
"""simple docstring"""






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class      _SCREAMING_SNAKE_CASE   (     UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[str]												=		StableDiffusionControlNetInpaintPipeline
			UpperCAmelCase_    :Any												=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			UpperCAmelCase_    :List[str]												=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCAmelCase_    :Dict												=		frozenset({"control_image"}				)  # skip `image` and `mask` for now, only test for control_image
			UpperCAmelCase_    :Optional[int]												=		TEXT_TO_IMAGE_IMAGE_PARAMS






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						torch.manual_seed(0			)
						lowerCAmelCase_       :Union[str, Any]     	=    UNetaDConditionModel(
						    block_out_channels=(32, 64)			,  layers_per_block=2			,  sample_size=32			,  in_channels=9			,  out_channels=4			,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,  cross_attention_dim=32			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :Any     	=    ControlNetModel(
						    block_out_channels=(32, 64)			,  layers_per_block=2			,  in_channels=4			,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,  cross_attention_dim=32			,  conditioning_embedding_out_channels=(16, 32)			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[Any]     	=    DDIMScheduler(
						    beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  beta_schedule="""scaled_linear"""			,  clip_sample=snake_case_			,  set_alpha_to_one=snake_case_			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[Any]     	=    AutoencoderKL(
						    block_out_channels=[32, 64]			,  in_channels=3			,  out_channels=3			,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,  latent_channels=4			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :Any     	=    CLIPTextConfig(
						    bos_token_id=0			,  eos_token_id=2			,  hidden_size=32			,  intermediate_size=37			,  layer_norm_eps=1E-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )
						lowerCAmelCase_       :Optional[Any]     	=    CLIPTextModel(snake_case_			)
						lowerCAmelCase_       :Optional[int]     	=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

						lowerCAmelCase_       :Dict     	=    {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=0			)     ->	List[str]:

						if str(snake_case_			).startswith("""mps"""			):
									lowerCAmelCase_       :Optional[Any]     	=    torch.manual_seed(snake_case_			)
						else:
									lowerCAmelCase_       :str     	=    torch.Generator(device=snake_case_			).manual_seed(snake_case_			)

						lowerCAmelCase_       :Dict     	=    2
						lowerCAmelCase_       :int     	=    randn_tensor(
						    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,  generator=snake_case_			,  device=torch.device(snake_case_			)			,  )
						lowerCAmelCase_       :str     	=    floats_tensor((1, 3, 32, 32)			,  rng=random.Random(snake_case_			)			).to(snake_case_			)
						lowerCAmelCase_       :Any     	=    init_image.cpu().permute(0			,  2			,  3			,  1			)[0]

						lowerCAmelCase_       :Tuple     	=    Image.fromarray(np.uinta(snake_case_			)			).convert("""RGB"""			).resize((64, 64)			)
						lowerCAmelCase_       :Union[str, Any]     	=    Image.fromarray(np.uinta(init_image + 4			)			).convert("""RGB"""			).resize((64, 64)			)

						lowerCAmelCase_       :int     	=    {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'image': image,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)






			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()			,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,  )
			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3			)




class      _SCREAMING_SNAKE_CASE   (     UpperCAmelCase__				):
			UpperCAmelCase_    :Any												=		StableDiffusionControlNetInpaintPipeline
			UpperCAmelCase_    :Optional[Any]												=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			UpperCAmelCase_    :Tuple												=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCAmelCase_    :List[Any]												=		frozenset([]				)






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						torch.manual_seed(0			)
						lowerCAmelCase_       :str     	=    UNetaDConditionModel(
						    block_out_channels=(32, 64)			,  layers_per_block=2			,  sample_size=32			,  in_channels=4			,  out_channels=4			,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,  cross_attention_dim=32			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :Tuple     	=    ControlNetModel(
						    block_out_channels=(32, 64)			,  layers_per_block=2			,  in_channels=4			,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,  cross_attention_dim=32			,  conditioning_embedding_out_channels=(16, 32)			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :List[str]     	=    DDIMScheduler(
						    beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  beta_schedule="""scaled_linear"""			,  clip_sample=snake_case_			,  set_alpha_to_one=snake_case_			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[Any]     	=    AutoencoderKL(
						    block_out_channels=[32, 64]			,  in_channels=3			,  out_channels=3			,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,  latent_channels=4			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :Tuple     	=    CLIPTextConfig(
						    bos_token_id=0			,  eos_token_id=2			,  hidden_size=32			,  intermediate_size=37			,  layer_norm_eps=1E-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )
						lowerCAmelCase_       :Any     	=    CLIPTextModel(snake_case_			)
						lowerCAmelCase_       :List[str]     	=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

