





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Union[str, Any]         =   KandinskyVaaControlnetImgaImgPipeline
       _UpperCamelCase				:					List[str]         =   ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       _UpperCamelCase				:					Tuple         =   ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       _UpperCamelCase				:					Any         =   [
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       _UpperCamelCase				:					int         =   False

       @property
       def lowercase (		self: Union[str, Any]		)       -> int:




              """simple docstring"""


              return 32

       @property
       def lowercase (		self: Optional[int]		)       -> Tuple:




              """simple docstring"""


              return 32

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




              """simple docstring"""


              return self.time_input_dim

       @property
       def lowercase (		self: Tuple		)       -> Any:




              """simple docstring"""


              return self.time_input_dim * 4

       @property
       def lowercase (		self: Optional[Any]		)       -> Dict:




              """simple docstring"""


              return 100

       @property
       def lowercase (		self: Tuple		)       -> Union[str, Any]:




              """simple docstring"""


              torch.manual_seed(0		)

              UpperCamelCase_          =  {
                  "in_channels": 8,
                  # Out channels is double in channels because predicts mean and variance
                  "out_channels": 8,
                  "addition_embed_type": "image_hint",
                  "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,
              }

              UpperCamelCase_          =  UNetaDConditionModel(**_SCREAMING_SNAKE_CASE		)
              return model

       @property
       def lowercase (		self: int		)       -> str:




              """simple docstring"""


              return {
                  "block_out_channels": [32, 32, 64, 64],
                  "down_block_types": [
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "vq_embed_dim": 4,
              }

       @property
       def lowercase (		self: Tuple		)       -> List[str]:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  VQModel(**self.dummy_movq_kwargs		)
              return model

       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  self.dummy_unet
              UpperCamelCase_          =  self.dummy_movq

              UpperCamelCase_          =  {
                  "num_train_timesteps": 1000,
                  "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,
              }

              UpperCamelCase_          =  DDIMScheduler(**_SCREAMING_SNAKE_CASE		)

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

              return components

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(seed + 1		)		).to(
                  _SCREAMING_SNAKE_CASE		)
              # create init_image
              UpperCamelCase_          =  floats_tensor((1, 3, 64, 64)					,   rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  image.cpu().permute(0					,   2					,   3					,   1		)[0]
              UpperCamelCase_          =  Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE		)		).convert("RGB"		).resize((256, 256)		)
              # create hint
              UpperCamelCase_          =  floats_tensor((1, 3, 64, 64)					,   rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)

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



       def lowercase (		self: Optional[int]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  "cpu"

              UpperCamelCase_          =  self.get_dummy_components()

              UpperCamelCase_          =  self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  pipe.to(_SCREAMING_SNAKE_CASE		)

              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_          =  output.images

              UpperCamelCase_          =  pipe(
                  **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)					,   return_dict=_SCREAMING_SNAKE_CASE					,   )[0]

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

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

              UpperCamelCase_          =  np.array(
                  [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]		)
              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 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Dict		)       -> Dict:




              """simple docstring"""


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



       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"		)

              UpperCamelCase_          =  load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
              UpperCamelCase_          =  init_image.resize((512, 512)		)

              UpperCamelCase_          =  load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/kandinskyv22/hint_image_cat.png"		)
              UpperCamelCase_          =  torch.from_numpy(np.array(_SCREAMING_SNAKE_CASE		)		).float() / 2_55.0
              UpperCamelCase_          =  hint.permute(2					,   0					,   1		).unsqueeze(0		)

              UpperCamelCase_          =  "A robot, 4k photo"

              UpperCamelCase_          =  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
                  "kandinsky-community/kandinsky-2-2-prior"					,   torch_dtype=torch.floataa		)
              pipe_prior.to(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
                  "kandinsky-community/kandinsky-2-2-controlnet-depth"					,   torch_dtype=torch.floataa		)
              UpperCamelCase_          =  pipeline.to(_SCREAMING_SNAKE_CASE		)

              pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  torch.Generator(device="cpu"		).manual_seed(0		)

              UpperCamelCase_       , UpperCamelCase_          =  pipe_prior(
                  _SCREAMING_SNAKE_CASE					,   image=_SCREAMING_SNAKE_CASE					,   strength=0.85					,   generator=_SCREAMING_SNAKE_CASE					,   negative_prompt=""					,   ).to_tuple()

              UpperCamelCase_          =  pipeline(
                  image=_SCREAMING_SNAKE_CASE					,   image_embeds=_SCREAMING_SNAKE_CASE					,   negative_image_embeds=_SCREAMING_SNAKE_CASE					,   hint=_SCREAMING_SNAKE_CASE					,   generator=_SCREAMING_SNAKE_CASE					,   num_inference_steps=100					,   height=512					,   width=512					,   strength=0.5					,   output_type="np"					,   )

              UpperCamelCase_          =  output.images[0]

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

              assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: pyspark.sql.DataFrame					,   _SCREAMING_SNAKE_CASE: Optional[NamedSplit] = None					,   _SCREAMING_SNAKE_CASE: Optional[Features] = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: str = None					,   _SCREAMING_SNAKE_CASE: bool = False					,   _SCREAMING_SNAKE_CASE: str = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: str = "arrow"					,   **_SCREAMING_SNAKE_CASE: Union[str, Any]					,   )       -> Any:




              """simple docstring"""


              super().__init__(
                  split=_SCREAMING_SNAKE_CASE					,   features=_SCREAMING_SNAKE_CASE					,   cache_dir=_SCREAMING_SNAKE_CASE					,   keep_in_memory=_SCREAMING_SNAKE_CASE					,   streaming=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  load_from_cache_file
              UpperCamelCase_          =  file_format
              UpperCamelCase_          =  Spark(
                  df=_SCREAMING_SNAKE_CASE					,   features=_SCREAMING_SNAKE_CASE					,   cache_dir=_SCREAMING_SNAKE_CASE					,   working_dir=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )



       def lowercase (		self: str		)       -> Optional[Any]:




              """simple docstring"""


              if self.streaming:
                     return self.builder.as_streaming_dataset(split=self.split		)
              UpperCamelCase_          =  None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
              self.builder.download_and_prepare(
                  download_mode=_SCREAMING_SNAKE_CASE					,   file_format=self._file_format					,   )
              return self.builder.as_dataset(split=self.split		)

