





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  lowercase    (      UpperCAmelCase_ ,					unittest.TestCase       ):






					"""simple docstring"""



					snake_case_						  =			KandinskyVaaControlnetImgaImgPipeline
					snake_case_						  =			["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
					snake_case_						  =			["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
					snake_case_						  =			[
					    """generator""",
					    """height""",
					    """width""",
					    """strength""",
					    """guidance_scale""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					snake_case_						  =			False





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

						"""simple docstring"""



						return 32





					@property
					def       _UpperCamelCase       (    self						:     int							):

						"""simple docstring"""



						return 32





					@property
					def       _UpperCamelCase       (    self						:     List[Any]							):

						"""simple docstring"""



						return self.time_input_dim





					@property
					def       _UpperCamelCase       (    self						:     Tuple							):

						"""simple docstring"""



						return self.time_input_dim * 4





					@property
					def       _UpperCamelCase       (    self						:     Tuple							):

						"""simple docstring"""



						return 1_00





					@property
					def       _UpperCamelCase       (    self						:     Tuple							):

						"""simple docstring"""



						torch.manual_seed(0							)

						lowerCamelCase__										=			{
						    '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__										=			UNetaDConditionModel(**__UpperCAmelCase							)
						return model





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

						"""simple docstring"""



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





					def       _UpperCamelCase       (    self						:     Dict							):

						"""simple docstring"""



						lowerCamelCase__										=			self.dummy_unet
						lowerCamelCase__										=			self.dummy_movq

						lowerCamelCase__										=			{
						    'num_train_timesteps': 10_00,
						    '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__										=			DDIMScheduler(**__UpperCAmelCase							)

						lowerCamelCase__										=			{
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components





					def       _UpperCamelCase       (    self						:     List[str] ,					a_						:     Optional[Any] ,					a_						:     Optional[Any]=0							):

						"""simple docstring"""



						lowerCamelCase__										=			floats_tensor((1, self.text_embedder_hidden_size) ,					rng=random.Random(__UpperCAmelCase							)							).to(__UpperCAmelCase							)
						lowerCamelCase__										=			floats_tensor((1, self.text_embedder_hidden_size) ,					rng=random.Random(seed + 1							)							).to(
						    __UpperCAmelCase							)
						# create init_image
						lowerCamelCase__										=			floats_tensor((1, 3, 64, 64) ,					rng=random.Random(__UpperCAmelCase							)							).to(__UpperCAmelCase							)
						lowerCamelCase__										=			image.cpu().permute(0 ,					2 ,					3 ,					1							)[0]
						lowerCamelCase__										=			Image.fromarray(np.uinta(__UpperCAmelCase							)							).convert("""RGB"""							).resize((2_56, 2_56)							)
						# create hint
						lowerCamelCase__										=			floats_tensor((1, 3, 64, 64) ,					rng=random.Random(__UpperCAmelCase							)							).to(__UpperCAmelCase							)

						if str(__UpperCAmelCase							).startswith("""mps"""							):
							lowerCamelCase__										=			torch.manual_seed(__UpperCAmelCase							)
						else:
							lowerCamelCase__										=			torch.Generator(device=__UpperCAmelCase							).manual_seed(__UpperCAmelCase							)
						lowerCamelCase__										=			{
						    '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       _UpperCamelCase       (    self						:     List[Any]							):

						"""simple docstring"""



						lowerCamelCase__										=			'cpu'

						lowerCamelCase__										=			self.get_dummy_components()

						lowerCamelCase__										=			self.pipeline_class(**__UpperCAmelCase							)
						lowerCamelCase__										=			pipe.to(__UpperCAmelCase							)

						pipe.set_progress_bar_config(disable=__UpperCAmelCase							)

						lowerCamelCase__										=			pipe(**self.get_dummy_inputs(__UpperCAmelCase							)							)
						lowerCamelCase__										=			output.images

						lowerCamelCase__										=			pipe(
						    **self.get_dummy_inputs(__UpperCAmelCase							) ,					return_dict=__UpperCAmelCase ,					)[0]

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

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

						lowerCamelCase__										=			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  lowercase    (      unittest.TestCase       ):






					"""simple docstring"""



					def       _UpperCamelCase       (    self						:     Tuple							):

						"""simple docstring"""



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





					def       _UpperCamelCase       (    self						:     Union[str, Any]							):

						"""simple docstring"""



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

						lowerCamelCase__										=			load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
						lowerCamelCase__										=			init_image.resize((5_12, 5_12)							)

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

						lowerCamelCase__										=			'A robot, 4k photo'

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

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

						pipeline.set_progress_bar_config(disable=__UpperCAmelCase							)

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

						lowerCamelCase__										=			pipe_prior(
						    __UpperCAmelCase ,					image=__UpperCAmelCase ,					strength=0.8_5 ,					generator=__UpperCAmelCase ,					negative_prompt="""""" ,					).to_tuple()

						lowerCamelCase__										=			pipeline(
						    image=__UpperCAmelCase ,					image_embeds=__UpperCAmelCase ,					negative_image_embeds=__UpperCAmelCase ,					hint=__UpperCAmelCase ,					generator=__UpperCAmelCase ,					num_inference_steps=1_00 ,					height=5_12 ,					width=5_12 ,					strength=0.5 ,					output_type="""np""" ,					)

						lowerCamelCase__										=			output.images[0]

						assert image.shape == (5_12, 5_12, 3)

						assert_mean_pixel_difference(__UpperCAmelCase ,					__UpperCAmelCase							)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XCLIPModel""",
      """XCLIPPreTrainedModel""",
      """XCLIPTextModel""",
      """XCLIPVisionModel""",
  ]

if TYPE_CHECKING:
  from .configuration_x_clip import (
      XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      XCLIPConfig,
      XCLIPTextConfig,
      XCLIPVisionConfig,
  )
  from .processing_x_clip import XCLIPProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_x_clip import (
        XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        XCLIPModel,
        XCLIPPreTrainedModel,
        XCLIPTextModel,
        XCLIPVisionModel,
    )

else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