						lowerCAmelCase_       :Tuple     	=    {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components




class      _SCREAMING_SNAKE_CASE   (     UpperCAmelCase__      ,					UpperCAmelCase__      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[str]												=		StableDiffusionControlNetInpaintPipeline
			UpperCAmelCase_    :str												=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			UpperCAmelCase_    :Optional[int]												=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						torch.manual_seed(0			)
						lowerCAmelCase_       :Union[str, Any]     	=    UNetaDConditionModel(
						    block_out_channels=(32, 64)			,  layers_per_block=2			,  sample_size=32			,  in_channels=9			,  out_channels=4			,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,  cross_attention_dim=32			,  )
						torch.manual_seed(0			)

						def init_weights(__A			):
									if isinstance(snake_case_			,  torch.nn.Convad			):
												torch.nn.init.normal(m.weight			)
												m.bias.data.fill_(1.0			)

						lowerCAmelCase_       :Any     	=    ControlNetModel(
						    block_out_channels=(32, 64)			,  layers_per_block=2			,  in_channels=4			,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,  cross_attention_dim=32			,  conditioning_embedding_out_channels=(16, 32)			,  )
						controlneta.controlnet_down_blocks.apply(snake_case_			)

						torch.manual_seed(0			)
						lowerCAmelCase_       :Union[str, Any]     	=    ControlNetModel(
						    block_out_channels=(32, 64)			,  layers_per_block=2			,  in_channels=4			,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,  cross_attention_dim=32			,  conditioning_embedding_out_channels=(16, 32)			,  )
						controlneta.controlnet_down_blocks.apply(snake_case_			)

						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[Any]     	=    DDIMScheduler(
						    beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  beta_schedule="""scaled_linear"""			,  clip_sample=snake_case_			,  set_alpha_to_one=snake_case_			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[int]     	=    AutoencoderKL(
						    block_out_channels=[32, 64]			,  in_channels=3			,  out_channels=3			,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,  latent_channels=4			,  )
						torch.manual_seed(0			)
						lowerCAmelCase_       :int     	=    CLIPTextConfig(
						    bos_token_id=0			,  eos_token_id=2			,  hidden_size=32			,  intermediate_size=37			,  layer_norm_eps=1E-05			,  num_attention_heads=4			,  num_hidden_layers=5			,  pad_token_id=1			,  vocab_size=1000			,  )
						lowerCAmelCase_       :Tuple     	=    CLIPTextModel(snake_case_			)
						lowerCAmelCase_       :Optional[Any]     	=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

						lowerCAmelCase_       :Dict     	=    MultiControlNetModel([controlneta, controlneta]			)

						lowerCAmelCase_       :str     	=    {
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=0			)     ->	str:

						if str(snake_case_			).startswith("""mps"""			):
									lowerCAmelCase_       :Any     	=    torch.manual_seed(snake_case_			)
						else:
									lowerCAmelCase_       :List[str]     	=    torch.Generator(device=snake_case_			).manual_seed(snake_case_			)

						lowerCAmelCase_       :List[str]     	=    2

						lowerCAmelCase_       :Union[str, Any]     	=    [
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,  generator=snake_case_			,  device=torch.device(snake_case_			)			,  ),
						    randn_tensor(
						        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,  generator=snake_case_			,  device=torch.device(snake_case_			)			,  ),
						]
						lowerCAmelCase_       :Tuple     	=    floats_tensor((1, 3, 32, 32)			,  rng=random.Random(snake_case_			)			).to(snake_case_			)
						lowerCAmelCase_       :List[str]     	=    init_image.cpu().permute(0			,  2			,  3			,  1			)[0]

						lowerCAmelCase_       :Tuple     	=    Image.fromarray(np.uinta(snake_case_			)			).convert("""RGB"""			).resize((64, 64)			)
						lowerCAmelCase_       :str     	=    Image.fromarray(np.uinta(init_image + 4			)			).convert("""RGB"""			).resize((64, 64)			)

						lowerCAmelCase_       :Any     	=    {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'image': image,
						    'mask_image': mask_image,
						    'control_image': control_image,
						}

						return inputs






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						lowerCAmelCase_       :str     	=    self.get_dummy_components()
						lowerCAmelCase_       :Union[str, Any]     	=    self.pipeline_class(**snake_case_			)
						pipe.to(snake_case_			)

