


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


A   :					List[Any]	=      logging.get_logger(__name__)

def    lowercase_	(	_A    :					np.ndarray					,      _A    :					Union[int, Iterable[int]]					,      _A    :					bool					,      _A    :					int		):

      """simple docstring"""




      def constraint_to_multiple_of(_A    :					Optional[Any]					,      _A    :					Optional[int]					,      _A    :					Any=0					,      _A    :					int=None		):
            lowerCamelCase__	:       Tuple					=						round(val / multiple		) * multiple

            if max_val is not None and x > max_val:
                  lowerCamelCase__	:       str					=						math.floor(val / multiple		) * multiple

            if x < min_val:
                  lowerCamelCase__	:       Any					=						math.ceil(val / multiple		) * multiple

            return x

      lowerCamelCase__	:       int					=						(output_size, output_size) if isinstance(_A					,      _A		) else output_size

      lowerCamelCase__					,     lowerCamelCase__	:       int					=						get_image_size(_A		)
      lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						output_size

      # determine new height and width
      lowerCamelCase__	:       int					=						output_height / input_height
      lowerCamelCase__	:       int					=						output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width		) < abs(1 - scale_height		):
                  # fit width
                  lowerCamelCase__	:       Tuple					=						scale_width
            else:
                  # fit height
                  lowerCamelCase__	:       Optional[int]					=						scale_height

      lowerCamelCase__	:       Any					=						constraint_to_multiple_of(scale_height * input_height					,      multiple=_A		)
      lowerCamelCase__	:       Tuple					=						constraint_to_multiple_of(scale_width * input_width					,      multiple=_A		)

      return (new_height, new_width)







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    ["pixel_values"]





       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Dict[str, int] = None      ,   __lowerCamelCase							:    PILImageResampling = PILImageResampling.BILINEAR      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    int = 1      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Union[int, float] = 1 / 255      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   **__lowerCamelCase							:    List[str]      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						size if size is not None else {"height": 384, "width": 384}
             lowerCamelCase__	:       Any					=						get_size_dict(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						do_resize
             lowerCamelCase__	:       int					=						size
             lowerCamelCase__	:       str					=						keep_aspect_ratio
             lowerCamelCase__	:       Tuple					=						ensure_multiple_of
             lowerCamelCase__	:       Union[str, Any]					=						resample
             lowerCamelCase__	:       int					=						do_rescale
             lowerCamelCase__	:       int					=						rescale_factor
             lowerCamelCase__	:       List[Any]					=						do_normalize
             lowerCamelCase__	:       str					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             lowerCamelCase__	:       int					=						image_std if image_std is not None else IMAGENET_STANDARD_STD





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Dict[str, int]      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    int = 1      ,   __lowerCamelCase							:    PILImageResampling = PILImageResampling.BICUBIC      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    Dict      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						get_size_dict(__lowerCamelCase )
             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()}" )
             lowerCamelCase__	:       List[Any]					=						get_resize_output_image_size(
                 __lowerCamelCase      ,   output_size=(size["height"], size["width"])      ,   keep_aspect_ratio=__lowerCamelCase      ,   multiple=__lowerCamelCase      ,   )
             return resize(__lowerCamelCase      ,   size=__lowerCamelCase      ,   resample=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Union[int, float]      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    Dict      ,   ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Union[float, List[float]]      ,   __lowerCamelCase							:    Union[float, List[float]]      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    int      ,   ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    ImageInput      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    int = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    int = None      ,   __lowerCamelCase							:    PILImageResampling = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    float = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    Optional[Union[str, TensorType]] = None      ,   __lowerCamelCase							:    ChannelDimension = ChannelDimension.FIRST      ,   **__lowerCamelCase							:    List[Any]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						do_resize if do_resize is not None else self.do_resize
             lowerCamelCase__	:       Tuple					=						size if size is not None else self.size
             lowerCamelCase__	:       Dict					=						get_size_dict(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
             lowerCamelCase__	:       Optional[Any]					=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
             lowerCamelCase__	:       int					=						resample if resample is not None else self.resample
             lowerCamelCase__	:       Union[str, Any]					=						do_rescale if do_rescale is not None else self.do_rescale
             lowerCamelCase__	:       int					=						rescale_factor if rescale_factor is not None else self.rescale_factor
             lowerCamelCase__	:       Dict					=						do_normalize if do_normalize is not None else self.do_normalize
             lowerCamelCase__	:       Optional[Any]					=						image_mean if image_mean is not None else self.image_mean
             lowerCamelCase__	:       Dict					=						image_std if image_std is not None else self.image_std

             lowerCamelCase__	:       List[Any]					=						make_list_of_images(__lowerCamelCase )

             if not valid_images(__lowerCamelCase ):
                   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." )

             # All transformations expect numpy arrays.
             lowerCamelCase__	:       Any					=						[to_numpy_array(__lowerCamelCase ) for image in images]

