



'''simple docstring'''





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     ):
  """simple docstring"""




  a_												=KandinskyVaaControlnetImgaImgPipeline
  a_												=["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
  a_												=["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
  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 						_lowercase     ( self      :	Any   )					->					Optional[Any]:
    return 32




  @property
  def 						_lowercase     ( self      :	str   )					->					Dict:
    return 32




  @property
  def 						_lowercase     ( self      :	Optional[int]   )					->					Optional[int]:
    return self.time_input_dim




  @property
  def 						_lowercase     ( self      :	int   )					->					Dict:
    return self.time_input_dim * 4




  @property
  def 						_lowercase     ( self      :	Union[str, Any]   )					->					str:
    return 100




  @property
  def 						_lowercase     ( self      :	List[str]   )					->					str:
    torch.manual_seed(0   )

    __lowerCamelCase       :       Optional[int]           = {
        '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       :       Optional[int]           = UNetaDConditionModel(**_a   )
    return model




  @property
  def 						_lowercase     ( self      :	Any   )					->					Optional[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 						_lowercase     ( self      :	Tuple   )					->					Optional[int]:
    torch.manual_seed(0   )
    __lowerCamelCase       :       Optional[Any]           = VQModel(**self.dummy_movq_kwargs   )
    return model




  def 						_lowercase     ( self      :	int   )					->					Any:
    __lowerCamelCase       :       Any           = self.dummy_unet
    __lowerCamelCase       :       Tuple           = self.dummy_movq

    __lowerCamelCase       :       Tuple           = {
        'num_train_timesteps': 1000,
        'beta_schedule': 'linear',
        'beta_start': 0.00085,
        'beta_end': 0.012,
        'clip_sample': False,
        'set_alpha_to_one': False,
        'steps_offset': 0,
        'prediction_type': 'epsilon',
        'thresholding': False,
    }

    __lowerCamelCase       :       List[str]           = DDIMScheduler(**_a   )

    __lowerCamelCase       :       int           = {
        'unet': unet,
        'scheduler': scheduler,
        'movq': movq,
    }

    return components




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

    if str(_a   ).startswith('mps'   ):
      __lowerCamelCase       :       Any           = torch.manual_seed(_a   )
    else:
      __lowerCamelCase       :       str           = torch.Generator(device=_a   ).manual_seed(_a   )
    __lowerCamelCase       :       Union[str, Any]           = {
        '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      :	List[str]   )					->					Any:
    __lowerCamelCase       :       int           = 'cpu'

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

    __lowerCamelCase       :       int           = self.pipeline_class(**_a   )
    __lowerCamelCase       :       int           = pipe.to(_a   )

    pipe.set_progress_bar_config(disable=_a   )

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

    __lowerCamelCase       :       Dict           = pipe(
        **self.get_dummy_inputs(_a   )			,	return_dict=_a			,	)[0]

    __lowerCamelCase       :       Tuple           = 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(
        [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]   )
    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     ):
  """simple docstring"""




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




  def 						_lowercase     ( self      :	str   )					->					Dict:
    __lowerCamelCase       :       int           = load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'   )

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

    __lowerCamelCase       :       int           = load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/kandinskyv22/hint_image_cat.png'   )
    __lowerCamelCase       :       List[Any]           = torch.from_numpy(np.array(_a   )   ).float() / 255.0
    __lowerCamelCase       :       int           = hint.permute(2			,	0			,	1   ).unsqueeze(0   )

    __lowerCamelCase       :       List[str]           = 'A robot, 4k photo'

    __lowerCamelCase       :       Any           = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-2-prior'			,	torch_dtype=torch.floataa   )
    pipe_prior.to(_a   )

    __lowerCamelCase       :       Optional[int]           = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-2-controlnet-depth'			,	torch_dtype=torch.floataa   )
    __lowerCamelCase       :       Union[str, Any]           = pipeline.to(_a   )

    pipeline.set_progress_bar_config(disable=_a   )

    __lowerCamelCase       :       Optional[Any]           = torch.Generator(device='cpu'   ).manual_seed(0   )

    __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = pipe_prior(
        _a			,	image=_a			,	strength=0.85			,	generator=_a			,	negative_prompt=''			,	).to_tuple()

    __lowerCamelCase       :       Union[str, Any]           = pipeline(
        image=_a			,	image_embeds=_a			,	negative_image_embeds=_a			,	hint=_a			,	generator=_a			,	num_inference_steps=100			,	height=512			,	width=512			,	strength=0.5			,	output_type='np'			,	)

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

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

    assert_mean_pixel_difference(_a			,	_a   )











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase											=						{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['SpeechEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






