





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Union[str, Any]					=     [tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:])




class        _a    (    UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			unittest.TestCase):

      """simple docstring"""


      UpperCamelCase__  		=       StableDiffusionLatentUpscalePipeline
      UpperCamelCase__  		=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          """height""",
          """width""",
          """cross_attention_kwargs""",
          """negative_prompt_embeds""",
          """prompt_embeds""",
      }
      UpperCamelCase__  		=       PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
      UpperCamelCase__  		=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      UpperCamelCase__  		=       frozenset(
          [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCamelCase__  		=       frozenset([])

      UpperCamelCase__  		=       True






      @property
      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     1
          UpperCamelCase__:	Tuple					=     4
          UpperCamelCase__:	str					=     (16, 16)

          UpperCamelCase__:	Any					=     floats_tensor((batch_size, num_channels) + sizes	,    rng=random.Random(0					)					).to(__lowerCamelCase					)
          return image






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          torch.manual_seed(0					)
          UpperCamelCase__:	Optional[Any]					=     UNetaDConditionModel(
              act_fn="gelu"	,    attention_head_dim=8	,    norm_num_groups=__lowerCamelCase	,    block_out_channels=[32, 32, 64, 64]	,    time_cond_proj_dim=160	,    conv_in_kernel=1	,    conv_out_kernel=1	,    cross_attention_dim=32	,    down_block_types=(
                  "KDownBlock2D",
                  "KCrossAttnDownBlock2D",
                  "KCrossAttnDownBlock2D",
                  "KCrossAttnDownBlock2D",
              )	,    in_channels=8	,    mid_block_type=__lowerCamelCase	,    only_cross_attention=__lowerCamelCase	,    out_channels=5	,    resnet_time_scale_shift="scale_shift"	,    time_embedding_type="fourier"	,    timestep_post_act="gelu"	,    up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")	,    )
          UpperCamelCase__:	Dict					=     AutoencoderKL(
              block_out_channels=[32, 32, 64, 64]	,    in_channels=3	,    out_channels=3	,    down_block_types=[
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
              ]	,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]	,    latent_channels=4	,    )
          UpperCamelCase__:	Optional[Any]					=     EulerDiscreteScheduler(prediction_type="sample"					)
          UpperCamelCase__:	Tuple					=     CLIPTextConfig(
              bos_token_id=0	,    eos_token_id=2	,    hidden_size=32	,    intermediate_size=37	,    layer_norm_eps=1e-05	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1000	,    hidden_act="quick_gelu"	,    projection_dim=512	,    )
          UpperCamelCase__:	Optional[Any]					=     CLIPTextModel(__lowerCamelCase					)
          UpperCamelCase__:	Optional[Any]					=     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

          UpperCamelCase__:	str					=     {
              "unet": model.eval(),
              "vae": vae.eval(),
              "scheduler": scheduler,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
          }

          return components






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Optional[int]=0					):
          '''simple docstring'''


          if str(__lowerCamelCase					).startswith("mps"					):
              UpperCamelCase__:	Tuple					=     torch.manual_seed(__lowerCamelCase					)
          else:
              UpperCamelCase__:	int					=     torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)
          UpperCamelCase__:	Tuple					=     {
              "prompt": "A painting of a squirrel eating a burger",
              "image": self.dummy_image.cpu(),
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }
          return inputs






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	str					=     "cpu"

          UpperCamelCase__:	Any					=     self.get_dummy_components()
          UpperCamelCase__:	Tuple					=     self.pipeline_class(**__lowerCamelCase					)
          pipe.to(__lowerCamelCase					)
          pipe.set_progress_bar_config(disable=__lowerCamelCase					)

          UpperCamelCase__:	List[Any]					=     self.get_dummy_inputs(__lowerCamelCase					)
          UpperCamelCase__:	Any					=     pipe(**__lowerCamelCase					).images
          UpperCamelCase__:	Dict					=     image[0, -3:, -3:, -1]

          self.assertEqual(image.shape	,    (1, 256, 256, 3)					)
          UpperCamelCase__:	Union[str, Any]					=     np.array(
              [0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055]					)
          UpperCamelCase__:	int					=     np.abs(image_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(__lowerCamelCase	,    1e-3					)






      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          super().test_attention_slicing_forward_pass(expected_max_diff=7e-3					)






      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          super().test_cpu_offload_forward_pass(expected_max_diff=3e-3					)






      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


          super().test_inference_batch_single_identical(expected_max_diff=7e-3					)