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

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

             if do_normalize:
                   lowerCamelCase__	:       Any					=						[self.normalize(image=__lowerCamelCase      ,   mean=__lowerCamelCase      ,   std=__lowerCamelCase ) for image in images]

             lowerCamelCase__	:       str					=						[to_channel_dimension_format(__lowerCamelCase      ,   __lowerCamelCase ) for image in images]

             lowerCamelCase__	:       Optional[Any]					=						{"pixel_values": images}
             return BatchFeature(data=__lowerCamelCase      ,   tensor_type=__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Tuple] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						outputs.logits

             # Resize logits and compute semantic segmentation maps
             if target_sizes is not None:
                   if len(__lowerCamelCase ) != len(__lowerCamelCase ):
                         raise ValueError(
                             "Make sure that you pass in as many target sizes as the batch dimension of the logits" )

                   if is_torch_tensor(__lowerCamelCase ):
                         lowerCamelCase__	:       Any					=						target_sizes.numpy()

                   lowerCamelCase__	:       Optional[Any]					=						[]

                   for idx in range(len(__lowerCamelCase ) ):
                         lowerCamelCase__	:       List[str]					=						torch.nn.functional.interpolate(
                             logits[idx].unsqueeze(dim=0 )      ,   size=target_sizes[idx]      ,   mode="bilinear"      ,   align_corners=__lowerCamelCase )
                         lowerCamelCase__	:       Optional[int]					=						resized_logits[0].argmax(dim=0 )
                         semantic_segmentation.append(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Union[str, Any]					=						logits.argmax(dim=1 )
                   lowerCamelCase__	:       Any					=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

             return semantic_segmentation






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     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    KandinskyVaaImgaImgPipeline
       A__      		=    ["image_embeds", "negative_image_embeds", "image"]
       A__      		=    [
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       A__      		=    [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       A__      		=    False





       @property
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return self.time_input_dim





       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return self.time_input_dim * 4





       @property
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             return 100





       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             torch.manual_seed(0 )

             lowerCamelCase__	:       Optional[Any]					=						{
                 "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,
             }

             lowerCamelCase__	:       Tuple					=						UNetaDConditionModel(**__lowerCamelCase )
             return model





       @property
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             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 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       int					=						VQModel(**self.dummy_movq_kwargs )
             return model





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.dummy_unet
             lowerCamelCase__	:       Optional[Any]					=						self.dummy_movq

             lowerCamelCase__	:       Optional[int]					=						{
                 "num_train_timesteps": 1000,
                 "beta_schedule": "linear",
                 "beta_start": 0.0_0_0_8_5,
                 "beta_end": 0.0_1_2,
                 "clip_sample": False,
                 "set_alpha_to_one": False,
                 "steps_offset": 0,
                 "prediction_type": "epsilon",
                 "thresholding": False,
             }

             lowerCamelCase__	:       List[Any]					=						DDIMScheduler(**__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						{
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }

             return components





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    int=0 ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(seed + 1 ) ).to(
                 __lowerCamelCase )
             # create init_image
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						image.cpu().permute(0      ,   2      ,   3      ,   1 )[0]
             lowerCamelCase__	:       Optional[int]					=						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" ).resize((256, 256) )

             if str(__lowerCamelCase ).startswith("mps" ):
                   lowerCamelCase__	:       Optional[int]					=						torch.manual_seed(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       List[str]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						{
                 "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 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						"cpu"

             lowerCamelCase__	:       str					=						self.get_dummy_components()

             lowerCamelCase__	:       Optional[int]					=						self.pipeline_class(**__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						pipe.to(__lowerCamelCase )

             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
             lowerCamelCase__	:       List[str]					=						output.images

             lowerCamelCase__	:       Optional[Any]					=						pipe(
                 **self.get_dummy_inputs(__lowerCamelCase )      ,   return_dict=__lowerCamelCase      ,   )[0]

             lowerCamelCase__	:       int					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       int					=						image_from_tuple[0, -3:, -3:, -1]

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

             lowerCamelCase__	:       str					=						np.array(
                 [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
             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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinskyv22/kandinskyv22_img2img_frog.npy" )

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

             lowerCamelCase__	:       str					=						KandinskyVaaPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-prior"      ,   torch_dtype=torch.floataa )
             pipe_prior.to(__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						KandinskyVaaImgaImgPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-decoder"      ,   torch_dtype=torch.floataa )
             lowerCamelCase__	:       str					=						pipeline.to(__lowerCamelCase )

             pipeline.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						torch.Generator(device="cpu" ).manual_seed(0 )
             lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						pipe_prior(
                 __lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=5      ,   negative_prompt=""      ,   ).to_tuple()

             lowerCamelCase__	:       Optional[Any]					=						pipeline(
                 image=__lowerCamelCase      ,   image_embeds=__lowerCamelCase      ,   negative_image_embeds=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=100      ,   height=768      ,   width=768      ,   strength=0.2      ,   output_type="np"      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(__lowerCamelCase      ,   __lowerCamelCase )


