




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__         =     {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     ['''DebertaV2TokenizerFast''']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDebertaV2ForMaskedLM''',
		    '''TFDebertaV2ForQuestionAnswering''',
		    '''TFDebertaV2ForSequenceClassification''',
		    '''TFDebertaV2ForTokenClassification''',
		    '''TFDebertaV2Model''',
		    '''TFDebertaV2PreTrainedModel''',
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DebertaV2ForMaskedLM''',
		    '''DebertaV2ForMultipleChoice''',
		    '''DebertaV2ForQuestionAnswering''',
		    '''DebertaV2ForSequenceClassification''',
		    '''DebertaV2ForTokenClassification''',
		    '''DebertaV2Model''',
		    '''DebertaV2PreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deberta_va import (
		    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DebertaVaConfig,
		    DebertaVaOnnxConfig,
		)
		from .tokenization_deberta_va import DebertaVaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta_va import (
				    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				    TFDebertaVaPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta_va import (
				    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaVaForMaskedLM,
				    DebertaVaForMultipleChoice,
				    DebertaVaForQuestionAnswering,
				    DebertaVaForSequenceClassification,
				    DebertaVaForTokenClassification,
				    DebertaVaModel,
				    DebertaVaPreTrainedModel,
				)

else:
		import sys

		a__         =     _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 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






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

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

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """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 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






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

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

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






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)
     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(lowercase_			),       output_type=lowercase_			)

       a__    =self.to_np(lowercase_			)
       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 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






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

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

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)
       for i, o in zip(lowercase_,       lowercase_			):
         a__    =np.array(lowercase_			)
         a__    =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 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






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

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

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

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

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






     """simple docstring"""






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

     a__    =self.dummy_sample
     a__    =list(lowercase_			)

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

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

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

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

     a__    =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
