import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      KandinskyVaaImgaImgPipeline
  _SCREAMING_SNAKE_CASE     					=      ["""image_embeds""", """negative_image_embeds""", """image"""]
  _SCREAMING_SNAKE_CASE     					=      [
      """image_embeds""",
      """negative_image_embeds""",
      """image""",
  ]
  _SCREAMING_SNAKE_CASE     					=      [
      """generator""",
      """height""",
      """width""",
      """strength""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  _SCREAMING_SNAKE_CASE     					=      False

  @property
  def 		_UpperCAmelCase (		self			:   str							):
   return 32

  @property
  def 		_UpperCAmelCase (		self			:   int							):
   return 32

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return self.time_input_dim

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   return self.time_input_dim * 4

  @property
  def 		_UpperCAmelCase (		self			:   Dict							):
   return 1_00

  @property
  def 		_UpperCAmelCase (		self			:   List[str]							):
   torch.manual_seed(0							)

   __a							=	{
       "in_channels": 4,
       # Out channels is double in channels because predicts mean and variance
       "out_channels": 8,
       "addition_embed_type": "image",
       "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
       "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
       "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "layers_per_block": 1,
       "encoder_hid_dim": self.text_embedder_hidden_size,
       "encoder_hid_dim_type": "image_proj",
       "cross_attention_dim": self.cross_attention_dim,
       "attention_head_dim": 4,
       "resnet_time_scale_shift": "scale_shift",
       "class_embed_type": None,
   }

   __a							=	UNetaDConditionModel(**__SCREAMING_SNAKE_CASE							)
   return model

  @property
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   return {
       "block_out_channels": [32, 64],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }

  @property
  def 		_UpperCAmelCase (		self			:   Dict							):
   torch.manual_seed(0							)
   __a							=	VQModel(**self.dummy_movq_kwargs							)
   return model

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.dummy_unet
   __a							=	self.dummy_movq

   __a							=	{
       "num_train_timesteps": 10_00,
       "beta_schedule": "linear",
       "beta_start": 0.0_00_85,
       "beta_end": 0.0_12,
       "clip_sample": False,
       "set_alpha_to_one": False,
       "steps_offset": 0,
       "prediction_type": "epsilon",
       "thresholding": False,
   }

   __a							=	DDIMScheduler(**__SCREAMING_SNAKE_CASE							)

   __a							=	{
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   str=0							):
   __a							=	floats_tensor((1, self.text_embedder_hidden_size)  ,				rng=random.Random(__SCREAMING_SNAKE_CASE							)							).to(__SCREAMING_SNAKE_CASE							)
   __a							=	floats_tensor((1, self.text_embedder_hidden_size)  ,				rng=random.Random(seed + 1							)							).to(
       __SCREAMING_SNAKE_CASE							)
   # create init_image
   __a							=	floats_tensor((1, 3, 64, 64)  ,				rng=random.Random(__SCREAMING_SNAKE_CASE							)							).to(__SCREAMING_SNAKE_CASE							)
   __a							=	image.cpu().permute(0  ,				2  ,				3  ,				1							)[0]
   __a							=	Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE							)							).convert("RGB"							).resize((2_56, 2_56)							)

   if str(__SCREAMING_SNAKE_CASE							).startswith("mps"							):
    __a							=	torch.manual_seed(__SCREAMING_SNAKE_CASE							)
   else:
    __a							=	torch.Generator(device=__SCREAMING_SNAKE_CASE							).manual_seed(__SCREAMING_SNAKE_CASE							)
   __a							=	{
       "image": init_image,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 64,
       "width": 64,
       "num_inference_steps": 10,
       "guidance_scale": 7.0,
       "strength": 0.2,
       "output_type": "np",
   }
   return inputs

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	"cpu"

   __a							=	self.get_dummy_components()

   __a							=	self.pipeline_class(**__SCREAMING_SNAKE_CASE							)
   __a							=	pipe.to(__SCREAMING_SNAKE_CASE							)

   pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

   __a							=	pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)							)
   __a							=	output.images

   __a							=	pipe(
       **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)  ,				return_dict=__SCREAMING_SNAKE_CASE  ,				)[0]

   __a							=	image[0, -3:, -3:, -1]
   __a							=	image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)

   __a							=	np.array(
       [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]							)
   assert (
       np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
   ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
   ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   List[str]							):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinskyv22/kandinskyv22_img2img_frog.npy"							)

   __a							=	load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"							)
   __a							=	"A red cartoon frog, 4k"

   __a							=	KandinskyVaaPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-2-prior"  ,				torch_dtype=torch.floataa							)
   pipe_prior.to(__SCREAMING_SNAKE_CASE							)

   __a							=	KandinskyVaaImgaImgPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-2-decoder"  ,				torch_dtype=torch.floataa							)
   __a							=	pipeline.to(__SCREAMING_SNAKE_CASE							)

   pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

   __a							=	torch.Generator(device="cpu"							).manual_seed(0							)
   __a							,  __a							=	pipe_prior(
       __SCREAMING_SNAKE_CASE  ,				generator=__SCREAMING_SNAKE_CASE  ,				num_inference_steps=5  ,				negative_prompt=""  ,				).to_tuple()

   __a							=	pipeline(
       image=__SCREAMING_SNAKE_CASE  ,				image_embeds=__SCREAMING_SNAKE_CASE  ,				negative_image_embeds=__SCREAMING_SNAKE_CASE  ,				generator=__SCREAMING_SNAKE_CASE  ,				num_inference_steps=1_00  ,				height=7_68  ,				width=7_68  ,				strength=0.2  ,				output_type="np"  ,				)

   __a							=	output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)



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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""pixel_values"""]

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float] = 1 / 2_55  ,				__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			:   bool = True  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   __a							=	size if size is not None else {"height": 3_84, "width": 3_84}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	do_resize
   __a							=	size
   __a							=	resample
   __a							=	do_rescale
   __a							=	rescale_factor
   __a							=	do_normalize
   __a							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   __a							=	image_std if image_std is not None else OPENAI_CLIP_STD
   __a							=	do_convert_rgb

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int]  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   if "height" not in size or "width" not in size:
    raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
   __a							=	(size["height"], size["width"])
   return resize(__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   return rescale(__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__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  ,				):
   return normalize(__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   ImageInput  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[str, int]] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = 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			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   ChannelDimension = ChannelDimension.FIRST  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	do_resize if do_resize is not None else self.do_resize
   __a							=	resample if resample is not None else self.resample
   __a							=	do_rescale if do_rescale is not None else self.do_rescale
   __a							=	rescale_factor if rescale_factor is not None else self.rescale_factor
   __a							=	do_normalize if do_normalize is not None else self.do_normalize
   __a							=	image_mean if image_mean is not None else self.image_mean
   __a							=	image_std if image_std is not None else self.image_std
   __a							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __a							=	size if size is not None else self.size
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	make_list_of_images(__SCREAMING_SNAKE_CASE							)

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

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

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError("Image mean and std must be specified if do_normalize is True."							)

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    __a							=	[convert_to_rgb(__SCREAMING_SNAKE_CASE							) for image in images]

   # All transformations expect numpy arrays.
   __a							=	[to_numpy_array(__SCREAMING_SNAKE_CASE							) for image in images]

   if do_resize:
    __a							=	[self.resize(image=__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_rescale:
    __a							=	[self.rescale(image=__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_normalize:
    __a							=	[self.normalize(image=__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	BatchFeature(data={"pixel_values": images}  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

   return encoded_outputs


