'''simple docstring'''




import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[Any]     				=					fname.split(os.path.sep							)[-1]
     return re.search(r'^(.*)_\d+\.jpg$'  ,       SCREAMING_SNAKE_CASE_							).groups()[0]


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=None       ,				_a=None				)    ->     List[str]:
        lowercase_					:       Any     				=					file_names
        lowercase_					:       int     				=					image_transform
        lowercase_					:       Optional[Any]     				=					label_to_id
   def __len__(self				)    ->     List[str]:
        return len(self.file_names				)





   def __getitem__(self       ,				_a				)    ->     int:
        lowercase_					:       List[str]     				=					self.file_names[idx]
        lowercase_					:       Optional[Any]     				=					PIL.Image.open(_a				)
        lowercase_					:       Dict     				=					raw_image.convert('RGB'				)
        if self.image_transform is not None:
             lowercase_					:       List[str]     				=					self.image_transform(_a				)
        lowercase_					:       List[Any]     				=					extract_label(_a				)
        if self.label_to_id is not None:
             lowercase_					:       Any     				=					self.label_to_id[label]
        return {"image": image, "label": label}



def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     # Initialize accelerator
     if args.with_tracking:
          lowercase_					:       List[str]     				=					Accelerator(
              cpu=args.cpu  ,       mixed_precision=args.mixed_precision  ,       log_with='all'  ,       project_dir=args.project_dir							)
     else:
          lowercase_					:       Union[str, Any]     				=					Accelerator(cpu=args.cpu  ,       mixed_precision=args.mixed_precision							)

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_					:       List[Any]     				=					config['lr']
     lowercase_					:       str     				=					int(config['num_epochs']							)
     lowercase_					:       Optional[int]     				=					int(config['seed']							)
     lowercase_					:       Tuple     				=					int(config['batch_size']							)
     lowercase_					:       Optional[int]     				=					config['image_size']
     if not isinstance(SCREAMING_SNAKE_CASE_  ,       (list, tuple)							):
          lowercase_					:       int     				=					(image_size, image_size)

     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps  ,       'isdigit'							):
          if args.checkpointing_steps == "epoch":
               lowercase_					:       str     				=					args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               lowercase_					:       List[Any]     				=					int(args.checkpointing_steps							)
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
     else:
          lowercase_					:       Union[str, Any]     				=					None

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          lowercase_					:       Optional[int]     				=					os.path.split(SCREAMING_SNAKE_CASE_							)[-1].split('.'							)[0]
          accelerator.init_trackers(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # Grab all the image filenames
     lowercase_					:       int     				=					[os.path.join(args.data_dir  ,       SCREAMING_SNAKE_CASE_							) for fname in os.listdir(args.data_dir							) if fname.endswith('.jpg'							)]

     # Build the label correspondences
     lowercase_					:       int     				=					[extract_label(SCREAMING_SNAKE_CASE_							) for fname in file_names]
     lowercase_					:       List[Any]     				=					list(set(SCREAMING_SNAKE_CASE_							)							)
     id_to_label.sort()
     lowercase_					:       Optional[int]     				=					{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE_							)}

     # Set the seed before splitting the data.
     np.random.seed(SCREAMING_SNAKE_CASE_							)
     torch.manual_seed(SCREAMING_SNAKE_CASE_							)
     torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE_							)

     # Split our filenames between train and validation
     lowercase_					:       str     				=					np.random.permutation(len(SCREAMING_SNAKE_CASE_							)							)
     lowercase_					:       Optional[int]     				=					int(0.8 * len(SCREAMING_SNAKE_CASE_							)							)
     lowercase_					:       List[Any]     				=					random_perm[:cut]
     lowercase_					:       str     				=					random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     lowercase_					:       List[str]     				=					Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE_  ,       scale=(0.5, 1.0)							), ToTensor()]							)
     lowercase_					:       Optional[Any]     				=					PetsDataset(
         [file_names[i] for i in train_split]  ,       image_transform=SCREAMING_SNAKE_CASE_  ,       label_to_id=SCREAMING_SNAKE_CASE_							)

