import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	List[str]    		= KandinskyVaaInpaintPipeline
  lowerCAmelCase__		:	Optional[Any]    		= ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  lowerCAmelCase__		:	Optional[Any]    		= [
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  lowerCAmelCase__		:	str    		= [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  lowerCAmelCase__		:	Optional[int]    		= False






  @property
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Optional[int]:




    '''simple docstring'''




    return 32






  @property
  def 	_UpperCAmelCase				(				self:			int   ) -> List[str]:




    '''simple docstring'''




    return 32






  @property
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    return self.time_input_dim






  @property
  def 	_UpperCAmelCase				(				self:			str   ) -> Tuple:




    '''simple docstring'''




    return self.time_input_dim * 4






  @property
  def 	_UpperCAmelCase				(				self:			Dict   ) -> Dict:




    '''simple docstring'''




    return 100






  @property
  def 	_UpperCAmelCase				(				self:			str   ) -> List[Any]:




    '''simple docstring'''




    torch.manual_seed(0   )

    __UpperCAmelCase						    =       {
        "in_channels": 9,
        # Out channels is double in channels because predicts mean and variance
        "out_channels": 8,
        "addition_embed_type": "image",
        "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 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Any:




    '''simple docstring'''




    return {
        "block_out_channels": [32, 64],
        "down_block_types": ["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",
        ],
        "vq_embed_dim": 4,
    }






  @property
  def 	_UpperCAmelCase				(				self:			Any   ) -> Union[str, Any]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			str   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.dummy_unet
    __UpperCAmelCase						    =       self.dummy_movq

    __UpperCAmelCase						    =       DDIMScheduler(
        num_train_timesteps=1_000				,				beta_schedule="linear"				,				beta_start=0.00085				,				beta_end=0.012				,				clip_sample=__lowerCAmelCase				,				set_alpha_to_one=__lowerCAmelCase				,				steps_offset=1				,				prediction_type="epsilon"				,				thresholding=__lowerCAmelCase				,				)

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

    return components






  def 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Optional[Any]=0   ) -> Optional[int]:




    '''simple docstring'''




    __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 mask
    __UpperCAmelCase						    =       np.ones((64, 64)				,				dtype=np.floataa   )
    __UpperCAmelCase						    =       0

    if str(__lowerCAmelCase   ).startswith("mps"   ):
      __UpperCAmelCase						    =       torch.manual_seed(__lowerCAmelCase   )
    else:
      __UpperCAmelCase						    =       torch.Generator(device=__lowerCAmelCase   ).manual_seed(__lowerCAmelCase   )
    __UpperCAmelCase						    =       {
        "image": init_image,
        "mask_image": mask,
        "image_embeds": image_embeds,
        "negative_image_embeds": negative_image_embeds,
        "generator": generator,
        "height": 64,
        "width": 64,
        "num_inference_steps": 2,
        "guidance_scale": 4.0,
        "output_type": "np",
    }
    return inputs






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Optional[Any]:




    '''simple docstring'''




    __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]

    print(F'''image.shape {image.shape}'''   )

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

    __UpperCAmelCase						    =       np.array(
        [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]   )

    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()}'''






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Tuple:




    '''simple docstring'''




    super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






@slow
@require_torch_gpu
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Tuple:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"   )

    __UpperCAmelCase						    =       load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
    __UpperCAmelCase						    =       np.ones((768, 768)				,				dtype=np.floataa   )
    __UpperCAmelCase						    =       0

    __UpperCAmelCase						    =       "a hat"

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

    __UpperCAmelCase						    =       KandinskyVaaInpaintPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-2-decoder-inpaint"				,				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				,				generator=__lowerCAmelCase				,				num_inference_steps=5				,				negative_prompt=""				,				).to_tuple()

    __UpperCAmelCase						    =       pipeline(
        image=__lowerCAmelCase				,				mask_image=__lowerCAmelCase				,				image_embeds=__lowerCAmelCase				,				negative_image_embeds=__lowerCAmelCase				,				generator=__lowerCAmelCase				,				num_inference_steps=100				,				height=768				,				width=768				,				output_type="np"				,				)

    __UpperCAmelCase						    =       output.images[0]

    assert image.shape == (768, 768, 3)

    assert_mean_pixel_difference(__lowerCAmelCase				,				__lowerCAmelCase   )




def       __lowerCAmelCase      (						A_   : str					)    ->  bool:
  if not all(x.isalpha() for x in string					):
    raise ValueError("String must only contain alphabetic characters."					)

  __UpperCAmelCase						    =       sorted(string.lower()					)
  return len(A_					) == len(set(A_					)					)


if __name__ == "__main__":
       a_							=      input("""Enter a string """).strip()

       a_							=      is_isogram(input_str)
       print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")



