






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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     (			a      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Tuple			 =KandinskyVaaControlnetPipeline
				UpperCamelCase__ :      Dict			 =["""image_embeds""", """negative_image_embeds""", """hint"""]
				UpperCamelCase__ :      int			 =["""image_embeds""", """negative_image_embeds""", """hint"""]
				UpperCamelCase__ :      List[str]			 =[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCamelCase__ :      Tuple			 =False


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return 32


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return 32


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return self.time_input_dim


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return self.time_input_dim * 4


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									return 100


				@property
				def       __lowercase							( self    ):




									"""simple docstring"""

									torch.manual_seed(0    )

									__UpperCamelCase      :					Tuple       		={
									    '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      :					Optional[Any]       		=UNetaDConditionModel(**lowerCamelCase__    )
									return model


				@property
				def       __lowercase							( self    ):




									"""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    ):




									"""simple docstring"""

									torch.manual_seed(0    )
									__UpperCamelCase      :					List[Any]       		=VQModel(**self.dummy_movq_kwargs    )
									return model


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		=self.dummy_unet
									__UpperCamelCase      :					List[Any]       		=self.dummy_movq

									__UpperCamelCase      :					int       		=DDIMScheduler(
									    num_train_timesteps=1000							,  beta_schedule='linear'							,  beta_start=0.00_085							,  beta_end=0.012							,  clip_sample=lowerCamelCase__							,  set_alpha_to_one=lowerCamelCase__							,  steps_offset=1							,  prediction_type='epsilon'							,  thresholding=lowerCamelCase__							,  )

									__UpperCamelCase      :					Optional[int]       		={
									    'unet': unet,
									    'scheduler': scheduler,
									    'movq': movq,
									}
									return components


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__=0    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=floats_tensor((1, self.text_embedder_hidden_size)							,  rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
									__UpperCamelCase      :					List[str]       		=floats_tensor((1, self.text_embedder_hidden_size)							,  rng=random.Random(seed + 1    )    ).to(
									    lowerCamelCase__    )

									# create hint
									__UpperCamelCase      :					List[str]       		=floats_tensor((1, 3, 64, 64)							,  rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )

									if str(lowerCamelCase__    ).startswith('mps'    ):
														__UpperCamelCase      :					Optional[int]       		=torch.manual_seed(lowerCamelCase__    )
									else:
														__UpperCamelCase      :					List[Any]       		=torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
									__UpperCamelCase      :					Tuple       		={
									    'image_embeds': image_embeds,
									    'negative_image_embeds': negative_image_embeds,
									    'hint': hint,
									    'generator': generator,
									    'height': 64,
									    'width': 64,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					str       		='cpu'

									__UpperCamelCase      :					Tuple       		=self.get_dummy_components()

									__UpperCamelCase      :					Any       		=self.pipeline_class(**lowerCamelCase__    )
									__UpperCamelCase      :					Optional[Any]       		=pipe.to(lowerCamelCase__    )

									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									__UpperCamelCase      :					Optional[Any]       		=pipe(**self.get_dummy_inputs(lowerCamelCase__    )    )
									__UpperCamelCase      :					Union[str, Any]       		=output.images

									__UpperCamelCase      :					Optional[Any]       		=pipe(
									    **self.get_dummy_inputs(lowerCamelCase__    )							,  return_dict=lowerCamelCase__							,  )[0]

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

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

									__UpperCamelCase      :					List[Any]       		=np.array(
									    [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]    )

									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 ):





				"""simple docstring"""







				def       __lowercase							( self    ):




									"""simple docstring"""

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







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					str       		=load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'    )

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

									__UpperCamelCase      :					Dict       		=KandinskyVaaPriorPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-2-prior'							,  torch_dtype=torch.floataa    )
									pipe_prior.to(lowerCamelCase__    )

									__UpperCamelCase      :					Dict       		=KandinskyVaaControlnetPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-2-controlnet-depth'							,  torch_dtype=torch.floataa    )
									__UpperCamelCase      :					List[Any]       		=pipeline.to(lowerCamelCase__    )
									pipeline.set_progress_bar_config(disable=lowerCamelCase__    )

									__UpperCamelCase      :					Tuple       		='A robot, 4k photo'

									__UpperCamelCase      :					Optional[int]       		=torch.Generator(device='cuda'    ).manual_seed(0    )
									__UpperCamelCase		,				__UpperCamelCase      :					List[str]       		=pipe_prior(
									    lowerCamelCase__							,  generator=lowerCamelCase__							,  num_inference_steps=5							,  negative_prompt=''							,  ).to_tuple()

									__UpperCamelCase      :					Any       		=torch.Generator(device='cuda'    ).manual_seed(0    )
									__UpperCamelCase      :					str       		=pipeline(
									    image_embeds=lowerCamelCase__							,  negative_image_embeds=lowerCamelCase__							,  hint=lowerCamelCase__							,  generator=lowerCamelCase__							,  num_inference_steps=100							,  output_type='np'							,  )

									__UpperCamelCase      :					Optional[int]       		=output.images[0]

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

									assert_mean_pixel_difference(lowerCamelCase__							,  lowerCamelCase__    )


"""simple docstring"""


from __future__ import annotations






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_failure_array(lowercase						)

  # 2) Step through text searching for pattern
  _UpperCAmelCase       , _UpperCAmelCase         =					0, 0  # index into text, pattern
  while i < len(lowercase						):
    if pattern[j] == text[i]:
      if j == (len(lowercase						) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _UpperCAmelCase         =					failure[j - 1]
      continue
    i += 1
  return False






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[0]
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					1
  while j < len(lowercase						):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCAmelCase         =					failure[i - 1]
      continue
    j += 1
    failure.append(lowercase						)
  return failure


if __name__ == "__main__":
    # Test 1)
    UpperCAmelCase__     						=  """abc1abc12"""
    UpperCAmelCase__     						=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
    UpperCAmelCase__     						=  """alskfjaldsk23adsfabcabc"""
    assert kmp(pattern, texta) and not kmp(pattern, texta)

    # Test 2)
    UpperCAmelCase__     						=  """ABABX"""
    UpperCAmelCase__     						=  """ABABZABABYABABX"""
    assert kmp(pattern, text)

    # Test 3)
    UpperCAmelCase__     						=  """AAAB"""
    UpperCAmelCase__     						=  """ABAAAAAB"""
    assert kmp(pattern, text)

    # Test 4)
    UpperCAmelCase__     						=  """abcdabcy"""
    UpperCAmelCase__     						=  """abcxabcdabxabcdabcdabcy"""
    assert kmp(pattern, text)

    # Test 5)
    UpperCAmelCase__     						=  """aabaabaaa"""
    assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
