



'''simple docstring'''






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor



class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		str     	=					1
        _UpperCAmelCase    :		Dict     	=					3
        _UpperCAmelCase    :		Dict     	=					8
        _UpperCAmelCase    :		Dict     	=					8

        _UpperCAmelCase    :		List[Any]     	=					torch.rand((batch_size, num_channels, height, width)				)

        return sample







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







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Union[str, Any]     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)

        _UpperCAmelCase    :		Optional[Any]     	=					self.dummy_sample
        _UpperCAmelCase    :		List[Any]     	=					self.to_np(a_				)

        for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase    :		Tuple     	=					image_processor.postprocess(
                   image_processor.preprocess(a_				)    ,output_type=a_    ,)
               _UpperCAmelCase    :		List[Any]     	=					self.to_np(a_				)
               _UpperCAmelCase    :		Union[str, Any]     	=					(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    :		Any     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)
        _UpperCAmelCase    :		Tuple     	=					self.dummy_sample.cpu().numpy().transpose(0    ,2    ,3    ,1				)

        for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase    :		str     	=					image_processor.postprocess(image_processor.preprocess(a_				)    ,output_type=a_				)

               _UpperCAmelCase    :		List[str]     	=					self.to_np(a_				)
               _UpperCAmelCase    :		Optional[Any]     	=					(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				)						->  Union[str, Any]:
        _UpperCAmelCase    :		Dict     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)

        _UpperCAmelCase    :		int     	=					self.dummy_sample.cpu().numpy().transpose(0    ,2    ,3    ,1				)
        _UpperCAmelCase    :		Any     	=					image_processor.numpy_to_pil(a_				)

        for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase    :		Dict     	=					image_processor.postprocess(image_processor.preprocess(a_				)    ,output_type=a_				)
               for i, o in zip(a_    ,a_				):
                      _UpperCAmelCase    :		List[Any]     	=					np.array(a_				)
                      _UpperCAmelCase    :		int     	=					self.to_np(a_				) if output_type == """pil""" else (self.to_np(a_				) * 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				)						->  Union[str, Any]:
        _UpperCAmelCase    :		List[Any]     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)

        _UpperCAmelCase    :		Optional[Any]     	=					self.dummy_sample
        _UpperCAmelCase    :		Union[str, Any]     	=					input_pt_ad.squeeze(0				)

        _UpperCAmelCase    :		Any     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		Any     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					self.to_np(self.dummy_sample				)
        _UpperCAmelCase    :		Tuple     	=					input_np_ad.squeeze(0				)

        _UpperCAmelCase    :		Dict     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		List[str]     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,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    :		Tuple     	=					VaeImageProcessor(do_resize=a_    ,do_normalize=a_				)

        _UpperCAmelCase    :		Dict     	=					self.dummy_sample
        _UpperCAmelCase    :		int     	=					list(a_				)

        _UpperCAmelCase    :		Tuple     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					self.to_np(self.dummy_sample				)
        list(a_				)

        _UpperCAmelCase    :		str     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		List[Any]     	=					image_processor.postprocess(
            image_processor.preprocess(a_				)    ,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_tf_available, is_torch_available


_lowerCamelCase							=      {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase							=      [
        '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CvtForImageClassification''',
        '''CvtModel''',
        '''CvtPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase							=      [
        '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFCvtForImageClassification''',
        '''TFCvtModel''',
        '''TFCvtPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cvt import (
            CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CvtForImageClassification,
            CvtModel,
            CvtPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_cvt import (
            TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCvtForImageClassification,
            TFCvtModel,
            TFCvtPreTrainedModel,
        )


else:
    import sys

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