						lowerCAmelCase_       :Optional[int]     	=    1_0.0
						lowerCAmelCase_       :Tuple     	=    4

						lowerCAmelCase_       :List[Any]     	=    self.get_dummy_inputs(snake_case_			)
						lowerCAmelCase_       :Optional[int]     	=    steps
						lowerCAmelCase_       :int     	=    scale
						lowerCAmelCase_       :Dict     	=    pipe(**snake_case_			)[0]

						lowerCAmelCase_       :Tuple     	=    self.get_dummy_inputs(snake_case_			)
						lowerCAmelCase_       :Any     	=    steps
						lowerCAmelCase_       :Any     	=    scale
						lowerCAmelCase_       :List[str]     	=    pipe(**snake_case_			,  control_guidance_start=0.1			,  control_guidance_end=0.2			)[0]

						lowerCAmelCase_       :str     	=    self.get_dummy_inputs(snake_case_			)
						lowerCAmelCase_       :Dict     	=    steps
						lowerCAmelCase_       :List[str]     	=    scale
						lowerCAmelCase_       :Tuple     	=    pipe(**snake_case_			,  control_guidance_start=[0.1, 0.3]			,  control_guidance_end=[0.2, 0.7]			)[0]

						lowerCAmelCase_       :Union[str, Any]     	=    self.get_dummy_inputs(snake_case_			)
						lowerCAmelCase_       :int     	=    steps
						lowerCAmelCase_       :Optional[Any]     	=    scale
						lowerCAmelCase_       :Dict     	=    pipe(**snake_case_			,  control_guidance_start=0.4			,  control_guidance_end=[0.5, 0.8]			)[0]

						# make sure that all outputs are different
						assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
						assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
						assert np.sum(np.abs(output_a - output_a			)			) > 1E-3






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)






			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()			,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,  )
			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						self._test_inference_batch_single_identical(expected_max_diff=2E-3			)






			def 					__lowerCAmelCase					(   self			)     ->	int:
						lowerCAmelCase_       :List[str]     	=    self.get_dummy_components()
						lowerCAmelCase_       :Optional[Any]     	=    self.pipeline_class(**snake_case_			)
						pipe.to(snake_case_			)
						pipe.set_progress_bar_config(disable=snake_case_			)
						with tempfile.TemporaryDirectory() as tmpdir:
									try:
												# save_pretrained is not implemented for Multi-ControlNet
												pipe.save_pretrained(snake_case_			)
									except NotImplementedError:
												pass




@slow
@require_torch_gpu
class      _SCREAMING_SNAKE_CASE   (     unittest.TestCase				):






			def 					__lowerCAmelCase					(   self			)     ->	int:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :str     	=    ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""			)

						lowerCAmelCase_       :Tuple     	=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    """runwayml/stable-diffusion-inpainting"""			,  safety_checker=snake_case_			,  controlnet=snake_case_			)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=snake_case_			)

						lowerCAmelCase_       :List[str]     	=    torch.Generator(device="""cpu"""			).manual_seed(0			)
						lowerCAmelCase_       :Union[str, Any]     	=    load_image(
						    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""			).resize((512, 512)			)

						lowerCAmelCase_       :Optional[Any]     	=    load_image(
						    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
						    """/stable_diffusion_inpaint/input_bench_mask.png"""			).resize((512, 512)			)

						lowerCAmelCase_       :Optional[int]     	=    'pitch black hole'

						lowerCAmelCase_       :str     	=    load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""			).resize((512, 512)			)

						lowerCAmelCase_       :Any     	=    pipe(
						    snake_case_			,  image=snake_case_			,  mask_image=snake_case_			,  control_image=snake_case_			,  generator=snake_case_			,  output_type="""np"""			,  num_inference_steps=3			,  )

						lowerCAmelCase_       :int     	=    output.images[0]

						assert image.shape == (512, 512, 3)

						lowerCAmelCase_       :Optional[int]     	=    load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""			)

						assert np.abs(expected_image - image			).max() < 9E-2






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :Optional[int]     	=    ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""			)

						lowerCAmelCase_       :List[Any]     	=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    """runwayml/stable-diffusion-v1-5"""			,  safety_checker=snake_case_			,  controlnet=snake_case_			)
						lowerCAmelCase_       :Dict     	=    DDIMScheduler.from_config(pipe.scheduler.config			)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=snake_case_			)

						lowerCAmelCase_       :List[str]     	=    torch.Generator(device="""cpu"""			).manual_seed(33			)

