"""simple docstring"""



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 @property
 def        _lowerCAmelCase				(   self		:      str				)				->			Tuple:




    '''simple docstring'''






    a__     				=		1
    a__     				=		3
    a__     				=		8
    a__     				=		8

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

    return sample


 def        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      Tuple				)				->			int:




    '''simple docstring'''






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


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			int:




    '''simple docstring'''






    a__     				=		VaeImageProcessor(do_resize=_snake_case					,   do_normalize=_snake_case				)

    a__     				=		self.dummy_sample
    a__     				=		self.to_np(_snake_case				)

    for output_type in ["pt", "np", "pil"]:
       a__     				=		image_processor.postprocess(
           image_processor.preprocess(_snake_case				)					,   output_type=_snake_case					,   )
       a__     				=		self.to_np(_snake_case				)
       a__     				=		(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		:      int				)				->			Any:




    '''simple docstring'''






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

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

       a__     				=		self.to_np(_snake_case				)
       a__     				=		(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				)				->			int:




    '''simple docstring'''






    a__     				=		VaeImageProcessor(do_resize=_snake_case					,   do_normalize=_snake_case				)

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

    for output_type in ["pt", "np", "pil"]:
       a__     				=		image_processor.postprocess(image_processor.preprocess(_snake_case				)					,   output_type=_snake_case				)
       for i, o in zip(_snake_case					,   _snake_case				):
          a__     				=		np.array(_snake_case				)
          a__     				=		self.to_np(_snake_case				) if output_type == 'pil' else (self.to_np(_snake_case				) * 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		:      int				)				->			Tuple:




    '''simple docstring'''






    a__     				=		VaeImageProcessor(do_resize=_snake_case					,   do_normalize=_snake_case				)

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

    a__     				=		image_processor.postprocess(
        image_processor.preprocess(_snake_case				)					,   output_type='np'					,   )
    a__     				=		image_processor.postprocess(
        image_processor.preprocess(_snake_case				)					,   output_type='np'					,   )

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

    a__     				=		image_processor.postprocess(
        image_processor.preprocess(_snake_case				)					,   output_type='np'					,   )
    a__     				=		image_processor.postprocess(
        image_processor.preprocess(_snake_case				)					,   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		:      Any				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		VaeImageProcessor(do_resize=_snake_case					,   do_normalize=_snake_case				)

    a__     				=		self.dummy_sample
    a__     				=		list(_snake_case				)

    a__     				=		image_processor.postprocess(
        image_processor.preprocess(_snake_case				)					,   output_type='np'					,   )

    a__     				=		image_processor.postprocess(
        image_processor.preprocess(_snake_case				)					,   output_type='np'					,   )

    a__     				=		self.to_np(self.dummy_sample				)
    list(_snake_case				)

    a__     				=		image_processor.postprocess(
        image_processor.preprocess(_snake_case				)					,   output_type='np'					,   )

    a__     				=		image_processor.postprocess(
        image_processor.preprocess(_snake_case				)					,   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



"""simple docstring"""



from typing import TYPE_CHECKING

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


__magic_name__    				=					{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "HubertForCTC",
         "HubertForSequenceClassification",
         "HubertModel",
         "HubertPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFHubertForCTC",
         "TFHubertModel",
         "TFHubertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_hubert import (
              HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              HubertForCTC,
              HubertForSequenceClassification,
              HubertModel,
              HubertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_hubert import (
              TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFHubertForCTC,
              TFHubertModel,
              TFHubertPreTrainedModel,
          )


else:
     import sys

     __magic_name__    				=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


