





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	@property
	def       snake_case__(	self:	str ):
			lowercase__				:			int            =		1
			lowercase__				:			Any            =		3
			lowercase__				:			str            =		8
			lowercase__				:			Optional[Any]            =		8

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

			return sample

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

	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			List[str]            =		VaeImageProcessor(do_resize=lowerCamelCase_,				do_normalize=lowerCamelCase_ )

			lowercase__				:			Tuple            =		self.dummy_sample
			lowercase__				:			List[Any]            =		self.to_np(lowerCamelCase_ )

			for output_type in ["pt", "np", "pil"]:
					lowercase__				:			Optional[Any]            =		image_processor.postprocess(
					    image_processor.preprocess(lowerCamelCase_ ),				output_type=lowerCamelCase_,				)
					lowercase__				:			Union[str, Any]            =		self.to_np(lowerCamelCase_ )
					lowercase__				:			Optional[int]            =		(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       snake_case__(	self:	Tuple ):
			lowercase__				:			Optional[Any]            =		VaeImageProcessor(do_resize=lowerCamelCase_,				do_normalize=lowerCamelCase_ )
			lowercase__				:			Any            =		self.dummy_sample.cpu().numpy().transpose(0,				2,				3,				1 )

			for output_type in ["pt", "np", "pil"]:
					lowercase__				:			Dict            =		image_processor.postprocess(image_processor.preprocess(lowerCamelCase_ ),				output_type=lowerCamelCase_ )

					lowercase__				:			Union[str, Any]            =		self.to_np(lowerCamelCase_ )
					lowercase__				:			Tuple            =		(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       snake_case__(	self:	int ):
			lowercase__				:			int            =		VaeImageProcessor(do_resize=lowerCamelCase_,				do_normalize=lowerCamelCase_ )

			lowercase__				:			List[str]            =		self.dummy_sample.cpu().numpy().transpose(0,				2,				3,				1 )
			lowercase__				:			Dict            =		image_processor.numpy_to_pil(lowerCamelCase_ )

			for output_type in ["pt", "np", "pil"]:
					lowercase__				:			Any            =		image_processor.postprocess(image_processor.preprocess(lowerCamelCase_ ),				output_type=lowerCamelCase_ )
					for i, o in zip(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			Union[str, Any]            =		np.array(lowerCamelCase_ )
							lowercase__				:			Optional[int]            =		self.to_np(lowerCamelCase_ ) if output_type == 'pil' else (self.to_np(lowerCamelCase_ ) * 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       snake_case__(	self:	List[str] ):
			lowercase__				:			Optional[int]            =		VaeImageProcessor(do_resize=lowerCamelCase_,				do_normalize=lowerCamelCase_ )

			lowercase__				:			Tuple            =		self.dummy_sample
			lowercase__				:			Tuple            =		input_pt_ad.squeeze(0 )

			lowercase__				:			str            =		image_processor.postprocess(
			    image_processor.preprocess(lowerCamelCase_ ),				output_type='np',				)
			lowercase__				:			Any            =		image_processor.postprocess(
			    image_processor.preprocess(lowerCamelCase_ ),				output_type='np',				)

			lowercase__				:			Any            =		self.to_np(self.dummy_sample )
			lowercase__				:			str            =		input_np_ad.squeeze(0 )

			lowercase__				:			Optional[Any]            =		image_processor.postprocess(
			    image_processor.preprocess(lowerCamelCase_ ),				output_type='np',				)
			lowercase__				:			Tuple            =		image_processor.postprocess(
			    image_processor.preprocess(lowerCamelCase_ ),				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       snake_case__(	self:	Dict ):
			lowercase__				:			str            =		VaeImageProcessor(do_resize=lowerCamelCase_,				do_normalize=lowerCamelCase_ )

			lowercase__				:			Any            =		self.dummy_sample
			lowercase__				:			Optional[Any]            =		list(lowerCamelCase_ )

			lowercase__				:			Any            =		image_processor.postprocess(
			    image_processor.preprocess(lowerCamelCase_ ),				output_type='np',				)

			lowercase__				:			List[Any]            =		image_processor.postprocess(
			    image_processor.preprocess(lowerCamelCase_ ),				output_type='np',				)

			lowercase__				:			Any            =		self.to_np(self.dummy_sample )
			list(lowerCamelCase_ )

			lowercase__				:			Optional[int]            =		image_processor.postprocess(
			    image_processor.preprocess(lowerCamelCase_ ),				output_type='np',				)

			lowercase__				:			List[str]            =		image_processor.postprocess(
			    image_processor.preprocess(lowerCamelCase_ ),				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









from typing import TYPE_CHECKING

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


__UpperCamelCase:  Any   =   {
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   ["""DetaImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[str]   =   [
			    """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DetaForObjectDetection""",
			    """DetaModel""",
			    """DetaPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_deta import DetaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deta import (
						    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DetaForObjectDetection,
						    DetaModel,
						    DetaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  int   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


