
"""simple docstring"""



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
UpperCAmelCase__          = 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')

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

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







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       {}
       if self.train_dir is not None:
         _snake_case					     =       self.train_dir
       if self.validation_dir is not None:
         _snake_case					     =       self.validation_dir
       _snake_case					     =       data_files if data_files else None






@dataclass
class       lowerCAmelCase__				:
     __a						=					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."""
             )
         } ,	)
     __a						=					field(
         default=A_ ,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(A_	)} ,	)
     __a						=					field(
         default=A_ ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
     __a						=					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"""
             )
         } ,	)
     __a						=					field(
         default=A_ ,	metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""} ,	)
     __a						=					field(
         default="""main""" ,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} ,	)
     __a						=					field(default=A_ ,	metadata={"""help""": """Name or path of preprocessor config."""}	)
     __a						=					field(
         default=A_ ,	metadata={
             """help""": (
                 """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                 """with private models)."""
             )
         } ,	)
     __a						=					field(
         default=A_ ,	metadata={
             """help""": (
                 """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
             )
         } ,	)
     __a						=					field(
         default=A_ ,	metadata={
             """help""": (
                 """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
             )
         } ,	)
     __a						=					field(
         default=A_ ,	metadata={"""help""": """Stride to use for the encoder."""} ,	)






class       lowerCAmelCase__				:







     def __init__(							self			:			Any    ,      _lowerCamelCase			:			Tuple=192    ,      _lowerCamelCase			:			Optional[Any]=32    ,      _lowerCamelCase			:			Union[str, Any]=4    ,      _lowerCamelCase			:			Any=0.6			):
       _snake_case					     =       input_size
       _snake_case					     =       mask_patch_size
       _snake_case					     =       model_patch_size
       _snake_case					     =       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'''			)

       _snake_case					     =       self.input_size // self.mask_patch_size
       _snake_case					     =       self.mask_patch_size // self.model_patch_size

       _snake_case					     =       self.rand_size**2
       _snake_case					     =       int(np.ceil(self.token_count * self.mask_ratio			)			)







     def __call__(							self			:			int			):
       _snake_case					     =       np.random.permutation(self.token_count			)[: self.mask_count]
       _snake_case					     =       np.zeros(self.token_count    ,      dtype=_lowerCamelCase			)
       _snake_case					     =       1

       _snake_case					     =       mask.reshape((self.rand_size, self.rand_size)			)
       _snake_case					     =       mask.repeat(self.scale    ,      axis=0			).repeat(self.scale    ,      axis=1			)

       return torch.tensor(mask.flatten()			)






def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any		)     ->					List[str]:
  _snake_case					     =       torch.stack([example['''pixel_values'''] for example in examples]		)
  _snake_case					     =       torch.stack([example['''mask'''] for example in examples]		)
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def 					_UpperCAmelCase      ( )     ->					str:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  _snake_case					     =       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.
    _snake_case     ,					_snake_case     ,					_snake_case					     =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
  else:
    _snake_case     ,					_snake_case     ,					_snake_case					     =       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''' ,					__lowerCamelCase ,					__lowerCamelCase		)

  # 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()

  _snake_case					     =       training_args.get_process_log_level()
  logger.setLevel(__lowerCamelCase		)
  transformers.utils.logging.set_verbosity(__lowerCamelCase		)
  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.
  _snake_case					     =       None
  if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
    _snake_case					     =       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.
  _snake_case					     =       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.
  _snake_case					     =       None if '''validation''' in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split ,					__lowerCamelCase		) and data_args.train_val_split > 0.0:
    _snake_case					     =       ds['''train'''].train_test_split(data_args.train_val_split		)
    _snake_case					     =       split['''train''']
    _snake_case					     =       split['''test''']

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  _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,
  }
  if model_args.config_name_or_path:
    _snake_case					     =       AutoConfig.from_pretrained(model_args.config_name_or_path ,					**__lowerCamelCase		)
  elif model_args.model_name_or_path:
    _snake_case					     =       AutoConfig.from_pretrained(model_args.model_name_or_path ,					**__lowerCamelCase		)
  else:
    _snake_case					     =       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(__lowerCamelCase ,					'''decoder_type'''		):
    _snake_case					     =       '''simmim'''

  # adapt config
  _snake_case					     =       model_args.image_size if model_args.image_size is not None else config.image_size
  _snake_case					     =       model_args.patch_size if model_args.patch_size is not None else config.patch_size
  _snake_case					     =       (
      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:
    _snake_case					     =       AutoImageProcessor.from_pretrained(model_args.image_processor_name ,					**__lowerCamelCase		)
  elif model_args.model_name_or_path:
    _snake_case					     =       AutoImageProcessor.from_pretrained(model_args.model_name_or_path ,					**__lowerCamelCase		)
  else:
    _snake_case					     =       {
        conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
    }
    _snake_case					     =       IMAGE_PROCESSOR_TYPES[model_args.model_type]()

