






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A        =			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')

A        =			list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A        =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				int				    =						field(
		    default="""cifar10""" ,	metadata={"""help""": """Name of a dataset from the datasets package"""}						)
		UpperCAmelCase__      :				List[Any]				    =						field(
		    default=__A ,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
		UpperCAmelCase__      :				Optional[Any]				    =						field(
		    default=__A ,	metadata={"""help""": """The column name of the images in the files. If not set, will try to use \'image\' or \'img\'."""} ,	)
		UpperCAmelCase__      :				str				    =						field(default=__A ,	metadata={"""help""": """A folder containing the training data."""}						)
		UpperCAmelCase__      :				Tuple				    =						field(default=__A ,	metadata={"""help""": """A folder containing the validation data."""}						)
		UpperCAmelCase__      :				Any				    =						field(
		    default=0.15 ,	metadata={"""help""": """Percent to split off of train for validation."""}						)
		UpperCAmelCase__      :				Optional[int]				    =						field(default=32 ,	metadata={"""help""": """The size of the square patches to use for masking."""}						)
		UpperCAmelCase__      :				Optional[int]				    =						field(
		    default=0.6 ,	metadata={"""help""": """Percentage of patches to mask."""} ,	)
		UpperCAmelCase__      :				List[str]				    =						field(
		    default=__A ,	metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of training examples to this """
		            """value if set."""
		        )
		    } ,	)
		UpperCAmelCase__      :				str				    =						field(
		    default=__A ,	metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
		            """value if set."""
		        )
		    } ,	)


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {}
						if self.train_dir is not None:
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.train_dir
						if self.validation_dir is not None:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   self.validation_dir
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   data_files if data_files else None






@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Dict				    =						field(
		    default=__A ,	metadata={
		        """help""": (
		            """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
		            """checkpoint identifier on the hub. """
		            """Don\'t set if you want to train a model from scratch."""
		        )
		    } ,	)
		UpperCAmelCase__      :				Union[str, Any]				    =						field(
		    default=__A ,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__A						)} ,	)
		UpperCAmelCase__      :				Optional[Any]				    =						field(
		    default=__A ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
		UpperCAmelCase__      :				str				    =						field(
		    default=__A ,	metadata={
		        """help""": (
		            """Override some existing default config settings when a model is trained from scratch. Example: """
		            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
		        )
		    } ,	)
		UpperCAmelCase__      :				Dict				    =						field(
		    default=__A ,	metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""} ,	)
		UpperCAmelCase__      :				str				    =						field(
		    default="""main""" ,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} ,	)
		UpperCAmelCase__      :				Dict				    =						field(default=__A ,	metadata={"""help""": """Name or path of preprocessor config."""}						)
		UpperCAmelCase__      :				str				    =						field(
		    default=__A ,	metadata={
		        """help""": (
		            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
		            """with private models)."""
		        )
		    } ,	)
		UpperCAmelCase__      :				Any				    =						field(
		    default=__A ,	metadata={
		        """help""": (
		            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
		        )
		    } ,	)
		UpperCAmelCase__      :				Any				    =						field(
		    default=__A ,	metadata={
		        """help""": (
		            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
		        )
		    } ,	)
		UpperCAmelCase__      :				Dict				    =						field(
		    default=__A ,	metadata={"""help""": """Stride to use for the encoder."""} ,	)






class 							__a							:





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__=192 ,    UpperCamelCase__=32 ,    UpperCamelCase__=4 ,    UpperCamelCase__=0.6       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   input_size
						SCREAMING_SNAKE_CASE_   :	List[str]													=   mask_patch_size
						SCREAMING_SNAKE_CASE_   :	str													=   model_patch_size
						SCREAMING_SNAKE_CASE_   :	Any													=   mask_ratio

						if self.input_size % self.mask_patch_size != 0:
										raise ValueError('Input size must be divisible by mask patch size'       )
						if self.mask_patch_size % self.model_patch_size != 0:
										raise ValueError('Mask patch size must be divisible by model patch size'       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.input_size // self.mask_patch_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.mask_patch_size // self.model_patch_size

						SCREAMING_SNAKE_CASE_   :	Dict													=   self.rand_size**2
						SCREAMING_SNAKE_CASE_   :	List[str]													=   int(np.ceil(self.token_count * self.mask_ratio       )       )






		def __call__(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   np.random.permutation(self.token_count       )[: self.mask_count]
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   np.zeros(self.token_count ,    dtype=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   1

