase__  :Tuple				   =					1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										UpperCamelCase__  :Optional[Any]				   =					batch_size // MAX_GPU_BATCH_SIZE
										UpperCamelCase__  :Dict				   =					MAX_GPU_BATCH_SIZE

					set_seed(__a  )
					UpperCamelCase__   ,							UpperCamelCase__  :Tuple				   =					get_dataloaders(__a					,   __a  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__  :List[Any]				   =					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).
					UpperCamelCase__  :Optional[int]				   =					model.to(accelerator.device  )
					# Instantiate optimizer
					UpperCamelCase__  :Union[str, Any]				   =					AdamW(params=model.parameters()					,   lr=__a  )

					# Instantiate scheduler
					UpperCamelCase__  :Optional[Any]				   =					get_linear_schedule_with_warmup(
					    optimizer=__a					,   num_warmup_steps=100					,   num_training_steps=(len(__a  ) * 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.

					UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__  :str				   =					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  )
															UpperCamelCase__  :Dict				   =					model(**__a  )
															UpperCamelCase__  :Union[str, Any]				   =					outputs.loss
															UpperCamelCase__  :Tuple				   =					loss / gradient_accumulation_steps
															accelerator.backward(__a  )
															if step % gradient_accumulation_steps == 0:
																				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():
																				UpperCamelCase__  :Optional[int]				   =					model(**__a  )
															UpperCamelCase__  :int				   =					outputs.logits.argmax(dim=-1  )
															UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
															metric.add_batch(
															    predictions=__a					,   references=__a					,   )

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


def      a (     )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					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.'''					,   )
					parser.add_argument('''--cpu'''					,   action='''store_true'''					,   help='''If passed, will train on the CPU.'''  )
					UpperCamelCase__  :int				   =					parser.parse_args()
					UpperCamelCase__  :int				   =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__a					,   __a  )


if __name__ == "__main__":
		main()