
"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViltImageProcessor





class 							snake_case       (     unittest.TestCase		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=7       ,							__UpperCAmelCase=3       ,							__UpperCAmelCase=18       ,							__UpperCAmelCase=30       ,							__UpperCAmelCase=4_00       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=2       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							)   ->Dict:
      a_						   =     size if size is not None else {"shortest_edge": 30}
      a_						   =     parent
      a_						   =     batch_size
      a_						   =     num_channels
      a_						   =     image_size
      a_						   =     min_resolution
      a_						   =     max_resolution
      a_						   =     do_resize
      a_						   =     size
      a_						   =     size_divisor
      a_						   =     do_normalize
      a_						   =     image_mean
      a_						   =     image_std







   def    UpperCAmelCase__	( self)   ->List[Any]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=False)   ->str:
      if not batched:
         a_						   =     self.size["shortest_edge"]
         a_						   =     image_inputs[0]
         if isinstance(__UpperCAmelCase       ,							Image.Image):
            a_   ,			a_						   =     image.size
         else:
            a_   ,			a_						   =     image.shape[1], image.shape[2]
         a_						   =     size / min(__UpperCAmelCase       ,							__UpperCAmelCase)
         if h < w:
            a_   ,			a_						   =     size, scale * w
         else:
            a_   ,			a_						   =     scale * h, size

         a_						   =     int((13_33 / 8_00) * size)
         if max(__UpperCAmelCase       ,							__UpperCAmelCase) > max_size:
            a_						   =     max_size / max(__UpperCAmelCase       ,							__UpperCAmelCase)
            a_						   =     newh * scale
            a_						   =     neww * scale

         a_   ,			a_						   =     int(newh + 0.5), int(neww + 0.5)
         a_   ,			a_						   =     (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

      else:
         a_						   =     []
         for image in image_inputs:
            a_   ,			a_						   =     self.get_expected_values([image])
            expected_values.append((expected_height, expected_width))
         a_						   =     max(__UpperCAmelCase       ,							key=lambda __UpperCAmelCase: item[0])[0]
         a_						   =     max(__UpperCAmelCase       ,							key=lambda __UpperCAmelCase: item[1])[1]

      return expected_height, expected_width






@require_torch
@require_vision
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		List[str]     					=     ViltImageProcessor if is_vision_available() else None







   def    UpperCAmelCase__	( self)   ->int:
      a_						   =     ViltImageProcessingTester(self)







   @property
   def    UpperCAmelCase__	( self)   ->Any:
      return self.image_processor_tester.prepare_image_processor_dict()







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_mean"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_std"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_normalize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_resize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"size"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"size_divisor"))







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size       ,							{"shortest_edge": 30})

      a_						   =     self.image_processing_class.from_dict(self.image_processor_dict       ,							size=42)
      self.assertEqual(image_processor.size       ,							{"shortest_edge": 42})







   def    UpperCAmelCase__	( self)   ->Any:
      pass







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Initialize image_processing
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      a_						   =     prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase)
      for image in image_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							Image.Image)

      # Test not batched input
      a_						   =     image_processing(image_inputs[0]       ,							return_tensors="pt").pixel_values

      a_   ,			a_						   =     self.image_processor_tester.get_expected_values(__UpperCAmelCase)
      self.assertEqual(
          encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

      # Test batched
      a_						   =     image_processing(__UpperCAmelCase       ,							return_tensors="pt").pixel_values

      a_   ,			a_						   =     self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,							batched=__UpperCAmelCase)
      self.assertEqual(
          encoded_images.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )       ,							)







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Initialize image_processing
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a_						   =     prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							numpify=__UpperCAmelCase)
      for image in image_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							np.ndarray)

      # Test not batched input
      a_						   =     image_processing(image_inputs[0]       ,							return_tensors="pt").pixel_values

      a_   ,			a_						   =     self.image_processor_tester.get_expected_values(__UpperCAmelCase)
      self.assertEqual(
          encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

      # Test batched
      a_						   =     image_processing(__UpperCAmelCase       ,							return_tensors="pt").pixel_values

      a_   ,			a_						   =     self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,							batched=__UpperCAmelCase)
      self.assertEqual(
          encoded_images.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )       ,							)







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      # Initialize image_processing
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a_						   =     prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							torchify=__UpperCAmelCase)
      for image in image_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							torch.Tensor)

      # Test not batched input
      a_						   =     image_processing(image_inputs[0]       ,							return_tensors="pt").pixel_values

      a_   ,			a_						   =     self.image_processor_tester.get_expected_values(__UpperCAmelCase)
      self.assertEqual(
          encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

      # Test batched
      a_						   =     image_processing(__UpperCAmelCase       ,							return_tensors="pt").pixel_values

      a_   ,			a_						   =     self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,							batched=__UpperCAmelCase)
      self.assertEqual(
          encoded_images.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )       ,							)

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_						   =   {
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MobileViTV2ForImageClassification',
          'MobileViTV2ForSemanticSegmentation',
          'MobileViTV2Model',
          'MobileViTV2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_mobilevitva import (
          MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileViTVaConfig,
          MobileViTVaOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevitva import (
                MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTVaForImageClassification,
                MobileViTVaForSemanticSegmentation,
                MobileViTVaModel,
                MobileViTVaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase_						   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)