						SCREAMING_SNAKE_CASE_   :	Any													=   mask.reshape((self.rand_size, self.rand_size)       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   mask.repeat(self.scale ,    axis=0       ).repeat(self.scale ,    axis=1       )

						return torch.tensor(mask.flatten()       )






def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.stack([example['pixel_values'] for example in examples]      )
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.stack([example['mask'] for example in examples]      )
				return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
				if len(sys.argv      ) == 2 and sys.argv[1].endswith('.json'      ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('run_mim'    ,    _snake_case    ,    _snake_case      )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'    ,    datefmt='%m/%d/%Y %H:%M:%S'    ,    handlers=[logging.StreamHandler(sys.stdout      )]    ,    )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				SCREAMING_SNAKE_CASE_   :	Dict													=   training_args.get_process_log_level()
				logger.setLevel(_snake_case      )
				transformers.utils.logging.set_verbosity(_snake_case      )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + F'''distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}'''      )
				logger.info(F'''Training/evaluation parameters {training_args}'''      )

				# Detecting last checkpoint.
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   None
				if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   get_last_checkpoint(training_args.output_dir      )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
												raise ValueError(
												    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    'Use --overwrite_output_dir to overcome.'      )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'      )

    # Initialize our dataset.
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   load_dataset(
				    data_args.dataset_name    ,    data_args.dataset_config_name    ,    data_files=data_args.data_files    ,    cache_dir=model_args.cache_dir    ,    use_auth_token=True if model_args.use_auth_token else None    ,    )

				# If we don't have a validation split, split off a percentage of train as validation.
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   None if 'validation' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split    ,    _snake_case      ) and data_args.train_val_split > 0.0:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   ds['train'].train_test_split(data_args.train_val_split      )
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   split['train']
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   split['test']

				# Create config
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {
				    'cache_dir': model_args.cache_dir,
				    'revision': model_args.model_revision,
				    'use_auth_token': True if model_args.use_auth_token else None,
				}
				if model_args.config_name_or_path:
								SCREAMING_SNAKE_CASE_   :	str													=   AutoConfig.from_pretrained(model_args.config_name_or_path    ,    **_snake_case      )
				elif model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AutoConfig.from_pretrained(model_args.model_name_or_path    ,    **_snake_case      )
				else:
								SCREAMING_SNAKE_CASE_   :	Dict													=   CONFIG_MAPPING[model_args.model_type]()
								logger.warning('You are instantiating a new config instance from scratch.'      )
								if model_args.config_overrides is not None:
												logger.info(F'''Overriding config: {model_args.config_overrides}'''      )
												config.update_from_string(model_args.config_overrides      )
												logger.info(F'''New config: {config}'''      )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
				if hasattr(_snake_case    ,    'decoder_type'      ):
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   'simmim'

				# adapt config
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   model_args.image_size if model_args.image_size is not None else config.image_size
				SCREAMING_SNAKE_CASE_   :	str													=   model_args.patch_size if model_args.patch_size is not None else config.patch_size
				SCREAMING_SNAKE_CASE_   :	Any													=   (
				    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
				)

				config.update(
				    {
				        'image_size': model_args.image_size,
				        'patch_size': model_args.patch_size,
				        'encoder_stride': model_args.encoder_stride,
				    }      )

				# create image processor
				if model_args.image_processor_name:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AutoImageProcessor.from_pretrained(model_args.image_processor_name    ,    **_snake_case      )
				elif model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE_   :	str													=   AutoImageProcessor.from_pretrained(model_args.model_name_or_path    ,    **_snake_case      )
				else:
								SCREAMING_SNAKE_CASE_   :	int													=   {
								    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
								}
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   IMAGE_PROCESSOR_TYPES[model_args.model_type]()

				# create model
				if model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AutoModelForMaskedImageModeling.from_pretrained(
								    model_args.model_name_or_path    ,    from_tf=bool('.ckpt' in model_args.model_name_or_path      )    ,    config=_snake_case    ,    cache_dir=model_args.cache_dir    ,    revision=model_args.model_revision    ,    use_auth_token=True if model_args.use_auth_token else None    ,    )
				else:
								logger.info('Training new model from scratch'      )
								SCREAMING_SNAKE_CASE_   :	List[str]													=   AutoModelForMaskedImageModeling.from_config(_snake_case      )

				if training_args.do_train:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   ds['train'].column_names
				else:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   ds['validation'].column_names

				if data_args.image_column_name is not None:
								SCREAMING_SNAKE_CASE_   :	int													=   data_args.image_column_name
				elif "image" in column_names:
								SCREAMING_SNAKE_CASE_   :	int													=   'image'
				elif "img" in column_names:
								SCREAMING_SNAKE_CASE_   :	Any													=   'img'
				else:
								SCREAMING_SNAKE_CASE_   :	int													=   column_names[0]

				# transformations as done in original SimMIM paper
				# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
				SCREAMING_SNAKE_CASE_   :	str													=   Compose(
				    [
				        Lambda(lambda lowerCAmelCase__      : img.convert('RGB'      ) if img.mode != "RGB" else img      ),
				        RandomResizedCrop(model_args.image_size    ,    scale=(0.67, 1.0)    ,    ratio=(3.0 / 4.0, 4.0 / 3.0)      ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean    ,    std=image_processor.image_std      ),
				    ]      )

