
'''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
#
########################################################################


lowercase			:				Optional[int]    		=			16
lowercase			:				Optional[Any]    		=			32







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__ = 16						):




    '''simple docstring'''



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

    def tokenize_function(snake_case__						):
        # max_length=None => use the model max length (it's actually the default)
        A       :   Tuple					=						tokenizer(examples['''sentence1''']		,				examples['''sentence2''']		,				truncation=snake_case__		,				max_length=snake_case__						)
        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       :   Optional[int]					=						datasets.map(
            snake_case__		,				batched=snake_case__		,				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       :   Dict					=						tokenized_datasets.rename_column('''label'''		,				'''labels'''						)

    def collate_fn(snake_case__						):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A       :   Tuple					=						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":
            A       :   List[Any]					=						16
        elif accelerator.mixed_precision != "no":
            A       :   Dict					=						8
        else:
            A       :   Tuple					=						None

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

    # Instantiate dataloaders.
    A       :   Optional[int]					=						DataLoader(
        tokenized_datasets['''train''']		,				shuffle=snake_case__		,				collate_fn=snake_case__		,				batch_size=snake_case__		,				drop_last=snake_case__						)
    A       :   int					=						DataLoader(
        tokenized_datasets['''validation''']		,				shuffle=snake_case__		,				collate_fn=snake_case__		,				batch_size=snake_case__		,				drop_last=(accelerator.mixed_precision == '''fp8''')		,				)

    return train_dataloader, eval_dataloader







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



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

    A       :   Tuple					=						evaluate.load('''glue'''		,				'''mrpc'''						)

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

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

    # 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       :   int					=						model.to(accelerator.device						)
    # Instantiate optimizer
    A       :   List[Any]					=						AdamW(params=model.parameters()		,				lr=snake_case__						)

    # Instantiate scheduler
    A       :   Any					=						get_linear_schedule_with_warmup(
        optimizer=snake_case__		,				num_warmup_steps=100		,				num_training_steps=(len(snake_case__						) * 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       :   Tuple					=						accelerator.prepare(
        snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)

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

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

        A       :   Optional[Any]					=						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'		,				snake_case__						)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   str					=						argparse.ArgumentParser(description='''Simple example of training script.'''						)
    parser.add_argument(
        '''--mixed_precision'''		,				type=snake_case__		,				default=snake_case__		,				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       :   int					=						parser.parse_args()
    A       :   Union[str, Any]					=						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(snake_case__		,				snake_case__						)


if __name__ == "__main__":
      main()










'''simple docstring'''







from ..utils import DummyObject, requires_backends


class 			_a  (	metaclass=__a ):
       __a				:     int   				=					["""flax""", """transformers"""]
       def __init__(	self						:	Optional[Any]   ,				*lowercase						:	str   ,				**lowercase						:	List[Any]				):



           '''simple docstring'''

           requires_backends(self   ,				['''flax''', '''transformers''']				)
       @classmethod
       def     A      (	cls						:	Union[str, Any]   ,				*lowercase						:	List[Any]   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)







       @classmethod
       def     A      (	cls						:	Union[str, Any]   ,				*lowercase						:	Tuple   ,				**lowercase						:	int				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)






class 			_a  (	metaclass=__a ):
       __a				:     int   				=					["""flax""", """transformers"""]
       def __init__(	self						:	int   ,				*lowercase						:	Tuple   ,				**lowercase						:	Dict				):



           '''simple docstring'''

           requires_backends(self   ,				['''flax''', '''transformers''']				)
       @classmethod
       def     A      (	cls						:	List[str]   ,				*lowercase						:	Optional[int]   ,				**lowercase						:	List[Any]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)







       @classmethod
       def     A      (	cls						:	Dict   ,				*lowercase						:	Union[str, Any]   ,				**lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)






class 			_a  (	metaclass=__a ):
       __a				:     int   				=					["""flax""", """transformers"""]
       def __init__(	self						:	Optional[int]   ,				*lowercase						:	Union[str, Any]   ,				**lowercase						:	Any				):



           '''simple docstring'''

           requires_backends(self   ,				['''flax''', '''transformers''']				)
       @classmethod
       def     A      (	cls						:	Union[str, Any]   ,				*lowercase						:	Tuple   ,				**lowercase						:	Any				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)







       @classmethod
       def     A      (	cls						:	Any   ,				*lowercase						:	Dict   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)






class 			_a  (	metaclass=__a ):
       __a				:     Any   				=					["""flax""", """transformers"""]
       def __init__(	self						:	Any   ,				*lowercase						:	Optional[Any]   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           requires_backends(self   ,				['''flax''', '''transformers''']				)
       @classmethod
       def     A      (	cls						:	Dict   ,				*lowercase						:	Optional[Any]   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)







       @classmethod
       def     A      (	cls						:	Union[str, Any]   ,				*lowercase						:	str   ,				**lowercase						:	Any				):



           '''simple docstring'''

           requires_backends(cls   ,				['''flax''', '''transformers''']				)




