'''simple docstring'''




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class     a__      (   snake_case_							,   snake_case_							,   unittest.TestCase ):
     lowerCamelCase  :     List[str]							=StableDiffusionPanoramaPipeline
     lowerCamelCase  :     Optional[int]							=TEXT_TO_IMAGE_PARAMS
     lowerCamelCase  :     Optional[int]							=TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCamelCase  :     Optional[Any]							=TEXT_TO_IMAGE_IMAGE_PARAMS
     lowerCamelCase  :     int							=TEXT_TO_IMAGE_IMAGE_PARAMS






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         torch.manual_seed(0    )
         __lowerCamelCase		=  UNetaDConditionModel(
             block_out_channels=(32, 64)  ,				layers_per_block=1  ,				sample_size=32  ,				in_channels=4  ,				out_channels=4  ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,				cross_attention_dim=32  ,				)
         __lowerCamelCase		=  DDIMScheduler()
         torch.manual_seed(0    )
         __lowerCamelCase		=  AutoencoderKL(
             block_out_channels=[32, 64]  ,				in_channels=3  ,				out_channels=3  ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,				latent_channels=4  ,				)
         torch.manual_seed(0    )
         __lowerCamelCase		=  CLIPTextConfig(
             bos_token_id=0  ,				eos_token_id=2  ,				hidden_size=32  ,				intermediate_size=37  ,				layer_norm_eps=1e-0_5  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				pad_token_id=1  ,				vocab_size=10_00  ,				)
         __lowerCamelCase		=  CLIPTextModel(a    )
         __lowerCamelCase		=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

         __lowerCamelCase		=  {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components






     def    SCREAMING_SNAKE_CASE__   ( self		:		int  ,				a		:		Any  ,				a		:		Dict=0    ):





         """simple docstring"""



         __lowerCamelCase		=  torch.manual_seed(a    )
         __lowerCamelCase		=  {
             '''prompt''': '''a photo of the dolomites''',
             '''generator''': generator,
             # Setting height and width to None to prevent OOMs on CPU.
             '''height''': None,
             '''width''': None,
             '''num_inference_steps''': 1,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  sd_pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase		=  np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         super().test_inference_batch_consistent(batch_sizes=[1, 2]    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         super().test_inference_batch_single_identical(batch_size=2  ,				expected_max_diff=3.2_5e-3    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  '''french fries'''
         __lowerCamelCase		=  sd_pipe(**a  ,				negative_prompt=a    )
         __lowerCamelCase		=  output.images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

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

         __lowerCamelCase		=  np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  sd_pipe(**a  ,				view_batch_size=2    )
         __lowerCamelCase		=  output.images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

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

         __lowerCamelCase		=  np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				beta_schedule='''scaled_linear'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  sd_pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

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

         __lowerCamelCase		=  np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase		=  self.get_dummy_components()
         __lowerCamelCase		=  PNDMScheduler(
             beta_start=0.0_00_85  ,				beta_end=0.0_12  ,				beta_schedule='''scaled_linear'''  ,				skip_prk_steps=a    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline(**a    )
         __lowerCamelCase		=  sd_pipe.to(a    )
         sd_pipe.set_progress_bar_config(disable=a    )

         __lowerCamelCase		=  self.get_dummy_inputs(a    )
         __lowerCamelCase		=  sd_pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1]

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

         __lowerCamelCase		=  np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				a		:		Optional[int]=0    ):





         """simple docstring"""



         __lowerCamelCase		=  torch.manual_seed(a    )
         __lowerCamelCase		=  {
             '''prompt''': '''a photo of the dolomites''',
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs






     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         __lowerCamelCase		=  '''stabilityai/stable-diffusion-2-base'''
         __lowerCamelCase		=  DDIMScheduler.from_pretrained(a  ,				subfolder='''scheduler'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(a  ,				scheduler=a  ,				safety_checker=a    )
         pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  self.get_inputs()
         __lowerCamelCase		=  pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 20_48, 3)