     # For evaluation, we use a deterministic Resize
     lowercase_					:       int     				=					Compose([Resize(SCREAMING_SNAKE_CASE_							), ToTensor()]							)
     lowercase_					:       List[str]     				=					PetsDataset([file_names[i] for i in eval_split]  ,       image_transform=SCREAMING_SNAKE_CASE_  ,       label_to_id=SCREAMING_SNAKE_CASE_							)

     # Instantiate dataloaders.
     lowercase_					:       Dict     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_  ,       num_workers=4							)
     lowercase_					:       List[str]     				=					DataLoader(SCREAMING_SNAKE_CASE_  ,       shuffle=SCREAMING_SNAKE_CASE_  ,       batch_size=SCREAMING_SNAKE_CASE_  ,       num_workers=4							)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_					:       Optional[Any]     				=					create_model('resnet50d'  ,       pretrained=SCREAMING_SNAKE_CASE_  ,       num_classes=len(SCREAMING_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).
     lowercase_					:       Tuple     				=					model.to(accelerator.device							)

     # Freezing the base model
     for param in model.parameters():
          lowercase_					:       Tuple     				=					False
     for param in model.get_classifier().parameters():
          lowercase_					:       Union[str, Any]     				=					True

     # We normalize the batches of images to be a bit faster.
     lowercase_					:       Dict     				=					torch.tensor(model.default_cfg['mean']							)[None, :, None, None].to(accelerator.device							)
     lowercase_					:       List[str]     				=					torch.tensor(model.default_cfg['std']							)[None, :, None, None].to(accelerator.device							)

     # Instantiate optimizer
     lowercase_					:       str     				=					torch.optim.Adam(params=model.parameters()  ,       lr=lr / 25							)

     # Instantiate learning rate scheduler
     lowercase_					:       Tuple     				=					OneCycleLR(optimizer=SCREAMING_SNAKE_CASE_  ,       max_lr=SCREAMING_SNAKE_CASE_  ,       epochs=SCREAMING_SNAKE_CASE_  ,       steps_per_epoch=len(SCREAMING_SNAKE_CASE_							)							)

     # 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.
     lowercase_				,lowercase_				,lowercase_				,lowercase_				,lowercase_					:       int     				=					accelerator.prepare(
         SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     # We need to keep track of how many total steps we have iterated over
     lowercase_					:       List[str]     				=					0
     # We also need to keep track of the starting epoch so files are named properly
     lowercase_					:       str     				=					0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
               accelerator.load_state(args.resume_from_checkpoint							)
               lowercase_					:       Any     				=					os.path.basename(args.resume_from_checkpoint							)
          else:
               # Get the most recent checkpoint
               lowercase_					:       Tuple     				=					[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
               dirs.sort(key=os.path.getctime							)
               lowercase_					:       Optional[int]     				=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          lowercase_					:       Optional[Any]     				=					os.path.splitext(SCREAMING_SNAKE_CASE_							)[0]

          if "epoch" in training_difference:
               lowercase_					:       Any     				=					int(training_difference.replace('epoch_'  ,       ''							)							) + 1
               lowercase_					:       Optional[int]     				=					None
          else:
               lowercase_					:       str     				=					int(training_difference.replace('step_'  ,       ''							)							)
               lowercase_					:       Optional[Any]     				=					resume_step // len(SCREAMING_SNAKE_CASE_							)
               resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE_							)