      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3					)






      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          super().test_save_load_local(expected_max_difference=3e-3					)






      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          super().test_save_load_optional_components(expected_max_difference=3e-3					)






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     [
              "DDIMScheduler",
              "DDPMScheduler",
              "PNDMScheduler",
              "HeunDiscreteScheduler",
              "EulerAncestralDiscreteScheduler",
              "KDPM2DiscreteScheduler",
              "KDPM2AncestralDiscreteScheduler",
              "DPMSolverSDEScheduler",
          ]
          UpperCamelCase__:	int					=     self.get_dummy_components()
          UpperCamelCase__:	Optional[Any]					=     self.pipeline_class(**__lowerCamelCase					)

          # make sure that PNDM does not need warm-up
          pipe.scheduler.register_to_config(skip_prk_steps=__lowerCamelCase					)

          pipe.to(__lowerCamelCase					)
          pipe.set_progress_bar_config(disable=__lowerCamelCase					)
          UpperCamelCase__:	Tuple					=     self.get_dummy_inputs(__lowerCamelCase					)
          UpperCamelCase__:	int					=     2

          UpperCamelCase__:	Dict					=     []
          for scheduler_enum in KarrasDiffusionSchedulers:
              if scheduler_enum.name in skip_schedulers:
                  # no sigma schedulers are not supported
                  # no schedulers
                  continue

              UpperCamelCase__:	Optional[int]					=     getattr(__lowerCamelCase	,    scheduler_enum.name					)
              UpperCamelCase__:	Optional[int]					=     scheduler_cls.from_config(pipe.scheduler.config					)
              UpperCamelCase__:	Dict					=     pipe(**__lowerCamelCase					)[0]
              outputs.append(__lowerCamelCase					)

          assert check_same_shape(__lowerCamelCase					)




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

      """simple docstring"""


      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     torch.manual_seed(33					)

          UpperCamelCase__:	Dict					=     StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	,    torch_dtype=torch.floataa					)
          pipe.to("cuda"					)

          UpperCamelCase__:	Optional[Any]					=     StableDiffusionLatentUpscalePipeline.from_pretrained(
              "stabilityai/sd-x2-latent-upscaler"	,    torch_dtype=torch.floataa					)
          upscaler.to("cuda"					)

          UpperCamelCase__:	Union[str, Any]					=     "a photo of an astronaut high resolution, unreal engine, ultra realistic"

          UpperCamelCase__:	Any					=     pipe(__lowerCamelCase	,    generator=__lowerCamelCase	,    output_type="latent"					).images

          UpperCamelCase__:	Optional[Any]					=     upscaler(
              prompt=__lowerCamelCase	,    image=__lowerCamelCase	,    num_inference_steps=20	,    guidance_scale=0	,    generator=__lowerCamelCase	,    output_type="np"	,    ).images[0]

          UpperCamelCase__:	List[str]					=     load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"					)
          assert np.abs((expected_image - image).mean()					) < 5e-2






      def 			UpperCAmelCase_       (		self:			str					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     torch.manual_seed(33					)

          UpperCamelCase__:	int					=     StableDiffusionLatentUpscalePipeline.from_pretrained(
              "stabilityai/sd-x2-latent-upscaler"	,    torch_dtype=torch.floataa					)
          upscaler.to("cuda"					)

          UpperCamelCase__:	Any					=     "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

          UpperCamelCase__:	Union[str, Any]					=     load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"					)

          UpperCamelCase__:	Tuple					=     upscaler(
              prompt=__lowerCamelCase	,    image=__lowerCamelCase	,    num_inference_steps=20	,    guidance_scale=0	,    generator=__lowerCamelCase	,    output_type="np"	,    ).images[0]

          UpperCamelCase__:	Tuple					=     load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"					)
          assert np.abs((expected_image - image).max()					) < 5e-2








import math



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Dict					=     len(A_)
    UpperCamelCase__:	Optional[Any]					=     int(math.floor(math.sqrt(A_)))
    UpperCamelCase__:	Union[str, Any]					=     0
    while arr[min(A_    ,A_) - 1] < x:
        UpperCamelCase__:	Any					=     step
        step += int(math.floor(math.sqrt(A_)))
        if prev >= n:
            return -1

    while arr[prev] < x:
        UpperCamelCase__:	Dict					=     prev + 1
        if prev == min(A_    ,A_):
            return -1
    if arr[prev] == x:
        return prev
    return -1


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

