





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     :     List[Any]							=	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     :     Optional[int]							=	list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase     :     List[str]							=	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     __lowerCAmelCase	:
      _lowercase    : Optional[str]            =      field(
          default="""cifar10"""						,    metadata={"""help""": """Name of a dataset from the datasets package"""})
      _lowercase    : Optional[str]            =      field(
          default=UpperCamelCase__						,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
      _lowercase    : Optional[str]            =      field(
          default=UpperCamelCase__						,    metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}						,    )
      _lowercase    : Optional[str]            =      field(default=UpperCamelCase__						,    metadata={"""help""": """A folder containing the training data."""})
      _lowercase    : Optional[str]            =      field(default=UpperCamelCase__						,    metadata={"""help""": """A folder containing the validation data."""})
      _lowercase    : Optional[float]            =      field(
          default=0.1_5						,    metadata={"""help""": """Percent to split off of train for validation."""})
      _lowercase    : int            =      field(default=32						,    metadata={"""help""": """The size of the square patches to use for masking."""})
      _lowercase    : float            =      field(
          default=0.6						,    metadata={"""help""": """Percentage of patches to mask."""}						,    )
      _lowercase    : Optional[int]            =      field(
          default=UpperCamelCase__						,    metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of training examples to this """
                  """value if set."""
              )
          }						,    )
      _lowercase    : Optional[int]            =      field(
          default=UpperCamelCase__						,    metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                  """value if set."""
              )
          }						,    )







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[str]  ={}
        if self.train_dir is not None:
          a__     : Tuple  =self.train_dir
        if self.validation_dir is not None:
          a__     : Optional[Any]  =self.validation_dir
        a__     : List[str]  =data_files if data_files else None







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







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__=1_9_2		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__=4		,				lowerCAmelCase__=0.6							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Optional[Any]  =input_size
        a__     : Dict  =mask_patch_size
        a__     : List[Any]  =model_patch_size
        a__     : Optional[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"							)

        a__     : Tuple  =self.input_size // self.mask_patch_size
        a__     : Dict  =self.mask_patch_size // self.model_patch_size

        a__     : Any  =self.rand_size**2
        a__     : Optional[int]  =int(np.ceil(self.token_count * self.mask_ratio							)							)







      def __call__(   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[str]  =np.random.permutation(self.token_count							)[: self.mask_count]
        a__     : str  =np.zeros(self.token_count		,				dtype=lowerCAmelCase__							)
        a__     : List[str]  =1

        a__     : Union[str, Any]  =mask.reshape((self.rand_size, self.rand_size)							)
        a__     : Tuple  =mask.repeat(self.scale		,				axis=0							).repeat(self.scale		,				axis=1							)

        return torch.tensor(mask.flatten()							)
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__     : int  =torch.stack([example["pixel_values"] for example in examples]						)
  a__     : List[str]  =torch.stack([example["mask"] for example in examples]						)
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def 					_A	(		):





  """simple docstring"""



  a__     : int  =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.
    a__     : List[Any]  =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    a__     : str  =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"	,	SCREAMING_SNAKE_CASE	,	SCREAMING_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()

  a__     : Union[str, Any]  =training_args.get_process_log_level()
  logger.setLevel(SCREAMING_SNAKE_CASE						)
  transformers.utils.logging.set_verbosity(SCREAMING_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.
  a__     : str  =None
  if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
    a__     : Union[str, Any]  =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.
  a__     : Optional[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.
  a__     : Optional[int]  =None if "validation" in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split	,	SCREAMING_SNAKE_CASE						) and data_args.train_val_split > 0.0:
    a__     : str  =ds["train"].train_test_split(data_args.train_val_split						)
    a__     : Optional[int]  =split["train"]
    a__     : Dict  =split["test"]

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  a__     : Tuple  ={
      "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:
    a__     : int  =AutoConfig.from_pretrained(model_args.config_name_or_path	,	**SCREAMING_SNAKE_CASE						)
  elif model_args.model_name_or_path:
    a__     : Optional[Any]  =AutoConfig.from_pretrained(model_args.model_name_or_path	,	**SCREAMING_SNAKE_CASE						)
  else:
    a__     : 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(SCREAMING_SNAKE_CASE	,	"decoder_type"						):
    a__     : Optional[Any]  ="simmim"

