

'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)


if is_vision_available():
  import PIL







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["pixel_values"]
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Dict[str, int] = None   ,				__SCREAMING_SNAKE_CASE   :			PILImageResampling = PILImageResampling.BICUBIC   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Dict[str, int] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Union[int, float] = 1 / 255   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[float, List[float]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[float, List[float]]] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				**__SCREAMING_SNAKE_CASE   :			Tuple   ,				)    ->					None:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = size if size is not None else {"""shortest_edge""": 224}
      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE   ,				default_to_square=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE   ,				default_to_square=__SCREAMING_SNAKE_CASE   ,				param_name="""crop_size"""     )

      __SCREAMING_SNAKE_CASE      = do_resize
      __SCREAMING_SNAKE_CASE      = size
      __SCREAMING_SNAKE_CASE      = resample
      __SCREAMING_SNAKE_CASE      = do_center_crop
      __SCREAMING_SNAKE_CASE      = crop_size
      __SCREAMING_SNAKE_CASE      = do_rescale
      __SCREAMING_SNAKE_CASE      = rescale_factor
      __SCREAMING_SNAKE_CASE      = do_normalize
      __SCREAMING_SNAKE_CASE      = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      __SCREAMING_SNAKE_CASE      = image_std if image_std is not None else OPENAI_CLIP_STD
      __SCREAMING_SNAKE_CASE      = do_convert_rgb
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			np.ndarray   ,				__SCREAMING_SNAKE_CASE   :			Dict[str, int]   ,				__SCREAMING_SNAKE_CASE   :			PILImageResampling = PILImageResampling.BICUBIC   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, ChannelDimension]] = None   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]   ,				)    ->					np.ndarray:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE   ,				default_to_square=__SCREAMING_SNAKE_CASE     )
      if "shortest_edge" not in size:
           raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'     )
      __SCREAMING_SNAKE_CASE      = get_resize_output_image_size(__SCREAMING_SNAKE_CASE   ,				size=size["""shortest_edge"""]   ,				default_to_square=__SCREAMING_SNAKE_CASE     )
      return resize(__SCREAMING_SNAKE_CASE   ,				size=__SCREAMING_SNAKE_CASE   ,				resample=__SCREAMING_SNAKE_CASE   ,				data_format=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			np.ndarray   ,				__SCREAMING_SNAKE_CASE   :			Dict[str, int]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, ChannelDimension]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				)    ->					np.ndarray:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE     )
      if "height" not in size or "width" not in size:
           raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'     )
      return center_crop(__SCREAMING_SNAKE_CASE   ,				size=(size["""height"""], size["""width"""])   ,				data_format=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			np.ndarray   ,				__SCREAMING_SNAKE_CASE   :			Union[int, float]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, ChannelDimension]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				)    ->					Optional[Any]:


      """simple docstring"""




      return rescale(__SCREAMING_SNAKE_CASE   ,				scale=__SCREAMING_SNAKE_CASE   ,				data_format=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			np.ndarray   ,				__SCREAMING_SNAKE_CASE   :			Union[float, List[float]]   ,				__SCREAMING_SNAKE_CASE   :			Union[float, List[float]]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, ChannelDimension]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					np.ndarray:


      """simple docstring"""




