


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor



class 							A					( unittest.TestCase							):
					'''simple docstring'''






					@property
					def 						lowerCamelCase__  (self					:		List[Any]	)		->     str:




							"""simple docstring"""


							lowercase__      =	1
							lowercase__      =	3
							lowercase__      =	8
							lowercase__      =	8

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

							return sample

					def 						lowerCamelCase__  (self					:		List[str]   ,      _UpperCAmelCase					:		Optional[Any]	)		->     str:




							"""simple docstring"""


							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 						lowerCamelCase__  (self					:		Optional[Any]	)		->     List[str]:




							"""simple docstring"""


							lowercase__      =	VaeImageProcessor(do_resize=_UpperCAmelCase   ,      do_normalize=_UpperCAmelCase	)

							lowercase__      =	self.dummy_sample
							lowercase__      =	self.to_np(_UpperCAmelCase	)

							for output_type in ["pt", "np", "pil"]:
									lowercase__      =	image_processor.postprocess(
									    image_processor.preprocess(_UpperCAmelCase	)   ,      output_type=_UpperCAmelCase   ,      )
									lowercase__      =	self.to_np(_UpperCAmelCase	)
									lowercase__      =	(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 						lowerCamelCase__  (self					:		int	)		->     int:




							"""simple docstring"""


							lowercase__      =	VaeImageProcessor(do_resize=_UpperCAmelCase   ,      do_normalize=_UpperCAmelCase	)
							lowercase__      =	self.dummy_sample.cpu().numpy().transpose(0   ,      2   ,      3   ,      1	)

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

									lowercase__      =	self.to_np(_UpperCAmelCase	)
									lowercase__      =	(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 						lowerCamelCase__  (self					:		Tuple	)		->     Tuple:




							"""simple docstring"""


							lowercase__      =	VaeImageProcessor(do_resize=_UpperCAmelCase   ,      do_normalize=_UpperCAmelCase	)

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

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




							"""simple docstring"""


							lowercase__      =	VaeImageProcessor(do_resize=_UpperCAmelCase   ,      do_normalize=_UpperCAmelCase	)

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

							lowercase__      =	image_processor.postprocess(
							    image_processor.preprocess(_UpperCAmelCase	)   ,      output_type="""np"""   ,      )
							lowercase__      =	image_processor.postprocess(
							    image_processor.preprocess(_UpperCAmelCase	)   ,      output_type="""np"""   ,      )

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

							lowercase__      =	image_processor.postprocess(
							    image_processor.preprocess(_UpperCAmelCase	)   ,      output_type="""np"""   ,      )
							lowercase__      =	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 						lowerCamelCase__  (self					:		Any	)		->     int:




							"""simple docstring"""


							lowercase__      =	VaeImageProcessor(do_resize=_UpperCAmelCase   ,      do_normalize=_UpperCAmelCase	)

							lowercase__      =	self.dummy_sample
							lowercase__      =	list(_UpperCAmelCase	)

							lowercase__      =	image_processor.postprocess(
							    image_processor.preprocess(_UpperCAmelCase	)   ,      output_type="""np"""   ,      )

							lowercase__      =	image_processor.postprocess(
							    image_processor.preprocess(_UpperCAmelCase	)   ,      output_type="""np"""   ,      )

							lowercase__      =	self.to_np(self.dummy_sample	)
							list(_UpperCAmelCase	)

							lowercase__      =	image_processor.postprocess(
							    image_processor.preprocess(_UpperCAmelCase	)   ,      output_type="""np"""   ,      )

							lowercase__      =	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





from typing import TYPE_CHECKING

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


A :	int												=     {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	List[Any]												=     [
	    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RwkvForCausalLM',
	    'RwkvModel',
	    'RwkvPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_rwkv import (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		    RwkvPreTrainedModel,
		)
else:
	import sys

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