  # create model
  if model_args.model_name_or_path:
    _snake_case					     =       AutoModelForMaskedImageModeling.from_pretrained(
        model_args.model_name_or_path ,					from_tf=bool('''.ckpt''' in model_args.model_name_or_path		) ,					config=__lowerCamelCase ,					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'''		)
    _snake_case					     =       AutoModelForMaskedImageModeling.from_config(__lowerCamelCase		)

  if training_args.do_train:
    _snake_case					     =       ds['''train'''].column_names
  else:
    _snake_case					     =       ds['''validation'''].column_names

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

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  _snake_case					     =       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
  _snake_case					     =       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   :       Union[str, Any]		):
    _snake_case					     =       [transforms(__lowerCamelCase		) for image in examples[image_column_name]]
    _snake_case					     =       [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:
      _snake_case					     =       ds['''train'''].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
    # Set the training transforms
    ds["train"].set_transform(__lowerCamelCase		)

  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:
      _snake_case					     =       (
          ds['''validation'''].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
      )
    # Set the validation transforms
    ds["validation"].set_transform(__lowerCamelCase		)

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

  # Training
  if training_args.do_train:
    _snake_case					     =       None
    if training_args.resume_from_checkpoint is not None:
      _snake_case					     =       training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      _snake_case					     =       last_checkpoint
    _snake_case					     =       trainer.train(resume_from_checkpoint=__lowerCamelCase		)
    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:
    _snake_case					     =       trainer.evaluate()
    trainer.log_metrics('''eval''' ,					__lowerCamelCase		)
    trainer.save_metrics('''eval''' ,					__lowerCamelCase		)

  # Write model card and (optionally) push to hub
  _snake_case					     =       {
      '''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(**__lowerCamelCase		)
  else:
    trainer.create_model_card(**__lowerCamelCase		)


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 CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPImageProcessor, CLIPProcessor

@require_vision
class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       tempfile.mkdtemp()

       # fmt: off
       _snake_case					     =       ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
       # fmt: on
       _snake_case					     =       dict(zip(_lowerCamelCase    ,      range(len(_lowerCamelCase			)			)			)			)
       _snake_case					     =       ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
       _snake_case					     =       {'''unk_token''': '''<unk>'''}

       _snake_case					     =       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']			)
       _snake_case					     =       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''merges_file''']			)
       with open(self.vocab_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         fp.write(json.dumps(_lowerCamelCase			) + '''\n'''			)
       with open(self.merges_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         fp.write('''\n'''.join(_lowerCamelCase			)			)

       _snake_case					     =       {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
           '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
       }
       _snake_case					     =       os.path.join(self.tmpdirname    ,      _lowerCamelCase			)
       with open(self.image_processor_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         json.dump(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      **_lowerCamelCase			:			Any			):
       return CLIPTokenizer.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			str    ,      **_lowerCamelCase			:			Any			):
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			int    ,      **_lowerCamelCase			:			Optional[int]			):
       return CLIPImageProcessor.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       shutil.rmtree(self.tmpdirname			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       [np.random.randint(255    ,      size=(3, 30, 400)    ,      dtype=np.uinta			)]

       _snake_case					     =       [Image.fromarray(np.moveaxis(_lowerCamelCase    ,      0    ,      -1			)			) for x in image_inputs]

       return image_inputs







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.get_tokenizer()
       _snake_case					     =       self.get_rust_tokenizer()
       _snake_case					     =       self.get_image_processor()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)
       processor_slow.save_pretrained(self.tmpdirname			)
       _snake_case					     =       CLIPProcessor.from_pretrained(self.tmpdirname    ,      use_fast=_lowerCamelCase			)

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)
       processor_fast.save_pretrained(self.tmpdirname			)
       _snake_case					     =       CLIPProcessor.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    ,      _lowerCamelCase			)
       self.assertIsInstance(processor_fast.tokenizer    ,      _lowerCamelCase			)

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







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       CLIPProcessor(tokenizer=self.get_tokenizer()    ,      image_processor=self.get_image_processor()			)
       processor.save_pretrained(self.tmpdirname			)

       _snake_case					     =       self.get_tokenizer(bos_token='''(BOS)'''    ,      eos_token='''(EOS)'''			)
       _snake_case					     =       self.get_image_processor(do_normalize=_lowerCamelCase    ,      padding_value=1.0			)

       _snake_case					     =       CLIPProcessor.from_pretrained(
           self.tmpdirname    ,      bos_token='''(BOS)'''    ,      eos_token='''(EOS)'''    ,      do_normalize=_lowerCamelCase    ,      padding_value=1.0			)

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

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







     def      lowercase	(							self			:			int			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       image_processor(_lowerCamelCase    ,      return_tensors='''np'''			)
       _snake_case					     =       processor(images=_lowerCamelCase    ,      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      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''

       _snake_case					     =       processor(text=_lowerCamelCase			)

       _snake_case					     =       tokenizer(_lowerCamelCase			)

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







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''
       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       processor(text=_lowerCamelCase    ,      images=_lowerCamelCase			)

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

       # test if it raises when no input is passed
       with pytest.raises(_lowerCamelCase			):
         processor()







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       _snake_case					     =       processor.batch_decode(_lowerCamelCase			)
       _snake_case					     =       tokenizer.batch_decode(_lowerCamelCase			)

       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''
       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       processor(text=_lowerCamelCase    ,      images=_lowerCamelCase			)

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