    # Now we train the model
     for epoch in range(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
          model.train()
          if args.with_tracking:
               lowercase_					:       List[str]     				=					0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               lowercase_					:       List[Any]     				=					accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               lowercase_					:       str     				=					train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               lowercase_					:       int     				=					{k: v.to(accelerator.device							) for k, v in batch.items()}
               lowercase_					:       List[Any]     				=					(batch['image'] - mean) / std
               lowercase_					:       Any     				=					model(SCREAMING_SNAKE_CASE_							)
               lowercase_					:       Tuple     				=					torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE_  ,       batch['label']							)
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(SCREAMING_SNAKE_CASE_							)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               overall_step += 1
               if isinstance(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
                    lowercase_					:       str     				=					f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              lowercase_					:       str     				=					os.path.join(args.output_dir  ,       SCREAMING_SNAKE_CASE_							)
                         accelerator.save_state(SCREAMING_SNAKE_CASE_							)
          model.eval()
          lowercase_					:       Optional[int]     				=					0
          lowercase_					:       Any     				=					0
          for step, batch in enumerate(SCREAMING_SNAKE_CASE_							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               lowercase_					:       List[str]     				=					{k: v.to(accelerator.device							) for k, v in batch.items()}
               lowercase_					:       List[str]     				=					(batch['image'] - mean) / std
               with torch.no_grad():
                    lowercase_					:       List[str]     				=					model(SCREAMING_SNAKE_CASE_							)
               lowercase_					:       List[str]     				=					outputs.argmax(dim=-1							)
               lowercase_				,lowercase_					:       Union[str, Any]     				=					accelerator.gather_for_metrics((predictions, batch['label'])							)
               lowercase_					:       List[str]     				=					predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

          lowercase_					:       Optional[int]     				=					accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''							)
          if args.with_tracking:
               accelerator.log(
                   {
                       'accuracy': 100 * eval_metric,
                       'train_loss': total_loss.item() / len(SCREAMING_SNAKE_CASE_							),
                       'epoch': epoch,
                   }  ,       step=SCREAMING_SNAKE_CASE_  ,       )
          if checkpointing_steps == "epoch":
               lowercase_					:       List[str]     				=					f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    lowercase_					:       Union[str, Any]     				=					os.path.join(args.output_dir  ,       SCREAMING_SNAKE_CASE_							)
               accelerator.save_state(SCREAMING_SNAKE_CASE_							)

     if args.with_tracking:
          accelerator.end_training()

def _UpperCamelCase   (       ):
     lowercase_					:       Any     				=					argparse.ArgumentParser(description='Simple example of training script.'							)
     parser.add_argument('--data_dir'  ,       required=SCREAMING_SNAKE_CASE_  ,       help='The data folder on disk.'							)
     parser.add_argument('--fp16'  ,       action='store_true'  ,       help='If passed, will use FP16 training.'							)
     parser.add_argument(
         '--mixed_precision'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=SCREAMING_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.'							)
     parser.add_argument(
         '--checkpointing_steps'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=SCREAMING_SNAKE_CASE_  ,       help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'  ,       )
     parser.add_argument(
         '--output_dir'  ,       type=SCREAMING_SNAKE_CASE_  ,       default='.'  ,       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'  ,       )
     parser.add_argument(
         '--resume_from_checkpoint'  ,       type=SCREAMING_SNAKE_CASE_  ,       default=SCREAMING_SNAKE_CASE_  ,       help='If the training should continue from a checkpoint folder.'  ,       )
     parser.add_argument(
         '--with_tracking'  ,       action='store_true'  ,       help='Whether to load in all available experiment trackers from the environment and use them for logging.'  ,       )
     parser.add_argument(
         '--project_dir'  ,       type=SCREAMING_SNAKE_CASE_  ,       default='logs'  ,       help='Location on where to store experiment tracking logs` and relevent project information'  ,       )
     lowercase_					:       Optional[Any]     				=					parser.parse_args()
     lowercase_					:       Union[str, Any]     				=					{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
     training_function(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 main()





