


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


lowerCamelCase						=		16
lowerCamelCase						=		32







def 					lowerCamelCase_   (	_a     ,       _a = 16	):




		"""simple docstring"""






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

		def tokenize_function(_a	):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase__       :      Any   					= 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():
				lowerCAmelCase__       :      Dict   					= 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
		lowerCAmelCase__       :      List[Any]   					= 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.
				lowerCAmelCase__       :      Optional[int]   					= 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":
						lowerCAmelCase__       :      str   					= 16
				elif accelerator.mixed_precision != "no":
						lowerCAmelCase__       :      str   					= 8
				else:
						lowerCAmelCase__       :      Dict   					= None

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

		# Instantiate dataloaders.
		lowerCAmelCase__       :      List[str]   					= DataLoader(
		    tokenized_datasets['''train''']     ,       shuffle=lowerCamelCase__     ,       collate_fn=lowerCamelCase__     ,       batch_size=lowerCamelCase__     ,       drop_last=lowerCamelCase__	)
		lowerCAmelCase__       :      str   					= DataLoader(
		    tokenized_datasets['''validation''']     ,       shuffle=lowerCamelCase__     ,       collate_fn=lowerCamelCase__     ,       batch_size=lowerCamelCase__     ,       drop_last=(accelerator.mixed_precision == '''fp8''')     ,       )

		return train_dataloader, eval_dataloader







def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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

		lowerCAmelCase__       :      Optional[Any]   					= evaluate.load('''glue'''     ,       '''mrpc'''	)

		# If the batch size is too big we use gradient accumulation
		lowerCAmelCase__       :      List[Any]   					= 1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				lowerCAmelCase__       :      Union[str, Any]   					= batch_size // MAX_GPU_BATCH_SIZE
				lowerCAmelCase__       :      Any   					= MAX_GPU_BATCH_SIZE

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

		# Instantiate scheduler
		lowerCAmelCase__       :      Tuple   					= get_linear_schedule_with_warmup(
		    optimizer=lowerCamelCase__     ,       num_warmup_steps=100     ,       num_training_steps=(len(lowerCamelCase__	) * 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.

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

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







def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Tuple   					= 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.'''	)
		lowerCAmelCase__       :      int   					= parser.parse_args()
		lowerCAmelCase__       :      Dict   					= {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(lowerCamelCase__     ,       lowerCamelCase__	)


if __name__ == "__main__":
		main()








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class  _a							(    _lowercase):
	_a  :  Dict				   =							'''convbert'''




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str=3_0522		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=12		, _SCREAMING_SNAKE_CASE						:		Any=3072		, _SCREAMING_SNAKE_CASE						:		List[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		int=512		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2		, _SCREAMING_SNAKE_CASE						:		Tuple=9		, _SCREAMING_SNAKE_CASE						:		int=1		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(
			    pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      Tuple   					= num_hidden_layers
			lowerCAmelCase__       :      Union[str, Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Dict   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      Tuple   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= layer_norm_eps
			lowerCAmelCase__       :      int   					= embedding_size
			lowerCAmelCase__       :      Union[str, Any]   					= head_ratio
			lowerCAmelCase__       :      Optional[int]   					= conv_kernel_size
			lowerCAmelCase__       :      List[str]   					= num_groups
			lowerCAmelCase__       :      Dict   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      Optional[Any]   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[str]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			        ('''token_type_ids''', dynamic_axis),
			    ]		)




