


'''simple docstring'''
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 					lowerCamelCase_ ( __UpperCamelCase					:       Accelerator							,    __UpperCamelCase					:       int = 1_6							,    __UpperCamelCase					:       str = "bert-base-cased"		)					->	Optional[int]:







     """simple docstring"""
     _A				=							AutoTokenizer.from_pretrained(__UpperCamelCase		)
     _A				=							load_dataset('glue'							,    'mrpc'		)

     def tokenize_function(__UpperCamelCase					:       Dict		):
          # max_length=None => use the model max length (it's actually the default)
          _A				=							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
     _A				=							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
     _A				=							tokenized_datasets.rename_column('label'							,    'labels'		)

     def collate_fn(__UpperCamelCase					:       Dict		):
          # 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=1_2_8							,    return_tensors='pt'		)
          return tokenizer.pad(__UpperCamelCase							,    padding='longest'							,    return_tensors='pt'		)

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

     return train_dataloader, eval_dataloader

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       Tuple		)					->	Union[str, Any]:







     """simple docstring"""
     # Initialize accelerator
     _A				=							Accelerator()

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

     set_seed(__UpperCamelCase		)
     _A		,				_A				=							get_dataloaders(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

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

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

     if accelerator.state.deepspeed_plugin is not None:
          _A				=							accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          _A				=							1
     _A				=							(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
     ):
          _A				=							get_linear_schedule_with_warmup(
              optimizer=__UpperCamelCase							,    num_warmup_steps=0							,    num_training_steps=__UpperCamelCase							,    )
     else:
          _A				=							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.
     _A		,				_A		,				_A		,				_A		,				_A				=							accelerator.prepare(
         __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

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

     # Now we train the model
     _A				=							evaluate.load('glue'							,    'mrpc'		)
     _A				=							0
     _A				=							{}
     for epoch in range(__UpperCamelCase							,    __UpperCamelCase		):
          model.train()
          for step, batch in enumerate(__UpperCamelCase		):
               _A				=							model(**__UpperCamelCase		)
               _A				=							outputs.loss
               _A				=							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()
          _A				=							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():
                    _A				=							model(**__UpperCamelCase		)
               _A				=							outputs.logits.argmax(dim=-1		)
               # It is slightly faster to call this once, than multiple times
               _A		,				_A				=							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:
                         _A				=							predictions[: len(eval_dataloader.dataset		) - samples_seen]
                         _A				=							references[: len(eval_dataloader.dataset		) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=__UpperCamelCase							,    references=__UpperCamelCase							,    )

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

          if best_performance < eval_metric["accuracy"]:
               _A				=							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 					lowerCamelCase_ ( )					->	str:







     """simple docstring"""
     _A				=							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.'							,    )
     _A				=							parser.parse_args()
     _A				=							{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
     training_function(__UpperCamelCase							,    __UpperCamelCase		)


if __name__ == "__main__":
    main()









'''simple docstring'''
import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    lowerCAmelCase						=					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=18			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     )->      Dict:
            _A				=							size if size is not None else {'height': 20, 'width': 20}
            _A				=							parent
            _A				=							batch_size
            _A				=							num_channels
            _A				=							image_size
            _A				=							min_resolution
            _A				=							max_resolution
            _A				=							size
            _A				=							do_normalize
            _A				=							do_convert_rgb
            _A				=							[512, 1024, 2048, 4096]
            _A				=							patch_size if patch_size is not None else {'height': 16, 'width': 16}






       def   UpperCamelCase						( self   )->      Dict:
            return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
            _A				=							Image.open(requests.get(_UpperCamelCase			,     stream=_UpperCamelCase   ).raw   ).convert('RGB'   )
            return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,							)
@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							PixaStructImageProcessingTester(self   )






       @property
       def   UpperCamelCase						( self   )->      Tuple:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processor_tester.prepare_dummy_image()

            _A				=							self.image_processing_class(**self.image_processor_dict   )
            _A				=							2048

            _A				=							image_processor(_UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   )
            self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,     torch.tensor(0.0606   )			,     atol=1e-3			,     rtol=1e-3   )   )






       def   UpperCamelCase						( self   )->      int:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      List[str]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            _A				=							True

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 with self.assertRaises(_UpperCamelCase   ):
                      _A				=							image_processor(
                          image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches

                 _A				=							'Hello'

                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Optional[int]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     numpify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     np.ndarray   )

            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     torchify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     torch.Tensor   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,							)
@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      str:
            _A				=							PixaStructImageProcessingTester(self			,     num_channels=4   )
            _A				=							3






       @property
       def   UpperCamelCase						( self   )->      str:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * (self.image_processor_tester.num_channels - 1)
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )