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					tempfile.mkdtemp()

        lowercase_					:       int     				=					[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowercase_					:       Union[str, Any]     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)

        lowercase_					:       Union[str, Any]     				=					{
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        lowercase_					:       List[str]     				=					os.path.join(self.tmpdirname       ,				_a				)
        with open(self.image_processor_file       ,				'w'       ,				encoding='utf-8'				) as fp:
             json.dump(_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     int:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        shutil.rmtree(self.tmpdirname				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					[np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta				)]
        lowercase_					:       Optional[Any]     				=					[Image.fromarray(np.moveaxis(_a       ,				0       ,				-1				)				) for x in image_inputs]
        return image_inputs
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					self.get_tokenizer()
        lowercase_					:       Tuple     				=					self.get_rust_tokenizer()
        lowercase_					:       int     				=					self.get_image_processor()

        lowercase_					:       int     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_slow.save_pretrained(self.tmpdirname				)
        lowercase_					:       List[str]     				=					AlignProcessor.from_pretrained(self.tmpdirname       ,				use_fast=_a				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_fast.save_pretrained(self.tmpdirname				)
        lowercase_					:       Any     				=					AlignProcessor.from_pretrained(self.tmpdirname				)

        self.assertEqual(processor_slow.tokenizer.get_vocab()       ,				tokenizer_slow.get_vocab()				)
        self.assertEqual(processor_fast.tokenizer.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertEqual(tokenizer_slow.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertIsInstance(processor_slow.tokenizer       ,				_a				)
        self.assertIsInstance(processor_fast.tokenizer       ,				_a				)

        self.assertEqual(processor_slow.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertEqual(processor_fast.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertIsInstance(processor_slow.image_processor       ,				_a				)
        self.assertIsInstance(processor_fast.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       str     				=					AlignProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        lowercase_					:       Tuple     				=					self.get_tokenizer(bos_token='(BOS)'       ,				eos_token='(EOS)'				)
        lowercase_					:       str     				=					self.get_image_processor(do_normalize=_a       ,				padding_value=1.0				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor.from_pretrained(
            self.tmpdirname       ,				bos_token='(BOS)'       ,				eos_token='(EOS)'       ,				do_normalize=_a       ,				padding_value=1.0				)

        self.assertEqual(processor.tokenizer.get_vocab()       ,				tokenizer_add_kwargs.get_vocab()				)
        self.assertIsInstance(processor.tokenizer       ,				_a				)

        self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					self.get_image_processor()
        lowercase_					:       Union[str, Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[int]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					image_processor(_a       ,				return_tensors='np'				)
        lowercase_					:       Union[str, Any]     				=					processor(images=_a       ,				return_tensors='np'				)

        for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()       ,				input_processor[key].sum()       ,				delta=1e-2				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       List[Any]     				=					self.get_image_processor()
        lowercase_					:       int     				=					self.get_tokenizer()

        lowercase_					:       Any     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Tuple     				=					'lower newer'

        lowercase_					:       Optional[Any]     				=					processor(text=_a				)

        lowercase_					:       List[Any]     				=					tokenizer(_a       ,				padding='max_length'       ,				max_length=64				)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.get_image_processor()
        lowercase_					:       List[Any]     				=					self.get_tokenizer()

        lowercase_					:       List[str]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[Any]     				=					'lower newer'
        lowercase_					:       List[Any]     				=					self.prepare_image_inputs()

        lowercase_					:       List[str]     				=					processor(text=_a       ,				images=_a				)

        self.assertListEqual(list(inputs.keys()				)       ,				['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']				)

        # test if it raises when no input is passed
        with pytest.raises(_a				):
             processor()
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					self.get_image_processor()
        lowercase_					:       Optional[Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_					:       str     				=					processor.batch_decode(_a				)
        lowercase_					:       Dict     				=					tokenizer.batch_decode(_a				)

        self.assertListEqual(_a       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					self.get_image_processor()
        lowercase_					:       str     				=					self.get_tokenizer()

        lowercase_					:       str     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[int]     				=					'lower newer'
        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					processor(text=_a       ,				images=_a				)

        self.assertListEqual(list(inputs.keys()				)       ,				processor.model_input_names				)




