"""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        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
				_snake_case     :  Optional[Any]     							=							KandinskyVaaControlnetImgaImgPipeline
				_snake_case     :  Optional[int]     							=							['image_embeds', 'negative_image_embeds', 'image', 'hint']
				_snake_case     :  int     							=							['image_embeds', 'negative_image_embeds', 'image', 'hint']
				_snake_case     :  Any     							=							[
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				_snake_case     :  List[Any]     							=							False


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


				@property
				def      lowerCAmelCase_  (					self	:  Optional[int]					):
						return 32


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


				@property
				def      lowerCAmelCase_  (					self	:  Tuple					):
						return self.time_input_dim * 4


				@property
				def      lowerCAmelCase_  (					self	:  Any					):
						return 100


				@property
				def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
						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(**__lowerCAmelCase					)
						return model


				@property
				def      lowerCAmelCase_  (					self	:  Optional[int]					):
						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      lowerCAmelCase_  (					self	:  Tuple					):
						torch.manual_seed(0					)
						_UpperCAmelCase						 =      VQModel(**self.dummy_movq_kwargs					)
						return model


				def      lowerCAmelCase_  (					self	:  Dict					):
						_UpperCAmelCase						 =      self.dummy_unet
						_UpperCAmelCase						 =      self.dummy_movq

						_UpperCAmelCase						 =      {
						    """num_train_timesteps""": 1000,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.00_085,
						    """beta_end""": 0.012,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						_UpperCAmelCase						 =      DDIMScheduler(**__lowerCAmelCase					)

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

						return components


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

						if str(__lowerCAmelCase					).startswith("""mps"""					):
								_UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
						else:
								_UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)
						_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      lowerCAmelCase_  (					self	:  Optional[Any]					):
						_UpperCAmelCase						 =      """cpu"""

						_UpperCAmelCase						 =      self.get_dummy_components()

						_UpperCAmelCase						 =      self.pipeline_class(**__lowerCAmelCase					)
						_UpperCAmelCase						 =      pipe.to(__lowerCAmelCase					)

						pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

						_UpperCAmelCase						 =      pipe(**self.get_dummy_inputs(__lowerCAmelCase					)					)
						_UpperCAmelCase						 =      output.images

						_UpperCAmelCase						 =      pipe(
						    **self.get_dummy_inputs(__lowerCAmelCase					)   ,				return_dict=__lowerCAmelCase   ,				)[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_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]					)
						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        a				(		unittest.TestCase   ):


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


				def      lowerCAmelCase_  (					self	:  Optional[Any]					):
						_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(__lowerCAmelCase					)					).float() / 255.0
						_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(__lowerCAmelCase					)

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

						pipeline.set_progress_bar_config(disable=__lowerCAmelCase					)

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

						_UpperCAmelCase ,				_UpperCAmelCase						 =      pipe_prior(
						    __lowerCAmelCase   ,				image=__lowerCAmelCase   ,				strength=0.85   ,				generator=__lowerCAmelCase   ,				negative_prompt=""""""   ,				).to_tuple()

						_UpperCAmelCase						 =      pipeline(
						    image=__lowerCAmelCase   ,				image_embeds=__lowerCAmelCase   ,				negative_image_embeds=__lowerCAmelCase   ,				hint=__lowerCAmelCase   ,				generator=__lowerCAmelCase   ,				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(__lowerCAmelCase   ,				__lowerCAmelCase					)

"""simple docstring"""


import copy
import re


class        a				:
    _snake_case     :  Any     							=							'hp'
    _snake_case     :  Union[str, Any]     							=							{}
    _snake_case     :  Optional[Any]     							=							None


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      prefix
      _UpperCAmelCase						 =      defaults
      cls.build_naming_info()


