
"""simple docstring"""
import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor




class  _snake_case	(    unittest.TestCase							):






							@property
							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       1
												__a:    str		     =       3
												__a:    List[Any]		     =       8
												__a:    int		     =       8

												__a:    Optional[int]		     =       torch.rand((batch_size, num_channels, height, width)     )

												return sample






							def 				__lowercase	(       self    :      Optional[Any]    ,			snake_case__    :      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 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    Tuple		     =       VaeImageProcessor(do_resize=snake_case__    ,			do_normalize=snake_case__     )

												__a:    Any		     =       self.dummy_sample
												__a:    Union[str, Any]		     =       self.to_np(snake_case__     )

												for output_type in ["pt", "np", "pil"]:
																	__a:    Any		     =       image_processor.postprocess(
																	    image_processor.preprocess(snake_case__     )    ,			output_type=snake_case__    ,			)
																	__a:    Optional[int]		     =       self.to_np(snake_case__     )
																	__a:    List[str]		     =       (input_np * 2_5_5).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 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    List[Any]		     =       VaeImageProcessor(do_resize=snake_case__    ,			do_normalize=snake_case__     )
												__a:    List[Any]		     =       self.dummy_sample.cpu().numpy().transpose(0    ,			2    ,			3    ,			1     )

												for output_type in ["pt", "np", "pil"]:
																	__a:    Any		     =       image_processor.postprocess(image_processor.preprocess(snake_case__     )    ,			output_type=snake_case__     )

																	__a:    int		     =       self.to_np(snake_case__     )
																	__a:    Optional[Any]		     =       (input_np * 2_5_5).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 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       VaeImageProcessor(do_resize=snake_case__    ,			do_normalize=snake_case__     )

												__a:    Any		     =       self.dummy_sample.cpu().numpy().transpose(0    ,			2    ,			3    ,			1     )
												__a:    Optional[Any]		     =       image_processor.numpy_to_pil(snake_case__     )

												for output_type in ["pt", "np", "pil"]:
																	__a:    Optional[int]		     =       image_processor.postprocess(image_processor.preprocess(snake_case__     )    ,			output_type=snake_case__     )
																	for i, o in zip(snake_case__    ,			snake_case__     ):
																						__a:    int		     =       np.array(snake_case__     )
																						__a:    Dict		     =       self.to_np(snake_case__     ) if output_type == """pil""" else (self.to_np(snake_case__     ) * 2_5_5).round()
																						assert (
																						    np.abs(in_np - out_np     ).max() < 1e-6
																						), f"""decoded output does not match input for output_type {output_type}"""






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    str		     =       VaeImageProcessor(do_resize=snake_case__    ,			do_normalize=snake_case__     )

												__a:    Optional[int]		     =       self.dummy_sample
												__a:    List[Any]		     =       input_pt_ad.squeeze(0     )

												__a:    int		     =       image_processor.postprocess(
												    image_processor.preprocess(snake_case__     )    ,			output_type="""np"""    ,			)
												__a:    Any		     =       image_processor.postprocess(
												    image_processor.preprocess(snake_case__     )    ,			output_type="""np"""    ,			)

												__a:    Union[str, Any]		     =       self.to_np(self.dummy_sample     )
												__a:    Optional[Any]		     =       input_np_ad.squeeze(0     )

												__a:    str		     =       image_processor.postprocess(
												    image_processor.preprocess(snake_case__     )    ,			output_type="""np"""    ,			)
												__a:    Any		     =       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 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												__a:    int		     =       VaeImageProcessor(do_resize=snake_case__    ,			do_normalize=snake_case__     )

												__a:    str		     =       self.dummy_sample
												__a:    Optional[int]		     =       list(snake_case__     )

												__a:    Union[str, Any]		     =       image_processor.postprocess(
												    image_processor.preprocess(snake_case__     )    ,			output_type="""np"""    ,			)

												__a:    Union[str, Any]		     =       image_processor.postprocess(
												    image_processor.preprocess(snake_case__     )    ,			output_type="""np"""    ,			)

												__a:    List[str]		     =       self.to_np(self.dummy_sample     )
												list(snake_case__     )

												__a:    Tuple		     =       image_processor.postprocess(
												    image_processor.preprocess(snake_case__     )    ,			output_type="""np"""    ,			)

												__a:    Optional[int]		     =       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_torch_available, is_vision_available


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

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       ['MobileNetV1FeatureExtractor']
			lowercase		:    Union[str, Any]			=       ['MobileNetV1ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Tuple			=       [
			    '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

			lowercase		:    Union[str, Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