				# create mask generator
				SCREAMING_SNAKE_CASE_   :	Tuple													=   MaskGenerator(
				    input_size=model_args.image_size    ,    mask_patch_size=data_args.mask_patch_size    ,    model_patch_size=model_args.patch_size    ,    mask_ratio=data_args.mask_ratio    ,    )

				def preprocess_images(lowerCAmelCase__				:		Tuple      ):
								SCREAMING_SNAKE_CASE_   :	str													=   [transforms(_snake_case      ) for image in examples[image_column_name]]
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [mask_generator() for i in range(len(examples[image_column_name]      )      )]

								return examples

				if training_args.do_train:
								if "train" not in ds:
												raise ValueError('--do_train requires a train dataset'      )
								if data_args.max_train_samples is not None:
												SCREAMING_SNAKE_CASE_   :	Tuple													=   ds['train'].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
								# Set the training transforms
								ds["train"].set_transform(_snake_case      )

				if training_args.do_eval:
								if "validation" not in ds:
												raise ValueError('--do_eval requires a validation dataset'      )
								if data_args.max_eval_samples is not None:
												SCREAMING_SNAKE_CASE_   :	Tuple													=   (
												    ds['validation'].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
												)
								# Set the validation transforms
								ds["validation"].set_transform(_snake_case      )

				# Initialize our trainer
				SCREAMING_SNAKE_CASE_   :	Any													=   Trainer(
				    model=_snake_case    ,    args=_snake_case    ,    train_dataset=ds['train'] if training_args.do_train else None    ,    eval_dataset=ds['validation'] if training_args.do_eval else None    ,    tokenizer=_snake_case    ,    data_collator=_snake_case    ,    )

				# Training
				if training_args.do_train:
								SCREAMING_SNAKE_CASE_   :	Any													=   None
								if training_args.resume_from_checkpoint is not None:
												SCREAMING_SNAKE_CASE_   :	str													=   training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												SCREAMING_SNAKE_CASE_   :	List[str]													=   last_checkpoint
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   trainer.train(resume_from_checkpoint=_snake_case      )
								trainer.save_model()
								trainer.log_metrics('train'    ,    train_result.metrics      )
								trainer.save_metrics('train'    ,    train_result.metrics      )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								SCREAMING_SNAKE_CASE_   :	Dict													=   trainer.evaluate()
								trainer.log_metrics('eval'    ,    _snake_case      )
								trainer.save_metrics('eval'    ,    _snake_case      )

				# Write model card and (optionally) push to hub
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {
				    'finetuned_from': model_args.model_name_or_path,
				    'tasks': 'masked-image-modeling',
				    'dataset': data_args.dataset_name,
				    'tags': ['masked-image-modeling'],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**_snake_case      )
				else:
								trainer.create_model_card(**_snake_case      )


if __name__ == "__main__":
							main()







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 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE_   :	Dict													=   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]       )       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   {
						    '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],
						}
						SCREAMING_SNAKE_CASE_   :	str													=   os.path.join(self.tmpdirname ,    UpperCamelCase__       )
						with open(self.image_processor_file ,    'w' ,    encoding='utf-8'       ) as fp:
										json.dump(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizer.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizerFast.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self       ):
						shutil.rmtree(self.tmpdirname       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   [np.random.randint(255 ,    size=(3, 30, 400) ,    dtype=np.uinta       )]
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [Image.fromarray(np.moveaxis(UpperCamelCase__ ,    0 ,    -1       )       ) for x in image_inputs]
						return image_inputs


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.get_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.from_pretrained(self.tmpdirname ,    use_fast=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, 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 ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.tokenizer ,    UpperCamelCase__       )

						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 ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.image_processor ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer(bos_token='(BOS)' ,    eos_token='(EOS)'       )
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor(do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor.from_pretrained(
						    self.tmpdirname ,    bos_token='(BOS)' ,    eos_token='(EOS)' ,    do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

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

						self.assertEqual(processor.image_processor.to_json_string() ,    image_processor_add_kwargs.to_json_string()       )
						self.assertIsInstance(processor.image_processor ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

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

						SCREAMING_SNAKE_CASE_   :	int													=   image_processor(UpperCamelCase__ ,    return_tensors='np'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   processor(images=UpperCamelCase__ ,    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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   'lower newer'

						SCREAMING_SNAKE_CASE_   :	Tuple													=   processor(text=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	int													=   tokenizer(UpperCamelCase__ ,    padding='max_length' ,    max_length=64       )

						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key] ,    encoded_processor[key]       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

						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(UpperCamelCase__       ):
										processor()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						SCREAMING_SNAKE_CASE_   :	List[str]													=   processor.batch_decode(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.batch_decode(UpperCamelCase__       )

						self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

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