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 	_lowerCAmelCase		(  _SCREAMING_SNAKE_CASE   , unittest.TestCase					):
							_UpperCAmelCase         =  KandinskyVaaControlnetImgaImgPipeline
							_UpperCAmelCase         =  ['image_embeds', 'negative_image_embeds', 'image', 'hint']
							_UpperCAmelCase         =  ['image_embeds', 'negative_image_embeds', 'image', 'hint']
							_UpperCAmelCase         =  [
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							_UpperCAmelCase         =  False







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







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







							@property
							def 					snake_case	(					self  :     Optional[Any]		):
										return self.time_input_dim







							@property
							def 					snake_case	(					self  :     str		):
										return self.time_input_dim * 4







							@property
							def 					snake_case	(					self  :     Union[str, Any]		):
										return 100







							@property
							def 					snake_case	(					self  :     Union[str, Any]		):
										torch.manual_seed(0		)

										lowerCamelCase    :Union[str, Any]    	=  {
										    """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,
										}

										lowerCamelCase    :Union[str, Any]    	=  UNetaDConditionModel(**__snake_case		)
										return model







							@property
							def 					snake_case	(					self  :     Any		):
										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 					snake_case	(					self  :     List[str]		):
										torch.manual_seed(0		)
										lowerCamelCase    :List[Any]    	=  VQModel(**self.dummy_movq_kwargs		)
										return model







							def 					snake_case	(					self  :     Optional[Any]		):
										lowerCamelCase    :Optional[Any]    	=  self.dummy_unet
										lowerCamelCase    :List[str]    	=  self.dummy_movq

										lowerCamelCase    :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    :Dict    	=  DDIMScheduler(**__snake_case		)

										lowerCamelCase    :Optional[Any]    	=  {
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components







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

										if str(__snake_case		).startswith('''mps'''		):
													lowerCamelCase    :Dict    	=  torch.manual_seed(__snake_case		)
										else:
													lowerCamelCase    :List[Any]    	=  torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
										lowerCamelCase    :str    	=  {
										    """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 					snake_case	(					self  :     Any		):
										lowerCamelCase    :int    	=  """cpu"""

										lowerCamelCase    :Union[str, Any]    	=  self.get_dummy_components()

										lowerCamelCase    :Any    	=  self.pipeline_class(**__snake_case		)
										lowerCamelCase    :str    	=  pipe.to(__snake_case		)

										pipe.set_progress_bar_config(disable=__snake_case		)

										lowerCamelCase    :str    	=  pipe(**self.get_dummy_inputs(__snake_case		)		)
										lowerCamelCase    :Dict    	=  output.images

										lowerCamelCase    :List[str]    	=  pipe(
										    **self.get_dummy_inputs(__snake_case		)						,       return_dict=__snake_case						,       )[0]

										lowerCamelCase    :str    	=  image[0, -3:, -3:, -1]
										lowerCamelCase    :List[str]    	=  image_from_tuple[0, -3:, -3:, -1]

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

										lowerCamelCase    :Dict    	=  np.array(
										    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]		)
										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 	_lowerCAmelCase		(  unittest.TestCase					):







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







							def 					snake_case	(					self  :     int		):
										lowerCamelCase    :List[str]    	=  load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''		)

										lowerCamelCase    :Union[str, Any]    	=  load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
										lowerCamelCase    :List[Any]    	=  init_image.resize((512, 512)		)

										lowerCamelCase    :Any    	=  load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/hint_image_cat.png'''		)
										lowerCamelCase    :Tuple    	=  torch.from_numpy(np.array(__snake_case		)		).float() / 2_5_5.0
										lowerCamelCase    :Tuple    	=  hint.permute(2						,       0						,       1		).unsqueeze(0		)

										lowerCamelCase    :Optional[Any]    	=  """A robot, 4k photo"""

										lowerCamelCase    :Optional[int]    	=  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior'''						,       torch_dtype=torch.floataa		)
										pipe_prior.to(__snake_case		)

										lowerCamelCase    :List[Any]    	=  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''						,       torch_dtype=torch.floataa		)
										lowerCamelCase    :Any    	=  pipeline.to(__snake_case		)

										pipeline.set_progress_bar_config(disable=__snake_case		)

										lowerCamelCase    :Optional[int]    	=  torch.Generator(device='''cpu'''		).manual_seed(0		)

										lowerCamelCase    :int    	=  pipe_prior(
										    __snake_case						,       image=__snake_case						,       strength=0.8_5						,       generator=__snake_case						,       negative_prompt=''''''						,       ).to_tuple()

										lowerCamelCase    :Tuple    	=  pipeline(
										    image=__snake_case						,       image_embeds=__snake_case						,       negative_image_embeds=__snake_case						,       hint=__snake_case						,       generator=__snake_case						,       num_inference_steps=100						,       height=512						,       width=512						,       strength=0.5						,       output_type='''np'''						,       )

										lowerCamelCase    :Tuple    	=  output.images[0]

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

										assert_mean_pixel_difference(__snake_case						,       __snake_case		)







'''simple docstring'''