  # adapt config
  a__     : List[Any]  =model_args.image_size if model_args.image_size is not None else config.image_size
  a__     : Union[str, Any]  =model_args.patch_size if model_args.patch_size is not None else config.patch_size
  a__     : List[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:
    a__     : Dict  =AutoImageProcessor.from_pretrained(model_args.image_processor_name	,	**SCREAMING_SNAKE_CASE						)
  elif model_args.model_name_or_path:
    a__     : str  =AutoImageProcessor.from_pretrained(model_args.model_name_or_path	,	**SCREAMING_SNAKE_CASE						)
  else:
    a__     : Tuple  ={
        conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
    }
    a__     : Any  =IMAGE_PROCESSOR_TYPES[model_args.model_type]()

  # create model
  if model_args.model_name_or_path:
    a__     : Dict  =AutoModelForMaskedImageModeling.from_pretrained(
        model_args.model_name_or_path	,	from_tf=bool(".ckpt" in model_args.model_name_or_path						)	,	config=SCREAMING_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"						)
    a__     : int  =AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE						)

  if training_args.do_train:
    a__     : Optional[Any]  =ds["train"].column_names
  else:
    a__     : Dict  =ds["validation"].column_names

  if data_args.image_column_name is not None:
    a__     : List[str]  =data_args.image_column_name
  elif "image" in column_names:
    a__     : str  ="image"
  elif "img" in column_names:
    a__     : int  ="img"
  else:
    a__     : Optional[Any]  =column_names[0]

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  a__     : str  =Compose(
      [
          Lambda(lambda SCREAMING_SNAKE_CASE						: img.convert("RGB"						) if img.mode != "RGB" else img						),
          RandomResizedCrop(model_args.image_size	,	scale=(0.6_7, 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
  a__     : Optional[int]  =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(SCREAMING_SNAKE_CASE					:       str						):
    a__     : int  =[transforms(SCREAMING_SNAKE_CASE						) for image in examples[image_column_name]]
    a__     : Tuple  =[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:
      a__     : List[Any]  =ds["train"].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
    # Set the training transforms
    ds["train"].set_transform(SCREAMING_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:
      a__     : int  =(
          ds["validation"].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
      )
    # Set the validation transforms
    ds["validation"].set_transform(SCREAMING_SNAKE_CASE						)

  # Initialize our trainer
  a__     : Union[str, Any]  =Trainer(
      model=SCREAMING_SNAKE_CASE	,	args=SCREAMING_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=SCREAMING_SNAKE_CASE	,	data_collator=SCREAMING_SNAKE_CASE	,	)

  # Training
  if training_args.do_train:
    a__     : Optional[Any]  =None
    if training_args.resume_from_checkpoint is not None:
      a__     : List[str]  =training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      a__     : int  =last_checkpoint
    a__     : Optional[int]  =trainer.train(resume_from_checkpoint=SCREAMING_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:
    a__     : str  =trainer.evaluate()
    trainer.log_metrics("eval"	,	SCREAMING_SNAKE_CASE						)
    trainer.save_metrics("eval"	,	SCREAMING_SNAKE_CASE						)

  # Write model card and (optionally) push to hub
  a__     : Tuple  ={
      "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(**SCREAMING_SNAKE_CASE						)
  else:
    trainer.create_model_card(**SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     main()











import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor







class     __lowerCAmelCase	(   UpperCamelCase__):







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Optional[int]  =self.config_class(**self.inputs_dict							)
        self.parent.assertTrue(hasattr(lowerCAmelCase__		,				"tf_padding"							)							)
        self.parent.assertTrue(hasattr(lowerCAmelCase__		,				"depth_multiplier"							)							)







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=1_3		,				lowerCAmelCase__=3		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__=0.25		,				lowerCAmelCase__=8		,				lowerCAmelCase__=True		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__="relu6"		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=1_0		,				lowerCAmelCase__=None		,				)       ->						Dict:



        '''simple docstring'''




        a__     : int  =parent
        a__     : Optional[Any]  =batch_size
        a__     : Tuple  =num_channels
        a__     : Dict  =image_size
        a__     : Union[str, Any]  =depth_multiplier
        a__     : List[str]  =min_depth
        a__     : Dict  =tf_padding
        a__     : Any  =int(last_hidden_size * depth_multiplier							)
        a__     : Tuple  =output_stride
        a__     : Optional[Any]  =hidden_act
        a__     : str  =classifier_dropout_prob
        a__     : int  =use_labels
        a__     : List[Any]  =is_training
        a__     : List[str]  =num_labels
        a__     : Dict  =initializer_range
        a__     : Tuple  =scope







