




'''simple docstring'''







import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor





class 					lowerCamelCase_							(	unittest.TestCase							):




					'''simple docstring'''






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							_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     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							if isinstance(image[0],  PIL.Image.Image			):
									return np.stack([np.array(__UpperCamelCase			) for i in image],  axis=0			)
							elif isinstance(__UpperCamelCase,  torch.Tensor			):
									return image.cpu().numpy().transpose(0,  2,  3,  1			)
							return image






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	VaeImageProcessor(do_resize=__UpperCamelCase,  do_normalize=__UpperCamelCase			)

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

							for output_type in ["pt", "np", "pil"]:
									_snake_case     	=	image_processor.postprocess(
									    image_processor.preprocess(__UpperCamelCase			),  output_type=__UpperCamelCase,  )
									_snake_case     	=	self.to_np(__UpperCamelCase			)
									_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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	VaeImageProcessor(do_resize=__UpperCamelCase,  do_normalize=__UpperCamelCase			)
							_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(__UpperCamelCase			),  output_type=__UpperCamelCase			)

									_snake_case     	=	self.to_np(__UpperCamelCase			)
									_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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	VaeImageProcessor(do_resize=__UpperCamelCase,  do_normalize=__UpperCamelCase			)

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

							for output_type in ["pt", "np", "pil"]:
									_snake_case     	=	image_processor.postprocess(image_processor.preprocess(__UpperCamelCase			),  output_type=__UpperCamelCase			)
									for i, o in zip(__UpperCamelCase,  __UpperCamelCase			):
											_snake_case     	=	np.array(__UpperCamelCase			)
											_snake_case     	=	self.to_np(__UpperCamelCase			) if output_type == "pil" else (self.to_np(__UpperCamelCase			) * 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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	VaeImageProcessor(do_resize=__UpperCamelCase,  do_normalize=__UpperCamelCase			)

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

							_snake_case     	=	image_processor.postprocess(
							    image_processor.preprocess(__UpperCamelCase			),  output_type="np",  )
							_snake_case     	=	image_processor.postprocess(
							    image_processor.preprocess(__UpperCamelCase			),  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(__UpperCamelCase			),  output_type="np",  )
							_snake_case     	=	image_processor.postprocess(
							    image_processor.preprocess(__UpperCamelCase			),  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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	VaeImageProcessor(do_resize=__UpperCamelCase,  do_normalize=__UpperCamelCase			)

							_snake_case     	=	self.dummy_sample
							_snake_case     	=	list(__UpperCamelCase			)

							_snake_case     	=	image_processor.postprocess(
							    image_processor.preprocess(__UpperCamelCase			),  output_type="np",  )

							_snake_case     	=	image_processor.postprocess(
							    image_processor.preprocess(__UpperCamelCase			),  output_type="np",  )

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

							_snake_case     	=	image_processor.postprocess(
							    image_processor.preprocess(__UpperCamelCase			),  output_type="np",  )

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


_lowerCamelCase							:						List[Any]							     =  {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:						str							     =  ['MobileNetV1FeatureExtractor']
						_lowerCamelCase							:						Tuple							     =  ['MobileNetV1ImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:						List[Any]							     =  [
						    '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

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





