




import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @property
      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    1
          __UpperCamelCase			=    3
          __UpperCamelCase			=    8
          __UpperCamelCase			=    8

          __UpperCamelCase			=    torch.rand((batch_size, num_channels, height, width)					)

          return sample







      def    snake_case						(  self       :			int			,							snake_case       :			Optional[Any]					):







          if isinstance(image[0]			,							PIL.Image.Image					):
              return np.stack([np.array(__lowercase					) for i in image]			,							axis=0					)
          elif isinstance(__lowercase			,							torch.Tensor					):
              return image.cpu().numpy().transpose(0			,							2			,							3			,							1					)
          return image







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    VaeImageProcessor(do_resize=__lowercase			,							do_normalize=__lowercase					)

          __UpperCamelCase			=    self.dummy_sample
          __UpperCamelCase			=    self.to_np(__lowercase					)

          for output_type in ["pt", "np", "pil"]:
              __UpperCamelCase			=    image_processor.postprocess(
                  image_processor.preprocess(__lowercase					)			,							output_type=__lowercase			,							)
              __UpperCamelCase			=    self.to_np(__lowercase					)
              __UpperCamelCase			=    (input_np * 255).round() if output_type == '''pil''' else input_np
              assert (
                  np.abs(in_np - out_np					).max() < 1E-6
              ), F"decoded output does not match input for output_type {output_type}"







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    VaeImageProcessor(do_resize=__lowercase			,							do_normalize=__lowercase					)
          __UpperCamelCase			=    self.dummy_sample.cpu().numpy().transpose(0			,							2			,							3			,							1					)

          for output_type in ["pt", "np", "pil"]:
              __UpperCamelCase			=    image_processor.postprocess(image_processor.preprocess(__lowercase					)			,							output_type=__lowercase					)

              __UpperCamelCase			=    self.to_np(__lowercase					)
              __UpperCamelCase			=    (input_np * 255).round() if output_type == '''pil''' else input_np
              assert (
                  np.abs(in_np - out_np					).max() < 1E-6
              ), F"decoded output does not match input for output_type {output_type}"







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    VaeImageProcessor(do_resize=__lowercase			,							do_normalize=__lowercase					)

          __UpperCamelCase			=    self.dummy_sample.cpu().numpy().transpose(0			,							2			,							3			,							1					)
          __UpperCamelCase			=    image_processor.numpy_to_pil(__lowercase					)

          for output_type in ["pt", "np", "pil"]:
              __UpperCamelCase			=    image_processor.postprocess(image_processor.preprocess(__lowercase					)			,							output_type=__lowercase					)
              for i, o in zip(__lowercase			,							__lowercase					):
                  __UpperCamelCase			=    np.array(__lowercase					)
                  __UpperCamelCase			=    self.to_np(__lowercase					) if output_type == '''pil''' else (self.to_np(__lowercase					) * 255).round()
                  assert (
                      np.abs(in_np - out_np					).max() < 1E-6
                  ), F"decoded output does not match input for output_type {output_type}"







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    VaeImageProcessor(do_resize=__lowercase			,							do_normalize=__lowercase					)

          __UpperCamelCase			=    self.dummy_sample
          __UpperCamelCase			=    input_pt_ad.squeeze(0					)

          __UpperCamelCase			=    image_processor.postprocess(
              image_processor.preprocess(__lowercase					)			,							output_type='''np'''			,							)
          __UpperCamelCase			=    image_processor.postprocess(
              image_processor.preprocess(__lowercase					)			,							output_type='''np'''			,							)

          __UpperCamelCase			=    self.to_np(self.dummy_sample					)
          __UpperCamelCase			=    input_np_ad.squeeze(0					)

          __UpperCamelCase			=    image_processor.postprocess(
              image_processor.preprocess(__lowercase					)			,							output_type='''np'''			,							)
          __UpperCamelCase			=    image_processor.postprocess(
              image_processor.preprocess(__lowercase					)			,							output_type='''np'''			,							)

          assert np.abs(out_pt_ad - out_pt_ad					).max() < 1E-6
          assert np.abs(out_np_ad - out_np_ad					).max() < 1E-6







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    VaeImageProcessor(do_resize=__lowercase			,							do_normalize=__lowercase					)

          __UpperCamelCase			=    self.dummy_sample
          __UpperCamelCase			=    list(__lowercase					)

          __UpperCamelCase			=    image_processor.postprocess(
              image_processor.preprocess(__lowercase					)			,							output_type='''np'''			,							)

          __UpperCamelCase			=    image_processor.postprocess(
              image_processor.preprocess(__lowercase					)			,							output_type='''np'''			,							)

          __UpperCamelCase			=    self.to_np(self.dummy_sample					)
          list(__lowercase					)

          __UpperCamelCase			=    image_processor.postprocess(
              image_processor.preprocess(__lowercase					)			,							output_type='''np'''			,							)

          __UpperCamelCase			=    image_processor.postprocess(
              image_processor.preprocess(__lowercase					)			,							output_type='''np'''			,							)

          assert np.abs(out_pt_ad - out_pt_list					).max() < 1E-6
          assert np.abs(out_np_ad - out_np_list					).max() < 1E-6













from typing import TYPE_CHECKING

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


UpperCamelCase__ :			Union[str, Any]       	=			{
    """configuration_mobilevitv2""": [
        """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileViTV2Config""",
        """MobileViTV2OnnxConfig""",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			int       	=			[
				    """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__ :			List[Any]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