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : Union[str, Any]  =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        a__     : Any  =None
        a__     : List[Any]  =None
        if self.use_labels:
          a__     : Dict  =ids_tensor([self.batch_size]		,				self.num_labels							)
          a__     : Tuple  =ids_tensor([self.batch_size, self.image_size, self.image_size]		,				self.num_labels							)

        a__     : Optional[Any]  =self.get_config()

        return config, pixel_values, labels, pixel_labels







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        return MobileNetVaConfig(
            num_channels=self.num_channels		,				image_size=self.image_size		,				depth_multiplier=self.depth_multiplier		,				min_depth=self.min_depth		,				tf_padding=self.tf_padding		,				hidden_act=self.hidden_act		,				classifier_dropout_prob=self.classifier_dropout_prob		,				initializer_range=self.initializer_range		,				)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  =MobileNetVaModel(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : str  =model(lowerCAmelCase__							)
        self.parent.assertEqual(
            result.last_hidden_state.shape		,				(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )		,				)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : Any  =self.num_labels
        a__     : Dict  =MobileNetVaForImageClassification(lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : Optional[int]  =model(lowerCAmelCase__		,				labels=lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : str  =self.prepare_config_and_inputs()
        a__ ,							a__ ,							a__ ,							a__     : List[str]  =config_and_inputs
        a__     : Tuple  ={"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class     __lowerCAmelCase	(   UpperCamelCase__						,    UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : List[str]            =      (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
      _lowercase    : Optional[int]            =      (
          {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
          if is_torch_available()
          else {}
      )

      _lowercase    : Union[str, Any]            =      False
      _lowercase    : Optional[int]            =      False
      _lowercase    : List[str]            =      False
      _lowercase    : str            =      False







      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Optional[Any]  =MobileNetVaModelTester(self							)
        a__     : Optional[Any]  =MobileNetVaConfigTester(self		,				config_class=lowerCAmelCase__		,				has_text_modality=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        self.config_tester.run_common_tests()







      @unittest.skip(reason="MobileNetV1 does not use inputs_embeds"							)
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        pass







      @unittest.skip(reason="MobileNetV1 does not support input and output embeddings"							)
      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        pass







      @unittest.skip(reason="MobileNetV1 does not output attentions"							)
      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        pass







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__ ,							a__     : Optional[int]  =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__     : Optional[int]  =model_class(lowerCAmelCase__							)
          a__     : str  =inspect.signature(model.forward							)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          a__     : Optional[int]  =[*signature.parameters.keys()]

          a__     : Union[str, Any]  =["pixel_values"]
          self.assertListEqual(arg_names[:1]		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Optional[Any]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        def check_hidden_states_output(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							):
          a__     : int  =model_class(lowerCAmelCase__							)
          model.to(lowerCAmelCase__							)
          model.eval()

          with torch.no_grad():
            a__     : List[Any]  =model(**self._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__							)							)

          a__     : Optional[int]  =outputs.hidden_states

          a__     : Optional[int]  =2_6
          self.assertEqual(len(lowerCAmelCase__							)		,				lowerCAmelCase__							)

        a__ ,							a__     : Optional[Any]  =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__     : Optional[int]  =True
          check_hidden_states_output(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          a__     : Union[str, Any]  =True

          check_hidden_states_output(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : Dict  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)







      @slow
      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__     : List[str]  =MobileNetVaModel.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)
def 					_A	(		):





  """simple docstring"""



  a__     : Optional[Any]  =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
  return image







@require_torch
@require_vision
class     __lowerCAmelCase	(   unittest.TestCase):







      @cached_property
      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        return (
            MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"							) if is_vision_available() else None
        )







      @slow
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : str  =MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"							).to(lowerCAmelCase__							)

        a__     : Optional[Any]  =self.default_image_processor
        a__     : Optional[int]  =prepare_img()
        a__     : Optional[int]  =image_processor(images=lowerCAmelCase__		,				return_tensors="pt"							).to(lowerCAmelCase__							)

        # forward pass
        with torch.no_grad():
          a__     : int  =model(**lowerCAmelCase__							)

        # verify the logits
        a__     : str  =torch.Size((1, 1_0_0_1)							)
        self.assertEqual(outputs.logits.shape		,				lowerCAmelCase__							)

        a__     : int  =torch.tensor([-4.17_39, -1.12_33, 3.12_05]							).to(lowerCAmelCase__							)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				lowerCAmelCase__		,				atol=1E-4							)							)






