



"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case							:     Optional[int]   =					logging.get_logger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			['pixel_values']

	def __init__(  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	Optional[Dict[str, int]] = None ,    _SCREAMING_SNAKE_CASE:	PILImageResampling = PILImageResampling.BICUBIC ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	Union[int, float] = 1 / 255 ,    _SCREAMING_SNAKE_CASE:	Dict[str, int] = None ,    _SCREAMING_SNAKE_CASE:	bool = True ,    _SCREAMING_SNAKE_CASE:	Optional[Union[float, List[float]]] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Union[float, List[float]]] = None ,    **_SCREAMING_SNAKE_CASE:	Tuple ,    )       -> None:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Tuple				= size if size is not None else {"height": 224, "width": 224}
				__lowerCAmelCase   :						int				= get_size_dict(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Any				= crop_size if crop_size is not None else {"height": 224, "width": 224}
				__lowerCAmelCase   :						Tuple				= get_size_dict(_SCREAMING_SNAKE_CASE ,    default_to_square=_SCREAMING_SNAKE_CASE ,    param_name="crop_size")

				__lowerCAmelCase   :						Union[str, Any]				= do_resize
				__lowerCAmelCase   :						List[Any]				= do_rescale
				__lowerCAmelCase   :						Any				= do_normalize
				__lowerCAmelCase   :						Optional[Any]				= do_center_crop
				__lowerCAmelCase   :						str				= crop_size
				__lowerCAmelCase   :						List[str]				= size
				__lowerCAmelCase   :						List[Any]				= resample
				__lowerCAmelCase   :						Tuple				= rescale_factor
				__lowerCAmelCase   :						List[str]				= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				__lowerCAmelCase   :						Optional[Any]				= image_std if image_std is not None else IMAGENET_DEFAULT_STD

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	np.ndarray ,    _SCREAMING_SNAKE_CASE:	Dict[str, int] ,    _SCREAMING_SNAKE_CASE:	PILImageResampling = PILImageResampling.BILINEAR ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, ChannelDimension]] = None ,    **_SCREAMING_SNAKE_CASE:	List[str] ,    )       -> np.ndarray:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= get_size_dict(_SCREAMING_SNAKE_CASE)

				if "shortest_edge" in size:
							__lowerCAmelCase   :						List[Any]				= get_resize_output_image_size(_SCREAMING_SNAKE_CASE ,    size=size["shortest_edge"] ,    default_to_square=_SCREAMING_SNAKE_CASE)
							# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
				elif "height" in size and "width" in size:
							__lowerCAmelCase   :						Optional[int]				= (size["height"], size["width"])
				else:
							raise ValueError(F"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""")
				return resize(_SCREAMING_SNAKE_CASE ,    size=_SCREAMING_SNAKE_CASE ,    resample=_SCREAMING_SNAKE_CASE ,    data_format=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	np.ndarray ,    _SCREAMING_SNAKE_CASE:	Dict[str, int] ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, ChannelDimension]] = None ,    **_SCREAMING_SNAKE_CASE:	List[str] ,    )       -> np.ndarray:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= get_size_dict(_SCREAMING_SNAKE_CASE)
				if "height" not in size or "width" not in size:
							raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
				return center_crop(_SCREAMING_SNAKE_CASE ,    size=(size["height"], size["width"]) ,    data_format=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	np.ndarray ,    _SCREAMING_SNAKE_CASE:	float ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, ChannelDimension]] = None ,    **_SCREAMING_SNAKE_CASE:	int)       -> np.ndarray:

				"""simple docstring"""

				return rescale(_SCREAMING_SNAKE_CASE ,    scale=_SCREAMING_SNAKE_CASE ,    data_format=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	np.ndarray ,    _SCREAMING_SNAKE_CASE:	Union[float, List[float]] ,    _SCREAMING_SNAKE_CASE:	Union[float, List[float]] ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, ChannelDimension]] = None ,    **_SCREAMING_SNAKE_CASE:	int ,    )       -> np.ndarray:

				"""simple docstring"""

				return normalize(_SCREAMING_SNAKE_CASE ,    mean=_SCREAMING_SNAKE_CASE ,    std=_SCREAMING_SNAKE_CASE ,    data_format=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	ImageInput ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    _SCREAMING_SNAKE_CASE:	Dict[str, int] = None ,    _SCREAMING_SNAKE_CASE:	PILImageResampling = None ,    _SCREAMING_SNAKE_CASE:	bool = None ,    _SCREAMING_SNAKE_CASE:	int = None ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    _SCREAMING_SNAKE_CASE:	Optional[float] = None ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Union[float, List[float]]] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Union[float, List[float]]] = None ,    _SCREAMING_SNAKE_CASE:	Optional[Union[str, TensorType]] = None ,    _SCREAMING_SNAKE_CASE:	Union[str, ChannelDimension] = ChannelDimension.FIRST ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> BatchFeature:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= do_resize if do_resize is not None else self.do_resize
				__lowerCAmelCase   :						Dict				= do_rescale if do_rescale is not None else self.do_rescale
				__lowerCAmelCase   :						int				= do_normalize if do_normalize is not None else self.do_normalize
				__lowerCAmelCase   :						List[Any]				= do_center_crop if do_center_crop is not None else self.do_center_crop
				__lowerCAmelCase   :						Union[str, Any]				= crop_size if crop_size is not None else self.crop_size
				__lowerCAmelCase   :						Dict				= get_size_dict(_SCREAMING_SNAKE_CASE ,    param_name="crop_size" ,    default_to_square=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= resample if resample is not None else self.resample
				__lowerCAmelCase   :						Union[str, Any]				= rescale_factor if rescale_factor is not None else self.rescale_factor
				__lowerCAmelCase   :						Any				= image_mean if image_mean is not None else self.image_mean
				__lowerCAmelCase   :						Union[str, Any]				= image_std if image_std is not None else self.image_std

