






'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase		:		int								=			{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		Optional[Any]								=			['SegformerFeatureExtractor']
						_UpperCamelCase		:		Optional[Any]								=			['SegformerImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		Optional[int]								=			[
						    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'SegformerDecodeHead',
						    'SegformerForImageClassification',
						    'SegformerForSemanticSegmentation',
						    'SegformerLayer',
						    'SegformerModel',
						    'SegformerPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		int								=			[
						    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFSegformerDecodeHead',
						    'TFSegformerForImageClassification',
						    'TFSegformerForSemanticSegmentation',
						    'TFSegformerModel',
						    'TFSegformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_segformer import SegformerFeatureExtractor
												from .image_processing_segformer import SegformerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_segformer import (
												    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SegformerDecodeHead,
												    SegformerForImageClassification,
												    SegformerForSemanticSegmentation,
												    SegformerLayer,
												    SegformerModel,
												    SegformerPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_segformer import (
												    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSegformerDecodeHead,
												    TFSegformerForImageClassification,
												    TFSegformerForSemanticSegmentation,
												    TFSegformerModel,
												    TFSegformerPreTrainedModel,
												)

else:
						import sys

						_UpperCamelCase		:		Optional[Any]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''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 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Optional[int]       =   1
								UpperCAmelCase_     :   List[str]       =   3
								UpperCAmelCase_     :   Optional[int]       =   8
								UpperCAmelCase_     :   Optional[Any]       =   8

								UpperCAmelCase_     :   Any       =   torch.rand((batch_size, num_channels, height, width)     )

								return sample




					def 						A     (       self       :						Dict							,				_A       :						str     )  ->    List[Any]:
								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 						A     (       self       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[int]       =   self.to_np(_A     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Any       =   image_processor.postprocess(
											    image_processor.preprocess(_A     )							,				output_type=_A							,				)
											UpperCAmelCase_     :   Union[str, Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Union[str, Any]       =   (input_np * 2_55).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 						A     (       self       :						Optional[Any]     )  ->    int:
								UpperCAmelCase_     :   Union[str, Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )
								UpperCAmelCase_     :   List[Any]       =   self.dummy_sample.cpu().numpy().transpose(0							,				2							,				3							,				1     )

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   Union[str, Any]       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )

											UpperCAmelCase_     :   List[Any]       =   self.to_np(_A     )
											UpperCAmelCase_     :   Any       =   (input_np * 2_55).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 						A     (       self       :						Any     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Tuple       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

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

								for output_type in ["pt", "np", "pil"]:
											UpperCAmelCase_     :   str       =   image_processor.postprocess(image_processor.preprocess(_A     )							,				output_type=_A     )
											for i, o in zip(_A							,				_A     ):
														UpperCAmelCase_     :   Optional[int]       =   np.array(_A     )
														UpperCAmelCase_     :   Dict       =   self.to_np(_A     ) if output_type == '''pil''' else (self.to_np(_A     ) * 2_55).round()
														assert (
														    np.abs(in_np - out_np     ).max() < 1e-6
														), F"decoded output does not match input for output_type {output_type}"




					def 						A     (       self       :						Optional[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   int       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[Any]       =   self.dummy_sample
								UpperCAmelCase_     :   Optional[Any]       =   input_pt_ad.squeeze(0     )

								UpperCAmelCase_     :   Tuple       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Any       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   str       =   self.to_np(self.dummy_sample     )
								UpperCAmelCase_     :   Tuple       =   input_np_ad.squeeze(0     )

								UpperCAmelCase_     :   Optional[int]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)
								UpperCAmelCase_     :   Optional[Any]       =   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 						A     (       self       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   List[Any]       =   VaeImageProcessor(do_resize=_A							,				do_normalize=_A     )

								UpperCAmelCase_     :   Optional[int]       =   self.dummy_sample
								UpperCAmelCase_     :   Dict       =   list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[Any]       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Dict       =   self.to_np(self.dummy_sample     )
								list(_A     )

								UpperCAmelCase_     :   str       =   image_processor.postprocess(
								    image_processor.preprocess(_A     )							,				output_type='''np'''							,				)

								UpperCAmelCase_     :   Optional[int]       =   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