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__A   :    Union[str, Any]											= TypeVar("KEY")
__A   :    Union[str, Any]											= TypeVar("VAL")






@dataclass(frozen=_SCREAMING_SNAKE_CASE      ,slots=_SCREAMING_SNAKE_CASE)
class   __snake_case			(  Generic[KEY, VAL]):







    """simple docstring"""



    lowercase  					=   42
    lowercase  					=   42







class   __snake_case			(  _Item):







    """simple docstring"""



    def __init__(  self       :    int						)  ->    None:
      super().__init__(lowerCamelCase       ,		lowerCamelCase						)





    def __bool__(  self       :    Tuple						)  ->    bool:
      return False







__A   :    Any											= _DeletedItem()






class   __snake_case			(  MutableMapping[KEY, VAL]):







    """simple docstring"""



    def __init__(  self       :    Optional[int]       ,		lowerCamelCase       :    int = 8       ,		lowerCamelCase       :    float = 0.75						)  ->    None:
      lowerCAmelCase_     : str     =  initial_block_size
      lowerCAmelCase_     : list[_Item | None]     =  [None] * initial_block_size
      assert 0.0 < capacity_factor < 1.0
      lowerCAmelCase_     : Optional[Any]     =  capacity_factor
      lowerCAmelCase_     : List[Any]     =  0

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    KEY						)  ->    int:
      return hash(lowerCamelCase						) % len(self._buckets						)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int:
      return (ind + 1) % len(self._buckets						)

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    KEY       ,		lowerCamelCase       :    VAL						)  ->    bool:
      lowerCAmelCase_     : Union[str, Any]     =  self._buckets[ind]
      if not stored:
        lowerCAmelCase_     : List[Any]     =  _Item(lowerCamelCase       ,		lowerCamelCase						)
        self._len += 1
        return True
      elif stored.key == key:
        lowerCAmelCase_     : List[str]     =  _Item(lowerCamelCase       ,		lowerCamelCase						)
        return True
      else:
        return False

    def __lowercase  (  self       :    Optional[Any]						)  ->    bool:
      lowerCAmelCase_     : str     =  len(self._buckets						) * self._capacity_factor
      return len(self						) >= int(lowerCamelCase						)

    def __lowercase  (  self       :    Dict						)  ->    bool:
      if len(self._buckets						) <= self._initial_block_size:
        return False
      lowerCAmelCase_     : Optional[int]     =  len(self._buckets						) * self._capacity_factor / 2
      return len(self						) < limit

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Union[str, Any]     =  self._buckets
      lowerCAmelCase_     : str     =  [None] * new_size
      lowerCAmelCase_     : str     =  0
      for item in old_buckets:
        if item:
          self._add_item(item.key       ,		item.val						)

    def __lowercase  (  self       :    Tuple						)  ->    None:
      self._resize(len(self._buckets						) * 2						)

    def __lowercase  (  self       :    Union[str, Any]						)  ->    None:
      self._resize(len(self._buckets						) // 2						)

    def __lowercase  (  self       :    Optional[int]       ,		lowerCamelCase       :    KEY						)  ->    Iterator[int]:
      lowerCAmelCase_     : int     =  self._get_bucket_index(lowerCamelCase						)
      for _ in range(len(self._buckets						)						):
        yield ind
        lowerCAmelCase_     : Dict     =  self._get_next_ind(lowerCamelCase						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    KEY       ,		lowerCamelCase       :    VAL						)  ->    None:
      for ind in self._iterate_buckets(lowerCamelCase						):
        if self._try_set(lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase						):
          break

    def __setitem__(  self       :    Tuple       ,		lowerCamelCase       :    KEY       ,		lowerCamelCase       :    VAL						)  ->    None:
      if self._is_full():
        self._size_up()

      self._add_item(lowerCamelCase       ,		lowerCamelCase						)

    def __delitem__(  self       :    Optional[int]       ,		lowerCamelCase       :    KEY						)  ->    None:
      for ind in self._iterate_buckets(lowerCamelCase						):
        lowerCAmelCase_     : int     =  self._buckets[ind]
        if item is None:
          raise KeyError(lowerCamelCase						)
        if item is _deleted:
          continue
        if item.key == key:
          lowerCAmelCase_     : List[Any]     =  _deleted
          self._len -= 1
          break
      if self._is_sparse():
        self._size_down()

    def __getitem__(  self       :    Any       ,		lowerCamelCase       :    KEY						)  ->    VAL:
      for ind in self._iterate_buckets(lowerCamelCase						):
        lowerCAmelCase_     : Dict     =  self._buckets[ind]
        if item is None:
          break
        if item is _deleted:
          continue
        if item.key == key:
          return item.val
      raise KeyError(lowerCamelCase						)

    def __len__(  self       :    Any						)  ->    int:
      return self._len

    def __iter__(  self       :    Optional[Any]						)  ->    Iterator[KEY]:
      yield from (item.key for item in self._buckets if item)





    def __repr__(  self       :    List[Any]						)  ->    str:
      lowerCAmelCase_     : int     =  """ ,""".join(
          F'{item.key}: {item.val}' for item in self._buckets if item						)
      return F'HashMap({val_string})'