				__lowerCAmelCase   :						Optional[int]				= size if size is not None else self.size
				__lowerCAmelCase   :						Any				= get_size_dict(_SCREAMING_SNAKE_CASE)

				if not is_batched(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Dict				= [images]

				if not valid_images(_SCREAMING_SNAKE_CASE):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray.")

				if do_resize and size is None:
							raise ValueError("Size must be specified if do_resize is True.")

				if do_center_crop and crop_size is None:
							raise ValueError("Crop size must be specified if do_center_crop is True.")

				if do_rescale and rescale_factor is None:
							raise ValueError("Rescale factor must be specified if do_rescale is True.")

				# All transformations expect numpy arrays.
				__lowerCAmelCase   :						Tuple				= [to_numpy_array(_SCREAMING_SNAKE_CASE) for image in images]

				if do_resize:
							__lowerCAmelCase   :						Tuple				= [self.resize(image=_SCREAMING_SNAKE_CASE ,    size=_SCREAMING_SNAKE_CASE ,    resample=_SCREAMING_SNAKE_CASE) for image in images]

				if do_center_crop:
							__lowerCAmelCase   :						int				= [self.center_crop(image=_SCREAMING_SNAKE_CASE ,    size=_SCREAMING_SNAKE_CASE) for image in images]

				if do_rescale:
							__lowerCAmelCase   :						Dict				= [self.rescale(image=_SCREAMING_SNAKE_CASE ,    scale=_SCREAMING_SNAKE_CASE) for image in images]

				if do_normalize:
							__lowerCAmelCase   :						str				= [self.normalize(image=_SCREAMING_SNAKE_CASE ,    mean=_SCREAMING_SNAKE_CASE ,    std=_SCREAMING_SNAKE_CASE) for image in images]

				__lowerCAmelCase   :						List[str]				= [to_channel_dimension_format(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) for image in images]

				__lowerCAmelCase   :						int				= {"pixel_values": images}
				return BatchFeature(data=_SCREAMING_SNAKE_CASE ,    tensor_type=_SCREAMING_SNAKE_CASE)




"""simple docstring"""






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch



@require_torch
class     A__     :




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				return self.get_dummy_input()

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

				"""simple docstring"""

				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""")

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=False ,    _SCREAMING_SNAKE_CASE:	Dict=False ,    )       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= 4
				__lowerCAmelCase   :						List[str]				= 32
				__lowerCAmelCase   :						str				= (32, 32)

				__lowerCAmelCase   :						int				= torch.manual_seed(0)
				__lowerCAmelCase   :						Dict				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= (batch_size, num_channels) + sizes
				__lowerCAmelCase   :						Dict				= randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= {"hidden_states": hidden_states}

				if include_temb:
							__lowerCAmelCase   :						List[str]				= 128
							__lowerCAmelCase   :						Dict				= randn_tensor((batch_size, temb_channels) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				if include_res_hidden_states_tuple:
							__lowerCAmelCase   :						Dict				= torch.manual_seed(1)
							__lowerCAmelCase   :						int				= (randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE),)

				if include_encoder_hidden_states:
							__lowerCAmelCase   :						int				= floats_tensor((batch_size, 32, 32)).to(_SCREAMING_SNAKE_CASE)

				if include_skip_sample:
							__lowerCAmelCase   :						Optional[Any]				= randn_tensor(((batch_size, 3) + sizes) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				return dummy_input

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= {
				    "in_channels": 32,
				    "out_channels": 32,
				    "temb_channels": 128,
				}
				if self.block_type == "up":
							__lowerCAmelCase   :						Tuple				= 32

				if self.block_type == "mid":
							init_dict.pop("out_channels")

				__lowerCAmelCase   :						str				= self.dummy_input
				return init_dict, inputs_dict

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						Dict				= self.block_class(**_SCREAMING_SNAKE_CASE)
				unet_block.to(_SCREAMING_SNAKE_CASE)
				unet_block.eval()

				with torch.no_grad():
							__lowerCAmelCase   :						int				= unet_block(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						List[Any]				= output[0]

				self.assertEqual(output.shape ,    self.output_shape)

				__lowerCAmelCase   :						str				= output[0, -1, -3:, -3:]
				__lowerCAmelCase   :						List[Any]				= torch.tensor(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
				assert torch_all_close(output_slice.flatten() ,    _SCREAMING_SNAKE_CASE ,    atol=5e-3)







	@unittest.skipIf(torch_device == "mps" ,    "Training is not supported in mps")
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						str				= self.block_class(**_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.train()
				__lowerCAmelCase   :						Union[str, Any]				= model(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Union[str, Any]				= output[0]

				__lowerCAmelCase   :						List[Any]				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= randn_tensor(output.shape ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				loss.backward()