
'''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 			UpperCAmelCase__    (							A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		KandinskyVaaControlnetImgaImgPipeline
    lowerCAmelCase_					     =		['image_embeds', 'negative_image_embeds', 'image', 'hint']
    lowerCAmelCase_					     =		['image_embeds', 'negative_image_embeds', 'image', 'hint']
    lowerCAmelCase_					     =		[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowerCAmelCase_					     =		False



    @property
    def 	lowerCamelCase_	(						self   :							Any  ):
          return 3_2



    @property
    def 	lowerCamelCase_	(						self   :							List[str]  ):
          return 3_2



    @property
    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          return self.time_input_dim



    @property
    def 	lowerCamelCase_	(						self   :							Any  ):
          return self.time_input_dim * 4



    @property
    def 	lowerCamelCase_	(						self   :							str  ):
          return 1_0_0



    @property
    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          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					: Any		    = UNetaDConditionModel(**__A  )
          return model



    @property
    def 	lowerCamelCase_	(						self   :							Any  ):
          return {
              "block_out_channels": [3_2, 3_2, 6_4, 6_4],
              "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": 1_2,
              "out_channels": 3,
              "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
              "vq_embed_dim": 4,
          }



    @property
    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          torch.manual_seed(0  )
          _lowerCamelCase					: List[Any]		    = VQModel(**self.dummy_movq_kwargs  )
          return model



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Any		    = self.dummy_unet
          _lowerCamelCase					: int		    = self.dummy_movq

          _lowerCamelCase					: Optional[int]		    = {
              "num_train_timesteps": 1_0_0_0,
              "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					: Optional[Any]		    = DDIMScheduler(**__A  )

          _lowerCamelCase					: Dict		    = {
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }

          return components



    def 	lowerCamelCase_	(						self   :							Dict,__A   :							List[Any],__A   :							int=0  ):
          _lowerCamelCase					: List[str]		    = floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(__A  )  ).to(__A  )
          _lowerCamelCase					: Union[str, Any]		    = floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(seed + 1  )  ).to(
              __A  )
          # create init_image
          _lowerCamelCase					: Any		    = floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(__A  )  ).to(__A  )
          _lowerCamelCase					: Optional[Any]		    = image.cpu().permute(0,2,3,1  )[0]
          _lowerCamelCase					: List[Any]		    = Image.fromarray(np.uinta(__A  )  ).convert("RGB"  ).resize((2_5_6, 2_5_6)  )
          # create hint
          _lowerCamelCase					: str		    = floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(__A  )  ).to(__A  )

          if str(__A  ).startswith("mps"  ):
                _lowerCamelCase					: int		    = torch.manual_seed(__A  )
          else:
                _lowerCamelCase					: Union[str, Any]		    = torch.Generator(device=__A  ).manual_seed(__A  )
          _lowerCamelCase					: Optional[Any]		    = {
              "image": init_image,
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "hint": hint,
              "generator": generator,
              "height": 6_4,
              "width": 6_4,
              "num_inference_steps": 1_0,
              "guidance_scale": 7.0,
              "strength": 0.2,
              "output_type": "np",
          }
          return inputs



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Optional[int]		    = "cpu"

          _lowerCamelCase					: List[Any]		    = self.get_dummy_components()

          _lowerCamelCase					: Any		    = self.pipeline_class(**__A  )
          _lowerCamelCase					: Optional[int]		    = pipe.to(__A  )

          pipe.set_progress_bar_config(disable=__A  )

          _lowerCamelCase					: Optional[Any]		    = pipe(**self.get_dummy_inputs(__A  )  )
          _lowerCamelCase					: List[Any]		    = output.images

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

          _lowerCamelCase					: Optional[Any]		    = image[0, -3:, -3:, -1]
          _lowerCamelCase					: int		    = image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 6_4, 6_4, 3)

          _lowerCamelCase					: Any		    = 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 			UpperCAmelCase__    (							unittest.TestCase			):



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: List[str]		    = load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"  )

          _lowerCamelCase					: Union[str, Any]		    = load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"  )
          _lowerCamelCase					: Any		    = init_image.resize((5_1_2, 5_1_2)  )

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

          _lowerCamelCase					: int		    = "A robot, 4k photo"

          _lowerCamelCase					: Optional[Any]		    = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-prior",torch_dtype=torch.floataa  )
          pipe_prior.to(__A  )

          _lowerCamelCase					: str		    = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-controlnet-depth",torch_dtype=torch.floataa  )
          _lowerCamelCase					: Optional[int]		    = 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					: Any		    = pipeline(
              image=__A,image_embeds=__A,negative_image_embeds=__A,hint=__A,generator=__A,num_inference_steps=1_0_0,height=5_1_2,width=5_1_2,strength=0.5,output_type="np",)

          _lowerCamelCase					: List[Any]		    = output.images[0]

          assert image.shape == (5_1_2, 5_1_2, 3)

          assert_mean_pixel_difference(__A,__A  )




from __future__ import annotations

from typing import Any


def A    (							__UpperCamelCase       )    ->   int:
			if not postfix_notation:
						return 0

			A__	  =     {'+', '-', '*', '/'}
			A__	  =     []

			for token in postfix_notation:
						if token in operations:
									A__  ,			A__	  =     stack.pop(), stack.pop()
									if token == "+":
												stack.append(a + b       )
									elif token == "-":
												stack.append(a - b       )
									elif token == "*":
												stack.append(a * b       )
									else:
												if a * b < 0 and a % b != 0:
															stack.append(a // b + 1       )
												else:
															stack.append(a // b       )
						else:
									stack.append(int(__UpperCamelCase       )       )

			return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




