from typing import TYPE_CHECKING

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


lowerCAmelCase__					=				{
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCAmelCase__					=				['MobileNetV1FeatureExtractor']
  lowerCAmelCase__					=				['MobileNetV1ImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCAmelCase__					=				[
      'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MobileNetV1ForImageClassification',
      'MobileNetV1Model',
      'MobileNetV1PreTrainedModel',
      'load_tf_weights_in_mobilenet_v1',
  ]


if TYPE_CHECKING:
  from .configuration_mobilenet_va import (
      MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileNetVaConfig,
      MobileNetVaOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
    from .image_processing_mobilenet_va import MobileNetVaImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileNetVaForImageClassification,
        MobileNetVaModel,
        MobileNetVaPreTrainedModel,
        load_tf_weights_in_mobilenet_va,
    )


else:
  import sys

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

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor
class        a_				(		unittest.TestCase		):






 '''simple docstring'''






 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      1
        lowerCAmelCase__  			=      3
        lowerCAmelCase__  			=      8
        lowerCAmelCase__  			=      8

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

        return sample




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Any):







        '''simple docstring'''



        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      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      self.to_np(lowercase__)

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(
                   image_processor.preprocess(lowercase__)    ,					output_type=lowercase__    ,					)
               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (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):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)
        lowerCAmelCase__  			=      self.dummy_sample.cpu().numpy().transpose(0    ,					2    ,					3    ,					1)

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

               lowerCAmelCase__  			=      self.to_np(lowercase__)
               lowerCAmelCase__  			=      (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      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

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

        for output_type in ["pt", "np", "pil"]:
               lowerCAmelCase__  			=      image_processor.postprocess(image_processor.preprocess(lowercase__)    ,					output_type=lowercase__)
               for i, o in zip(lowercase__    ,					lowercase__):
                      lowerCAmelCase__  			=      np.array(lowercase__)
                      lowerCAmelCase__  			=      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):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

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

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

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

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)
        lowerCAmelCase__  			=      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      :						List[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      VaeImageProcessor(do_resize=lowercase__    ,					do_normalize=lowercase__)

        lowerCAmelCase__  			=      self.dummy_sample
        lowerCAmelCase__  			=      list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      self.to_np(self.dummy_sample)
        list(lowercase__)

        lowerCAmelCase__  			=      image_processor.postprocess(
            image_processor.preprocess(lowercase__)    ,					output_type='np'    ,					)

        lowerCAmelCase__  			=      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