      return normalize(__SCREAMING_SNAKE_CASE   ,				mean=__SCREAMING_SNAKE_CASE   ,				std=__SCREAMING_SNAKE_CASE   ,				data_format=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			ImageInput   ,				__SCREAMING_SNAKE_CASE   :			bool = None   ,				__SCREAMING_SNAKE_CASE   :			Dict[str, int] = None   ,				__SCREAMING_SNAKE_CASE   :			PILImageResampling = None   ,				__SCREAMING_SNAKE_CASE   :			bool = None   ,				__SCREAMING_SNAKE_CASE   :			int = None   ,				__SCREAMING_SNAKE_CASE   :			bool = None   ,				__SCREAMING_SNAKE_CASE   :			float = None   ,				__SCREAMING_SNAKE_CASE   :			bool = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[float, List[float]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[float, List[float]]] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, TensorType]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[ChannelDimension] = ChannelDimension.FIRST   ,				**__SCREAMING_SNAKE_CASE   :			str   ,				)    ->					PIL.Image.Image:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE      = size if size is not None else self.size
      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE   ,				param_name="""size"""   ,				default_to_square=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE      = do_center_crop if do_center_crop is not None else self.do_center_crop
      __SCREAMING_SNAKE_CASE      = crop_size if crop_size is not None else self.crop_size
      __SCREAMING_SNAKE_CASE      = get_size_dict(__SCREAMING_SNAKE_CASE   ,				param_name="""crop_size"""   ,				default_to_square=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = do_rescale if do_rescale is not None else self.do_rescale
      __SCREAMING_SNAKE_CASE      = rescale_factor if rescale_factor is not None else self.rescale_factor
      __SCREAMING_SNAKE_CASE      = do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE      = image_mean if image_mean is not None else self.image_mean
      __SCREAMING_SNAKE_CASE      = image_std if image_std is not None else self.image_std
      __SCREAMING_SNAKE_CASE      = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      __SCREAMING_SNAKE_CASE      = make_list_of_images(__SCREAMING_SNAKE_CASE     )

      if not valid_images(__SCREAMING_SNAKE_CASE     ):
           raise ValueError(
               """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
               """torch.Tensor, tf.Tensor or jax.ndarray."""     )

      if do_resize and size is None:
           raise ValueError("""Size must be specified if do_resize is True."""     )

      if do_center_crop and crop_size is None:
           raise ValueError("""Crop size must be specified if do_center_crop is True."""     )

      if do_rescale and rescale_factor is None:
           raise ValueError("""Rescale factor must be specified if do_rescale is True."""     )

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("""Image mean and std must be specified if do_normalize is True."""     )

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
           __SCREAMING_SNAKE_CASE      = [convert_to_rgb(__SCREAMING_SNAKE_CASE     ) for image in images]

      # All transformations expect numpy arrays.
      __SCREAMING_SNAKE_CASE      = [to_numpy_array(__SCREAMING_SNAKE_CASE     ) for image in images]

      if do_resize:
           __SCREAMING_SNAKE_CASE      = [self.resize(image=__SCREAMING_SNAKE_CASE   ,				size=__SCREAMING_SNAKE_CASE   ,				resample=__SCREAMING_SNAKE_CASE     ) for image in images]

      if do_center_crop:
           __SCREAMING_SNAKE_CASE      = [self.center_crop(image=__SCREAMING_SNAKE_CASE   ,				size=__SCREAMING_SNAKE_CASE     ) for image in images]

      if do_rescale:
           __SCREAMING_SNAKE_CASE      = [self.rescale(image=__SCREAMING_SNAKE_CASE   ,				scale=__SCREAMING_SNAKE_CASE     ) for image in images]

      if do_normalize:
           __SCREAMING_SNAKE_CASE      = [self.normalize(image=__SCREAMING_SNAKE_CASE   ,				mean=__SCREAMING_SNAKE_CASE   ,				std=__SCREAMING_SNAKE_CASE     ) for image in images]

      __SCREAMING_SNAKE_CASE      = [to_channel_dimension_format(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ) for image in images]

      __SCREAMING_SNAKE_CASE      = {"""pixel_values""": images}
      return BatchFeature(data=__SCREAMING_SNAKE_CASE   ,				tensor_type=__SCREAMING_SNAKE_CASE     )









'''simple docstring'''



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__  ( a   ):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.config_class(**self.inputs_dict     )
      self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""tf_padding"""     )     )
      self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""depth_multiplier"""     )     )