						lowerCAmelCase_       :Tuple     	=    load_image(
						    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""			)
						lowerCAmelCase_       :Any     	=    init_image.resize((512, 512)			)

						lowerCAmelCase_       :List[Any]     	=    load_image(
						    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""			)
						lowerCAmelCase_       :Dict     	=    mask_image.resize((512, 512)			)

						lowerCAmelCase_       :str     	=    'a handsome man with ray-ban sunglasses'

						def make_inpaint_condition(__A			,  __A			):
									lowerCAmelCase_       :Optional[int]     	=    np.array(image.convert("""RGB"""			)			).astype(np.floataa			) / 255.0
									lowerCAmelCase_       :List[Any]     	=    np.array(image_mask.convert("""L"""			)			).astype(np.floataa			) / 255.0

									assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
									lowerCAmelCase_       :Optional[int]     	=    -1.0  # set as masked pixel
									lowerCAmelCase_       :Union[str, Any]     	=    np.expand_dims(snake_case_			,  0			).transpose(0			,  3			,  1			,  2			)
									lowerCAmelCase_       :int     	=    torch.from_numpy(snake_case_			)
									return image

						lowerCAmelCase_       :Optional[int]     	=    make_inpaint_condition(snake_case_			,  snake_case_			)

						lowerCAmelCase_       :List[str]     	=    pipe(
						    snake_case_			,  image=snake_case_			,  mask_image=snake_case_			,  control_image=snake_case_			,  guidance_scale=9.0			,  eta=1.0			,  generator=snake_case_			,  num_inference_steps=20			,  output_type="""np"""			,  )
						lowerCAmelCase_       :str     	=    output.images[0]

						assert image.shape == (512, 512, 3)

						lowerCAmelCase_       :Dict     	=    load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""			)

						assert np.abs(expected_image - image			).max() < 9E-2













"""simple docstring"""




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Union[str, Any]             =  CONFIG_NAME

      @register_to_config
      def __init__(	self   , snake_case_ = True   , snake_case_ = 8   , snake_case_ = "lanczos"   , snake_case_ = True   , snake_case_ = False   , ):

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			int									=    images[None, ...]
        A_    :			Union[str, Any]									=    (images * 2_5_5).round().astype('uint8'    )
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          A_    :			List[str]									=    [Image.fromarray(image.squeeze()   , mode='L'    ) for image in images]
        else:
          A_    :			Tuple									=    [Image.fromarray(snake_case_    ) for image in images]

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if not isinstance(snake_case_   , snake_case_    ):
          A_    :			int									=    [images]
        A_    :			int									=    [np.array(snake_case_    ).astype(np.floataa    ) / 2_55.0 for image in images]
        A_    :			Tuple									=    np.stack(snake_case_   , axis=0    )

        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			Any									=    images[..., None]

        A_    :			Dict									=    torch.from_numpy(images.transpose(0   , 3   , 1   , 2    )    )
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    images.cpu().permute(0   , 2   , 3   , 1    ).float().numpy()
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return 2.0 * images - 1.0

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return (images / 2 + 0.5).clamp(0   , 1    )

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        A_    :			List[Any]									=    image.convert('RGB'    )
        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

        if height is None:
          A_    :			int									=    image.height
        if width is None:
          A_    :			List[Any]									=    image.width

        A_		,			A_    :			Dict									=    (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        A_    :			Any									=    image.resize((width, height)   , resample=PIL_INTERPOLATION[self.config.resample]    )
        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			Dict									=    (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(snake_case_   , snake_case_    ):
          A_    :			Dict									=    [image]
        elif not (isinstance(snake_case_   , snake_case_    ) and all(isinstance(snake_case_   , snake_case_    ) for i in image    )):
          raise ValueError(
              F"""Input is in incorrect format: {[type(snake_case_    ) for i in image]}. Currently, we only support {", ".join(snake_case_    )}"""    )

        if isinstance(image[0]   , PIL.Image.Image    ):
          if self.config.do_convert_rgb:
            A_    :			Dict									=    [self.convert_to_rgb(snake_case_    ) for i in image]
          if self.config.do_resize:
            A_    :			Any									=    [self.resize(snake_case_   , snake_case_   , snake_case_    ) for i in image]
          A_    :			Optional[int]									=    self.pil_to_numpy(snake_case_    )  # to np
          A_    :			Dict									=    self.numpy_to_pt(snake_case_    )  # to pt

        elif isinstance(image[0]   , np.ndarray    ):
          A_    :			Optional[Any]									=    np.concatenate(snake_case_   , axis=0    ) if image[0].ndim == 4 else np.stack(snake_case_   , axis=0    )
          A_    :			Optional[Any]									=    self.numpy_to_pt(snake_case_    )
          A_		,			A_		,			A_		,			A_    :			Tuple									=    image.shape
          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""    )

        elif isinstance(image[0]   , torch.Tensor    ):
          A_    :			int									=    torch.cat(snake_case_   , axis=0    ) if image[0].ndim == 4 else torch.stack(snake_case_   , axis=0    )
          A_		,			A_		,			A_		,			A_    :			str									=    image.shape

