

import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowercase     : List[str]       =						16
__lowercase     : List[str]       =						32







def       lowercase					(	__A :					Accelerator ,    __A :					int = 16 ,    __A :					str = "bert-base-cased"		)				->			Any:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		AutoTokenizer.from_pretrained(__A		)
    snake_case							:		Optional[Any]        =		load_dataset("""glue""" ,    """mrpc"""		)

    def tokenize_function(__A :					List[Any]		):
        # max_length=None => use the model max length (it's actually the default)
        snake_case							:		List[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
    snake_case							:		Optional[int]        =		datasets.map(
        __A ,    batched=__A ,    remove_columns=["""idx""", """sentence1""", """sentence2"""] ,    load_from_cache_file=__A		)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case							:		Optional[Any]        =		tokenized_datasets.rename_column("""label""" ,    """labels"""		)

    def collate_fn(__A :					List[Any]		):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(__A ,    padding="""max_length""" ,    max_length=128 ,    return_tensors="""pt"""		)
        return tokenizer.pad(__A ,    padding="""longest""" ,    return_tensors="""pt"""		)

    # Instantiate dataloaders.
    snake_case							:		Dict        =		DataLoader(
        tokenized_datasets["""train"""] ,    shuffle=__A ,    collate_fn=__A ,    batch_size=__A		)
    snake_case							:		Optional[int]        =		DataLoader(
        tokenized_datasets["""validation"""] ,    shuffle=__A ,    collate_fn=__A ,    batch_size=__A		)

    return train_dataloader, eval_dataloader







def       lowercase					(	__A :					Union[str, Any] ,    __A :					Optional[Any]		)				->			str:





    '''simple docstring'''




    snake_case							:		List[str]        =		Accelerator()

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

    set_seed(__A		)
    snake_case      ,		snake_case							:		str        =		get_dataloaders(__A ,    __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(__A ,    return_dict=__A		)

    # Instantiate optimizer
    snake_case							:		Any        =		(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    snake_case							:		str        =		optimizer_cls(params=model.parameters() ,    lr=__A		)

    if accelerator.state.deepspeed_plugin is not None:
        snake_case							:		List[str]        =		accelerator.state.deepspeed_plugin.deepspeed_config[
            """gradient_accumulation_steps"""
        ]
    else:
        snake_case							:		Optional[int]        =		1
    snake_case							:		int        =		(len(__A		) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        snake_case							:		Union[str, Any]        =		get_linear_schedule_with_warmup(
            optimizer=__A ,    num_warmup_steps=0 ,    num_training_steps=__A ,    )
    else:
        snake_case							:		Union[str, Any]        =		DummyScheduler(__A ,    total_num_steps=__A ,    warmup_num_steps=0		)

    # 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							:		Optional[Any]        =		accelerator.prepare(
        __A ,    __A ,    __A ,    __A ,    __A		)

    # We need to keep track of how many total steps we have iterated over
    snake_case							:		Dict        =		0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case							:		int        =		0

    # Now we train the model
    snake_case							:		Union[str, Any]        =		evaluate.load("""glue""" ,    """mrpc"""		)
    snake_case							:		List[str]        =		0
    snake_case							:		Dict        =		{}
    for epoch in range(__A ,    __A		):
        model.train()
        for step, batch in enumerate(__A		):
            snake_case							:		int        =		model(**__A		)
            snake_case							:		Optional[Any]        =		outputs.loss
            snake_case							:		List[str]        =		loss / gradient_accumulation_steps
            accelerator.backward(__A		)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        snake_case							:		Tuple        =		0
        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							:		int        =		model(**__A		)
            snake_case							:		List[Any]        =		outputs.logits.argmax(dim=-1		)
            # It is slightly faster to call this once, than multiple times
            snake_case      ,		snake_case							:		Union[str, Any]        =		accelerator.gather(
                (predictions, batch["""labels"""])		)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(__A		) - 1:
                    snake_case							:		List[Any]        =		predictions[: len(eval_dataloader.dataset		) - samples_seen]
                    snake_case							:		Any        =		references[: len(eval_dataloader.dataset		) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=__A ,    references=__A ,    )

        snake_case							:		List[str]        =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:""" ,    __A		)
        snake_case							:		Any        =		eval_metric["""accuracy"""]

        if best_performance < eval_metric["accuracy"]:
            snake_case							:		Union[str, Any]        =		eval_metric["""accuracy"""]

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir ,    """all_results.json"""		) ,    """w"""		) as f:
            json.dump(__A ,    __A		)







def       lowercase					(	)				->			List[Any]:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""		)
    parser.add_argument(
        """--model_name_or_path""" ,    type=__A ,    default="""bert-base-cased""" ,    help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,    required=__A ,    )
    parser.add_argument(
        """--output_dir""" ,    type=__A ,    default=""".""" ,    help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,    )
    parser.add_argument(
        """--performance_lower_bound""" ,    type=__A ,    default=__A ,    help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" ,    )
    parser.add_argument(
        """--num_epochs""" ,    type=__A ,    default=3 ,    help="""Number of train epochs.""" ,    )
    snake_case							:		List[str]        =		parser.parse_args()
    snake_case							:		str        =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
    training_function(__A ,    __A		)


if __name__ == "__main__":
     main()










import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __magic_name__	(				lowerCamelCase__     ,					lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  IFInpaintingPipeline
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCamelCase            =  PipelineTesterMixin.required_optional_params - {'''latents'''}



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        return self._get_dummy_components()



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str      ,   snake_case							:List[Any]=0	):





        '''simple docstring'''




        if str(snake_case	).startswith("mps"	):
           A_       :					Optional[Any]        =      torch.manual_seed(snake_case	)
        else:
           A_       :					Any        =      torch.Generator(device=snake_case	).manual_seed(snake_case	)

        A_       :					Optional[Any]        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)
        A_       :					Any        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)

        A_       :					List[Any]        =      {
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "numpy",
        }

        return inputs



     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()      ,   reason="XFormers attention is only available with CUDA and `xformers` installed"      ,   )
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != "cuda"      ,   reason="float16 requires CUDA"	)
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        super().test_save_load_floataa(expected_max_diff=1e-1	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        self._test_save_load_local()





     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2      ,   )
