

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class  __lowerCamelCase				(	unittest.TestCase	):







      @property
      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      1
        snake_case_       						=      3
        snake_case_       						=      8
        snake_case_       						=      8

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

        return sample







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase   )						->       Dict:
        if isinstance(image[0]							,  PIL.Image.Image   ):
          return np.stack([np.array(lowerCamelCase   ) for i in image]							,  axis=0   )
        elif isinstance(lowerCamelCase							,  torch.Tensor   ):
          return image.cpu().numpy().transpose(0							,  2							,  3							,  1   )
        return image







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_       						=      VaeImageProcessor(do_resize=lowerCamelCase							,  do_normalize=lowerCamelCase   )

        snake_case_       						=      self.dummy_sample
        snake_case_       						=      self.to_np(lowerCamelCase   )

        for output_type in ["pt", "np", "pil"]:
          snake_case_       						=      image_processor.postprocess(
              image_processor.preprocess(lowerCamelCase   )							,  output_type=lowerCamelCase							,  )
          snake_case_       						=      self.to_np(lowerCamelCase   )
          snake_case_       						=      (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 	lowerCAmelCase_    (     self   )						->       Any:
        snake_case_       						=      VaeImageProcessor(do_resize=lowerCamelCase							,  do_normalize=lowerCamelCase   )
        snake_case_       						=      self.dummy_sample.cpu().numpy().transpose(0							,  2							,  3							,  1   )

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

          snake_case_       						=      self.to_np(lowerCamelCase   )
          snake_case_       						=      (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 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      VaeImageProcessor(do_resize=lowerCamelCase							,  do_normalize=lowerCamelCase   )

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

        for output_type in ["pt", "np", "pil"]:
          snake_case_       						=      image_processor.postprocess(image_processor.preprocess(lowerCamelCase   )							,  output_type=lowerCamelCase   )
          for i, o in zip(lowerCamelCase							,  lowerCamelCase   ):
            snake_case_       						=      np.array(lowerCamelCase   )
            snake_case_       						=      self.to_np(lowerCamelCase   ) if output_type == """pil""" else (self.to_np(lowerCamelCase   ) * 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 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      VaeImageProcessor(do_resize=lowerCamelCase							,  do_normalize=lowerCamelCase   )

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

        snake_case_       						=      image_processor.postprocess(
            image_processor.preprocess(lowerCamelCase   )							,  output_type="""np"""							,  )
        snake_case_       						=      image_processor.postprocess(
            image_processor.preprocess(lowerCamelCase   )							,  output_type="""np"""							,  )

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

        snake_case_       						=      image_processor.postprocess(
            image_processor.preprocess(lowerCamelCase   )							,  output_type="""np"""							,  )
        snake_case_       						=      image_processor.postprocess(
            image_processor.preprocess(lowerCamelCase   )							,  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 	lowerCAmelCase_    (     self   )						->       str:
        snake_case_       						=      VaeImageProcessor(do_resize=lowerCamelCase							,  do_normalize=lowerCamelCase   )

        snake_case_       						=      self.dummy_sample
        snake_case_       						=      list(lowerCamelCase   )

        snake_case_       						=      image_processor.postprocess(
            image_processor.preprocess(lowerCamelCase   )							,  output_type="""np"""							,  )

        snake_case_       						=      image_processor.postprocess(
            image_processor.preprocess(lowerCamelCase   )							,  output_type="""np"""							,  )

        snake_case_       						=      self.to_np(self.dummy_sample   )
        list(lowerCamelCase   )

        snake_case_       						=      image_processor.postprocess(
            image_processor.preprocess(lowerCamelCase   )							,  output_type="""np"""							,  )

        snake_case_       						=      image_processor.postprocess(
            image_processor.preprocess(lowerCamelCase   )							,  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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

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