




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


_lowerCamelCase      			=						16
_lowerCamelCase      			=						32




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Accelerator	,					__UpperCamelCase      :						int = 16	,					__UpperCamelCase      :						str = "bert-base-cased"				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					AutoTokenizer.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					load_dataset('''glue'''	,					'''mrpc'''				)

    def tokenize_function(__UpperCamelCase      :						str				):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase_										=					tokenizer(examples['''sentence1''']	,					examples['''sentence2''']	,					truncation=__UpperCamelCase	,					max_length=__UpperCamelCase				)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    UpperCAmelCase_										=					datasets.map(
        __UpperCamelCase	,					batched=__UpperCamelCase	,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,					load_from_cache_file=__UpperCamelCase				)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    UpperCAmelCase_										=					tokenized_datasets.rename_column('''label'''	,					'''labels'''				)

    def collate_fn(__UpperCamelCase      :						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(__UpperCamelCase	,					padding='''max_length'''	,					max_length=128	,					return_tensors='''pt'''				)
        return tokenizer.pad(__UpperCamelCase	,					padding='''longest'''	,					return_tensors='''pt'''				)

    # Instantiate dataloaders.
    UpperCAmelCase_										=					DataLoader(
        tokenized_datasets['''train''']	,					shuffle=__UpperCamelCase	,					collate_fn=__UpperCamelCase	,					batch_size=__UpperCamelCase				)
    UpperCAmelCase_										=					DataLoader(
        tokenized_datasets['''validation''']	,					shuffle=__UpperCamelCase	,					collate_fn=__UpperCamelCase	,					batch_size=__UpperCamelCase				)

    return train_dataloader, eval_dataloader




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Optional[Any]				)  ->							Tuple:
    # Initialize accelerator
    UpperCAmelCase_										=					Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    UpperCAmelCase_										=					config['''lr''']
    UpperCAmelCase_										=					int(config['''num_epochs''']				)
    UpperCAmelCase_										=					int(config['''seed''']				)
    UpperCAmelCase_										=					int(config['''batch_size''']				)
    UpperCAmelCase_										=					args.model_name_or_path

    set_seed(__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_dataloaders(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    UpperCAmelCase_										=					AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase	,					return_dict=__UpperCamelCase				)

    # Instantiate optimizer
    UpperCAmelCase_										=					(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    UpperCAmelCase_										=					optimizer_cls(params=model.parameters()	,					lr=__UpperCamelCase				)

    if accelerator.state.deepspeed_plugin is not None:
        UpperCAmelCase_										=					accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        UpperCAmelCase_										=					1
    UpperCAmelCase_										=					(len(__UpperCamelCase				) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        UpperCAmelCase_										=					get_linear_schedule_with_warmup(
            optimizer=__UpperCamelCase	,					num_warmup_steps=0	,					num_training_steps=__UpperCamelCase	,					)
    else:
        UpperCAmelCase_										=					DummyScheduler(__UpperCamelCase	,					total_num_steps=__UpperCamelCase	,					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.
    UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					accelerator.prepare(
        __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

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

    # Now we train the model
    UpperCAmelCase_										=					evaluate.load('''glue'''	,					'''mrpc'''				)
    UpperCAmelCase_										=					0
    UpperCAmelCase_										=					{}
    for epoch in range(__UpperCamelCase	,					__UpperCamelCase				):
        model.train()
        for step, batch in enumerate(__UpperCamelCase				):
            UpperCAmelCase_										=					model(**__UpperCamelCase				)
            UpperCAmelCase_										=					outputs.loss
            UpperCAmelCase_										=					loss / gradient_accumulation_steps
            accelerator.backward(__UpperCamelCase				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        UpperCAmelCase_										=					0
        for step, batch in enumerate(__UpperCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
                UpperCAmelCase_										=					model(**__UpperCamelCase				)
            UpperCAmelCase_										=					outputs.logits.argmax(dim=-1				)
            # It is slightly faster to call this once, than multiple times
            UpperCAmelCase_			,							UpperCAmelCase_										=					accelerator.gather(
                (predictions, batch['''labels'''])				)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(__UpperCamelCase				) - 1:
                    UpperCAmelCase_										=					predictions[: len(eval_dataloader.dataset				) - samples_seen]
                    UpperCAmelCase_										=					references[: len(eval_dataloader.dataset				) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=__UpperCamelCase	,					references=__UpperCamelCase	,					)

        UpperCAmelCase_										=					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'	,					__UpperCamelCase				)
        UpperCAmelCase_										=					eval_metric['''accuracy''']

        if best_performance < eval_metric["accuracy"]:
            UpperCAmelCase_										=					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(__UpperCamelCase	,					__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	)  ->							List[Any]:
    UpperCAmelCase_										=					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
    parser.add_argument(
        '''--model_name_or_path'''	,					type=__UpperCamelCase	,					default='''bert-base-cased'''	,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,					required=__UpperCamelCase	,					)
    parser.add_argument(
        '''--output_dir'''	,					type=__UpperCamelCase	,					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=__UpperCamelCase	,					default=__UpperCamelCase	,					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=__UpperCamelCase	,					default=3	,					help='''Number of train epochs.'''	,					)
    UpperCAmelCase_										=					parser.parse_args()
    UpperCAmelCase_										=					{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(__UpperCamelCase	,					__UpperCamelCase				)


if __name__ == "__main__":
 main()











import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import MobileNetVaImageProcessor



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      int ,   __snake_case       :      List[Any]=7 ,   __snake_case       :      Any=3 ,   __snake_case       :      Any=18 ,   __snake_case       :      str=30 ,   __snake_case       :      Any=4_00 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      str=None ,   __snake_case       :      Any=True ,   __snake_case       :      List[Any]=None ,   ):
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 20}
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					num_channels
         UpperCAmelCase_										=					image_size
         UpperCAmelCase_										=					min_resolution
         UpperCAmelCase_										=					max_resolution
         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size




     def   lowerCamelCase_       (     self       :      Any    ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  MobileNetVaImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					MobileNetVaImageProcessingTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         return self.image_processor_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(__snake_case ,   '''do_resize'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''size'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''do_center_crop'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''crop_size'''    )    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 20}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 18, '''width''': 18}    )

         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict ,   size=42 ,   crop_size=84    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 84, '''width''': 84}    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         pass
     def   lowerCamelCase_       (     self       :      Tuple    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   Image.Image    )

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )
     def   lowerCamelCase_       (     self       :      str    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   numpify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   np.ndarray    )

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )




     def   lowerCamelCase_       (     self       :      int    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   torchify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   torch.Tensor    )

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )





