



"""simple docstring"""

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
# and perform gradient accumulation
#
# 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__    :  Dict							    =	16
lowercase__    :  Any							    =	32




def __lowercase			(							_a		,			_a = 16    ):
    snake_case_ :     Union[str, Any]				=	AutoTokenizer.from_pretrained('''bert-base-cased'''    )
    snake_case_ :     int				=	load_dataset('''glue'''		,			'''mrpc'''    )

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

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

    # Instantiate dataloaders.
    snake_case_ :     str				=	DataLoader(
        tokenized_datasets['''train''']		,			shuffle=_a		,			collate_fn=_a		,			batch_size=_a    )
    snake_case_ :     str				=	DataLoader(
        tokenized_datasets['''validation''']		,			shuffle=_a		,			collate_fn=_a		,			batch_size=_a    )

    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__    :  str							    =	mocked_dataloaders  # noqa: F811




def __lowercase			(							_a		,			_a    ):
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,			_a    ) == "1":
        snake_case_ :     List[Any]				=	2
    # New Code #
    snake_case_ :     Tuple				=	int(args.gradient_accumulation_steps    )
    # Initialize accelerator
    snake_case_ :     Dict				=	Accelerator(
        cpu=args.cpu		,			mixed_precision=args.mixed_precision		,			gradient_accumulation_steps=_a    )
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case_ :     str				=	config['''lr''']
    snake_case_ :     List[str]				=	int(config['''num_epochs''']    )
    snake_case_ :     Optional[int]				=	int(config['''seed''']    )
    snake_case_ :     str				=	int(config['''batch_size''']    )

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

    set_seed(_a    )
    snake_case_,	snake_case_ :     str				=	get_dataloaders(_a		,			_a    )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case_ :     Optional[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).
    snake_case_ :     str				=	model.to(accelerator.device    )

    # Instantiate optimizer
    snake_case_ :     Union[str, Any]				=	AdamW(params=model.parameters()		,			lr=_a    )

    # Instantiate scheduler
    snake_case_ :     Tuple				=	get_linear_schedule_with_warmup(
        optimizer=_a		,			num_warmup_steps=100		,			num_training_steps=(len(_a    ) * 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.
    snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_ :     Any				=	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    )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(_a    ):
                snake_case_ :     Optional[int]				=	model(**_a    )
                snake_case_ :     Dict				=	output.loss
                accelerator.backward(_a    )
                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():
                snake_case_ :     List[str]				=	model(**_a    )
            snake_case_ :     Tuple				=	outputs.logits.argmax(dim=-1    )
            snake_case_,	snake_case_ :     List[Any]				=	accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
            metric.add_batch(
                predictions=_a		,			references=_a		,			)

        snake_case_ :     Tuple				=	metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}:"		,			_a    )




def __lowercase			(							):
    snake_case_ :     List[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.'''		,			)
    # New Code #
    parser.add_argument(
        '''--gradient_accumulation_steps'''		,			type=_a		,			default=1		,			help='''The number of minibatches to be ran before gradients are accumulated.'''		,			)
    parser.add_argument('''--cpu'''		,			action='''store_true'''		,			help='''If passed, will train on the CPU.'''    )
    snake_case_ :     Optional[int]				=	parser.parse_args()
    snake_case_ :     List[str]				=	{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(_a		,			_a    )


if __name__ == "__main__":
     main()










"""simple docstring"""

import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf

if is_vision_available():
     from PIL import Image




def __lowercase			(							_a=""    ):
    snake_case_ :     List[str]				=	tempfile.mkdtemp()
    return os.path.join(_a		,			str(uuid.uuida()    ) + suffix    )




@require_soundfile
@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     int				=	torch.rand(12  ,     dtype=torch.floataa				) - 0.5
     snake_case_ :     Optional[int]				=	AgentAudio(lowercase_				)
     snake_case_ :     List[str]				=	str(agent_type.to_string()				)

     # Ensure that the tensor and the agent_type's tensor are the same
     self.assertTrue(torch.allclose(lowercase_  ,     agent_type.to_raw()  ,     atol=1E-4				)				)

     del agent_type

     # Ensure the path remains even after the object deletion
     self.assertTrue(os.path.exists(lowercase_				)				)

     # Ensure that the file contains the same value as the original tensor
     snake_case_,	snake_case_ :     int				=	sf.read(lowercase_				)
     self.assertTrue(torch.allclose(lowercase_  ,     torch.tensor(lowercase_				)  ,     atol=1E-4				)				)






 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Any				=	torch.rand(12  ,     dtype=torch.floataa				) - 0.5
     snake_case_ :     List[str]				=	get_new_path(suffix='''.wav'''				)
     sf.write(lowercase_  ,     lowercase_  ,     16000				)

     snake_case_ :     Tuple				=	AgentAudio(lowercase_				)

     self.assertTrue(torch.allclose(lowercase_  ,     agent_type.to_raw()  ,     atol=1E-4				)				)
     self.assertEqual(agent_type.to_string()  ,     lowercase_				)





@require_vision
@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     List[Any]				=	torch.randint(0  ,     256  ,     (64, 64, 3)				)
     snake_case_ :     str				=	AgentImage(lowercase_				)
     snake_case_ :     Union[str, Any]				=	str(agent_type.to_string()				)

     # Ensure that the tensor and the agent_type's tensor are the same
     self.assertTrue(torch.allclose(lowercase_  ,     agent_type._tensor  ,     atol=1E-4				)				)

     self.assertIsInstance(agent_type.to_raw()  ,     Image.Image				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     Any				=	Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
     snake_case_ :     Optional[int]				=	Image.open(lowercase_				)
     snake_case_ :     Tuple				=	AgentImage(lowercase_				)

     self.assertTrue(path.samefile(agent_type.to_string()				)				)
     self.assertTrue(image == agent_type.to_raw()				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)






 def 						_snake_case					(     self		:       str				):
     snake_case_ :     int				=	Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
     snake_case_ :     Dict				=	Image.open(lowercase_				)
     snake_case_ :     List[str]				=	AgentImage(lowercase_				)

     self.assertFalse(path.samefile(agent_type.to_string()				)				)
     self.assertTrue(image == agent_type.to_raw()				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)





class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     Tuple				=	'''Hey!'''
     snake_case_ :     Optional[Any]				=	AgentText(lowercase_				)

     self.assertEqual(lowercase_  ,     agent_type.to_string()				)
     self.assertEqual(lowercase_  ,     agent_type.to_raw()				)
     self.assertEqual(lowercase_  ,     lowercase_				)





