"""simple docstring"""







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 	UpperCamelCase    (					UpperCamelCase__   ,			unittest.TestCase		):
    UpperCAmelCase       :   List[Any]					=	KandinskyVaaControlnetPipeline
    UpperCAmelCase       :   List[Any]					=	["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :   Optional[Any]					=	["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :   Tuple					=	[
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase       :   List[Any]					=	False



    @property
    def       _lowercase  (self       :   List[str])					->			Union[str, Any]:
     return 32



    @property
    def       _lowercase  (self       :   Tuple)					->			Any:
     return 32



    @property
    def       _lowercase  (self       :   Any)					->			str:
     return self.time_input_dim



    @property
    def       _lowercase  (self       :   Union[str, Any])					->			Tuple:
     return self.time_input_dim * 4



    @property
    def       _lowercase  (self       :   Any)					->			Dict:
     return 1_00



    @property
    def       _lowercase  (self       :   Tuple)					->			Optional[Any]:
     torch.manual_seed(0)

     __snake_case    :							Dict   	=				{
         """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,
     }

     __snake_case    :							List[Any]   	=				UNetaDConditionModel(**_a)
     return model



    @property
    def       _lowercase  (self       :   Union[str, Any])					->			Any:
     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       :   Union[str, Any])					->			List[Any]:
     torch.manual_seed(0)
     __snake_case    :							Tuple   	=				VQModel(**self.dummy_movq_kwargs)
     return model



    def       _lowercase  (self       :   Dict)					->			Union[str, Any]:
     __snake_case    :							int   	=				self.dummy_unet
     __snake_case    :							Tuple   	=				self.dummy_movq

     __snake_case    :							str   	=				DDIMScheduler(
         num_train_timesteps=10_00			,						beta_schedule='linear'			,						beta_start=0.00_085			,						beta_end=0.012			,						clip_sample=_a			,						set_alpha_to_one=_a			,						steps_offset=1			,						prediction_type='epsilon'			,						thresholding=_a			,						)

     __snake_case    :							Dict   	=				{
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }
     return components



    def       _lowercase  (self       :   List[Any]			,						_A       :   Optional[int]			,						_A       :   Dict=0)					->			int:
     __snake_case    :							Optional[Any]   	=				floats_tensor((1, self.text_embedder_hidden_size)			,						rng=random.Random(_a)).to(_a)
     __snake_case    :							Tuple   	=				floats_tensor((1, self.text_embedder_hidden_size)			,						rng=random.Random(seed + 1)).to(
         _a)

     # create hint
     __snake_case    :							Union[str, Any]   	=				floats_tensor((1, 3, 64, 64)			,						rng=random.Random(_a)).to(_a)

     if str(_a).startswith('mps'):
      __snake_case    :							Optional[Any]   	=				torch.manual_seed(_a)
     else:
      __snake_case    :							List[Any]   	=				torch.Generator(device=_a).manual_seed(_a)
     __snake_case    :							Union[str, Any]   	=				{
         """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       :   Tuple)					->			int:
     __snake_case    :							Optional[Any]   	=				"""cpu"""

     __snake_case    :							Union[str, Any]   	=				self.get_dummy_components()

     __snake_case    :							str   	=				self.pipeline_class(**_a)
     __snake_case    :							Optional[int]   	=				pipe.to(_a)

     pipe.set_progress_bar_config(disable=_a)

     __snake_case    :							Union[str, Any]   	=				pipe(**self.get_dummy_inputs(_a))
     __snake_case    :							List[Any]   	=				output.images

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

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

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

     __snake_case    :							Dict   	=				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 	UpperCamelCase    (					unittest.TestCase		):



    def       _lowercase  (self       :   Optional[int])					->			Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






    def       _lowercase  (self       :   str)					->			Any:
     __snake_case    :							Optional[Any]   	=				load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy')

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

     __snake_case    :							Tuple   	=				KandinskyVaaPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-prior'			,						torch_dtype=torch.floataa)
     pipe_prior.to(_a)

     __snake_case    :							Optional[Any]   	=				KandinskyVaaControlnetPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-controlnet-depth'			,						torch_dtype=torch.floataa)
     __snake_case    :							Optional[int]   	=				pipeline.to(_a)
     pipeline.set_progress_bar_config(disable=_a)

     __snake_case    :							List[str]   	=				"""A robot, 4k photo"""

     __snake_case    :							Union[str, Any]   	=				torch.Generator(device='cuda').manual_seed(0)
     __snake_case    :							Union[str, Any]   	=				pipe_prior(
         _a			,						generator=_a			,						num_inference_steps=5			,						negative_prompt=''			,						).to_tuple()

     __snake_case    :							Tuple   	=				torch.Generator(device='cuda').manual_seed(0)
     __snake_case    :							str   	=				pipeline(
         image_embeds=_a			,						negative_image_embeds=_a			,						hint=_a			,						generator=_a			,						num_inference_steps=1_00			,						output_type='np'			,						)

     __snake_case    :							Optional[Any]   	=				output.images[0]

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

     assert_mean_pixel_difference(_a			,						_a)






"""simple docstring"""







import math
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				len(UpperCAmelCase_						)
	__snake_case    :							List[Any]   	=				int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
	__snake_case    :							Any   	=				0
	while arr[min(UpperCAmelCase_    ,							UpperCAmelCase_						) - 1] < x:
		__snake_case    :							Tuple   	=				step
		step += int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
		if prev >= n:
			return -1

	while arr[prev] < x:
		__snake_case    :							Union[str, Any]   	=				prev + 1
		if prev == min(UpperCAmelCase_    ,							UpperCAmelCase_						):
			return -1
	if arr[prev] == x:
		return prev
	return -1


if __name__ == "__main__":
	_a  :  str=				input("Enter numbers separated by a comma:\n").strip()
	_a  :  int=				[int(item) for item in user_input.split(",")]
	_a  :  Optional[Any]=				int(input("Enter the number to be searched:\n"))
	_a  :  Tuple=				jump_search(arr, x)
	if res == -1:
		print("Number not found!")
	else:
		print(f'''Number {x} is at index {res}''')