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]					):
      if len(__lowerCAmelCase					) == 0:
        return ""
      _UpperCAmelCase						 =      None
      if any(char.isdigit() for char in word					):
        raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''					)
      if word in info["short_word"]:
        return info["short_word"][word]
      for prefix_len in range(1   ,				len(__lowerCAmelCase					) + 1					):
        _UpperCAmelCase						 =      word[:prefix_len]
        if prefix in info["reverse_short_word"]:
          continue
        else:
          _UpperCAmelCase						 =      prefix
          break

      if short_word is None:
        # Paranoid fallback
        def int_to_alphabetic(__lowerCAmelCase	:  List[Any]					):
          _UpperCAmelCase						 =      """"""
          while integer != 0:
            _UpperCAmelCase						 =      chr(ord("""A"""					) + integer % 10					) + s
            integer //= 10
          return s

        _UpperCAmelCase						 =      0
        while True:
          _UpperCAmelCase						 =      word + """#""" + int_to_alphabetic(__lowerCAmelCase					)
          if sword in info["reverse_short_word"]:
            continue
          else:
            _UpperCAmelCase						 =      sword
            break

      _UpperCAmelCase						 =      short_word
      _UpperCAmelCase						 =      word
      return short_word


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      param_name.split("""_"""					)

      _UpperCAmelCase						 =      [TrialShortNamer.shortname_for_word(__lowerCAmelCase   ,				__lowerCAmelCase					) for word in words]

      # We try to create a separatorless short name, but if there is a collision we have to fallback
      # to a separated short name
      _UpperCAmelCase						 =      ["""""", """_"""]

      for separator in separators:
        _UpperCAmelCase						 =      separator.join(__lowerCAmelCase					)
        if shortname not in info["reverse_short_param"]:
          _UpperCAmelCase						 =      shortname
          _UpperCAmelCase						 =      param_name
          return shortname

      return param_name


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      TrialShortNamer.shortname_for_key(__lowerCAmelCase   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      short_name
      _UpperCAmelCase						 =      param_name


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Optional[Any]					):
      if cls.NAMING_INFO is not None:
        return

      _UpperCAmelCase						 =      {
          """short_word""": {},
          """reverse_short_word""": {},
          """short_param""": {},
          """reverse_short_param""": {},
      }

      _UpperCAmelCase						 =      list(cls.DEFAULTS.keys()					)

      for k in field_keys:
        cls.add_new_param_name(__lowerCAmelCase   ,				__lowerCAmelCase					)

      _UpperCAmelCase						 =      info


    @classmethod
    def      lowerCAmelCase_  (					cls	:  List[str]   ,				__lowerCAmelCase	:  int					):
      cls.build_naming_info()
      assert cls.PREFIX is not None
      _UpperCAmelCase						 =      [copy.copy(cls.PREFIX					)]

      for k, v in params.items():
        if k not in cls.DEFAULTS:
          raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''					)
        if v == cls.DEFAULTS[k]:
          # The default value is not added to the name
          continue

        _UpperCAmelCase						 =      cls.NAMING_INFO["""short_param"""][k]

        if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
          _UpperCAmelCase						 =      1 if v else 0

        _UpperCAmelCase						 =      """""" if isinstance(__lowerCAmelCase   ,				(int, float)					) else """-"""
        _UpperCAmelCase						 =      f'''{key}{sep}{v}'''
        name.append(__lowerCAmelCase					)

      return "_".join(__lowerCAmelCase					)


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      repr[len(cls.PREFIX					) + 1 :]
      if repr == "":
        _UpperCAmelCase						 =      []
      else:
        _UpperCAmelCase						 =      repr.split("""_"""					)

      _UpperCAmelCase						 =      {}

      for value in values:
        if "-" in value:
          _UpperCAmelCase ,				_UpperCAmelCase						 =      value.split("""-"""					)
        else:
          _UpperCAmelCase						 =      re.sub("""[0-9.]"""   ,				""""""   ,				__lowerCAmelCase					)
          _UpperCAmelCase						 =      float(re.sub("""[^0-9.]"""   ,				""""""   ,				__lowerCAmelCase					)					)

        _UpperCAmelCase						 =      cls.NAMING_INFO["""reverse_short_param"""][p_k]

        _UpperCAmelCase						 =      p_v

      for k in cls.DEFAULTS:
        if k not in parameters:
          _UpperCAmelCase						 =      cls.DEFAULTS[k]

      return parameters