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			str=13   ,				__SCREAMING_SNAKE_CASE   :			int=3   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=32   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0.25   ,				__SCREAMING_SNAKE_CASE   :			List[str]=8   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			int=1_024   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=32   ,				__SCREAMING_SNAKE_CASE   :			List[Any]="relu6"   ,				__SCREAMING_SNAKE_CASE   :			str=0.1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=True   ,				__SCREAMING_SNAKE_CASE   :			Tuple=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=10   ,				__SCREAMING_SNAKE_CASE   :			int=None   ,				)    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = batch_size
      __SCREAMING_SNAKE_CASE      = num_channels
      __SCREAMING_SNAKE_CASE      = image_size
      __SCREAMING_SNAKE_CASE      = depth_multiplier
      __SCREAMING_SNAKE_CASE      = min_depth
      __SCREAMING_SNAKE_CASE      = tf_padding
      __SCREAMING_SNAKE_CASE      = int(last_hidden_size * depth_multiplier     )
      __SCREAMING_SNAKE_CASE      = output_stride
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = classifier_dropout_prob
      __SCREAMING_SNAKE_CASE      = use_labels
      __SCREAMING_SNAKE_CASE      = is_training
      __SCREAMING_SNAKE_CASE      = num_labels
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = scope
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = None
      if self.use_labels:
           __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size]   ,				self.num_labels     )
           __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.image_size, self.image_size]   ,				self.num_labels     )

      __SCREAMING_SNAKE_CASE      = self.get_config()

      return config, pixel_values, labels, pixel_labels
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Tuple:


      """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  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			int     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaModel(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE     )
      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  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.num_labels
      __SCREAMING_SNAKE_CASE      = MobileNetVaForImageClassification(__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				labels=__SCREAMING_SNAKE_CASE     )
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )


 def  UpperCAmelCase__						(						self   :			str     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = config_and_inputs
      __SCREAMING_SNAKE_CASE      = {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
 lowerCAmelCase__       = (
     {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
     if is_torch_available()
     else {}
 )

 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 def  UpperCAmelCase__						(						self   :			str     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaModelTester(self     )
      __SCREAMING_SNAKE_CASE      = MobileNetVaConfigTester(self   ,				config_class=__SCREAMING_SNAKE_CASE   ,				has_text_modality=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Any:


      """simple docstring"""




      self.config_tester.run_common_tests()
 @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[Any]:


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					List[Any]:


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileNetV1 does not output attentions"""     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




      pass
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

           __SCREAMING_SNAKE_CASE      = ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[Any]:


      """simple docstring"""




      def check_hidden_states_output(__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			str     ):
           __SCREAMING_SNAKE_CASE      = model_class(__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()

           with torch.no_grad():
                __SCREAMING_SNAKE_CASE      = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )     )

           __SCREAMING_SNAKE_CASE      = outputs.hidden_states

           __SCREAMING_SNAKE_CASE      = 26
           self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           __SCREAMING_SNAKE_CASE      = True
           check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __SCREAMING_SNAKE_CASE      = True

           check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE     )


 @slow
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Optional[Any]:


      """simple docstring"""




      for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE      = MobileNetVaModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE     )







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     return image







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



 """simple docstring"""



 @cached_property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[str]:


      """simple docstring"""




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


 @slow
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self.default_image_processor
      __SCREAMING_SNAKE_CASE      = prepare_img()
      __SCREAMING_SNAKE_CASE      = image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors="""pt"""     ).to(__SCREAMING_SNAKE_CASE     )

      # forward pass
      with torch.no_grad():
           __SCREAMING_SNAKE_CASE      = model(**__SCREAMING_SNAKE_CASE     )

      # verify the logits
      __SCREAMING_SNAKE_CASE      = torch.Size((1, 1_001)     )
      self.assertEqual(outputs.logits.shape   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.tensor([-4.1739, -1.1233, 3.1205]     ).to(__SCREAMING_SNAKE_CASE     )

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