          # don't need any preprocess if the image is latents
          if channel == 4:
            return image

          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""    )

        # expected range [0,1], normalize to [-1,1]
        A_    :			Optional[Any]									=    self.config.do_normalize
        if image.min() < 0:
          warnings.warn(
              'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
              F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""   , snake_case_   , )
          A_    :			Optional[int]									=    False

        if do_normalize:
          A_    :			Any									=    self.normalize(snake_case_    )

        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , snake_case_ = None   , ):

        """simple docstring"""

        if not isinstance(snake_case_   , torch.Tensor    ):
          raise ValueError(
              F"""Input for postprocessing is in incorrect format: {type(snake_case_    )}. We only support pytorch tensor"""    )
        if output_type not in ["latent", "pt", "np", "pil"]:
          A_    :			Optional[Any]									=    (
              F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'   , '1.0.0'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Dict									=    'np'

        if output_type == "latent":
          return image

        if do_denormalize is None:
          A_    :			str									=    [self.config.do_normalize] * image.shape[0]

        A_    :			List[Any]									=    torch.stack(
            [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

        if output_type == "pt":
          return image

        A_    :			Optional[Any]									=    self.pt_to_numpy(snake_case_    )

        if output_type == "np":
          return image

        if output_type == "pil":
          return self.numpy_to_pil(snake_case_    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					str             =  CONFIG_NAME

      @register_to_config
      def __init__(	self   , snake_case_ = True   , snake_case_ = 8   , snake_case_ = "lanczos"   , snake_case_ = True   , ):

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			int									=    images[None, ...]
        A_    :			Optional[int]									=    (images * 2_5_5).round().astype('uint8'    )
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          A_    :			Optional[int]									=    [Image.fromarray(image.squeeze()   , mode='L'    ) for image in images]
        else:
          A_    :			Optional[Any]									=    [Image.fromarray(image[:, :, :3]    ) for image in images]

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return image[:, :, 1] * 2**8 + image[:, :, 2]

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			Union[str, Any]									=    images[None, ...]
        A_    :			Tuple									=    images[:, :, :, 3:]
        if images.shape[-1] == 6:
          A_    :			Dict									=    (images_depth * 2_5_5).round().astype('uint8'    )
          A_    :			List[Any]									=    [
              Image.fromarray(self.rgblike_to_depthmap(snake_case_    )   , mode='I;16'    ) for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          A_    :			Optional[int]									=    (images_depth * 6_55_35.0).astype(np.uintaa    )
          A_    :			Union[str, Any]									=    [Image.fromarray(snake_case_   , mode='I;16'    ) for image_depth in images_depth]
        else:
          raise Exception('Not supported'    )

        return pil_images

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , snake_case_ = None   , ):

        """simple docstring"""

        if not isinstance(snake_case_   , torch.Tensor    ):
          raise ValueError(
              F"""Input for postprocessing is in incorrect format: {type(snake_case_    )}. We only support pytorch tensor"""    )
        if output_type not in ["latent", "pt", "np", "pil"]:
          A_    :			Optional[int]									=    (
              F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'   , '1.0.0'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Any									=    'np'

        if do_denormalize is None:
          A_    :			int									=    [self.config.do_normalize] * image.shape[0]

        A_    :			List[str]									=    torch.stack(
            [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

        A_    :			Optional[Any]									=    self.pt_to_numpy(snake_case_    )

        if output_type == "np":
          if image.shape[-1] == 6:
            A_    :			Dict									=    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]    ) for im in image]   , axis=0    )
          else:
            A_    :			str									=    image[:, :, :, 3:]
          return image[:, :, :, :3], image_depth

        if output_type == "pil":
          return self.numpy_to_pil(snake_case_    ), self.numpy_to_depth(snake_case_    )
        else:
          raise Exception(F"""This type {output_type} is not supported"""    )