         __lowerCamelCase		=  np.array(
             [
                 0.36_96_83_92,
                 0.27_02_53_72,
                 0.32_44_67_66,
                 0.28_37_93_87,
                 0.36_36_32_74,
                 0.30_73_33_47,
                 0.27_10_00_27,
                 0.27_05_41_25,
                 0.25_53_60_96,
             ]    )

         assert np.abs(expected_slice - image_slice    ).max() < 1e-2






     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-base'''  ,				safety_checker=a    )
         __lowerCamelCase		=  LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  self.get_inputs()
         __lowerCamelCase		=  pipe(**a    ).images
         __lowerCamelCase		=  image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 20_48, 3)

         __lowerCamelCase		=  np.array(
             [
                 [
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                 ]
             ]    )

         assert np.abs(expected_slice - image_slice    ).max() < 1e-3






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  0

         def callback_fn(a		:		List[str]  ,				a		:		Union[str, Any]  ,				a		:		int    ) -> None:
             __lowerCamelCase		=  True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                 __lowerCamelCase		=  latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 2_56)
                 __lowerCamelCase		=  latents[0, -3:, -3:, -1]

                 __lowerCamelCase		=  np.array(
                     [
                         0.18_68_18_69,
                         0.33_90_78_16,
                         0.5_36_12_76,
                         0.14_43_28_65,
                         -0.02_85_66_11,
                         -0.73_94_11_23,
                         0.23_39_79_87,
                         0.47_32_26_82,
                         -0.37_82_31_64,
                     ]    )
                 assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2
             elif step == 2:
                 __lowerCamelCase		=  latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 2_56)
                 __lowerCamelCase		=  latents[0, -3:, -3:, -1]

                 __lowerCamelCase		=  np.array(
                     [
                         0.18_53_96_45,
                         0.33_98_72_48,
                         0.5_37_85_59,
                         0.14_43_71_42,
                         -0.02_45_52_61,
                         -0.7_33_83_17,
                         0.23_99_07_55,
                         0.47_35_62_72,
                         -0.3_78_65_05,
                     ]    )

                 assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2

         __lowerCamelCase		=  False

         __lowerCamelCase		=  '''stabilityai/stable-diffusion-2-base'''
         __lowerCamelCase		=  DDIMScheduler.from_pretrained(a  ,				subfolder='''scheduler'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(a  ,				scheduler=a  ,				safety_checker=a    )
         __lowerCamelCase		=  pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing()

         __lowerCamelCase		=  self.get_inputs()
         pipe(**a  ,				callback=a  ,				callback_steps=1    )
         assert callback_fn.has_been_called
         assert number_of_steps == 3






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase		=  '''stabilityai/stable-diffusion-2-base'''
         __lowerCamelCase		=  DDIMScheduler.from_pretrained(a  ,				subfolder='''scheduler'''    )
         __lowerCamelCase		=  StableDiffusionPanoramaPipeline.from_pretrained(a  ,				scheduler=a  ,				safety_checker=a    )
         __lowerCamelCase		=  pipe.to(a    )
         pipe.set_progress_bar_config(disable=a    )
         pipe.enable_attention_slicing(1    )
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase		=  self.get_inputs()
         __lowerCamelCase		=  pipe(**a    )

         __lowerCamelCase		=  torch.cuda.max_memory_allocated()
         # make sure that less than 5.2 GB is allocated
         assert mem_bytes < 5.5 * 10**9


import math





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 0	, lowerCamelCase__ = 0    ):
				lowerCamelCase_						=			end or len(lowerCamelCase__    )
				for i in range(lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			i
								lowerCamelCase_						=			array[i]
								while temp_index != start and temp_index_value < array[temp_index - 1]:
												lowerCamelCase_						=			array[temp_index - 1]
												temp_index -= 1
								lowerCamelCase_						=			temp_index_value
				return array





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):  # Max Heap
				lowerCamelCase_						=			index
				lowerCamelCase_						=			2 * index + 1  # Left Node
				lowerCamelCase_						=			2 * index + 2  # Right Node

				if left_index < heap_size and array[largest] < array[left_index]:
								lowerCamelCase_						=			left_index

				if right_index < heap_size and array[largest] < array[right_index]:
								lowerCamelCase_						=			right_index

				if largest != index:
								lowerCamelCase_	,    lowerCamelCase_						=			array[largest], array[index]
								heapify(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )

				for i in range(n // 2	, -1	, -1    ):
								heapify(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				for i in range(n - 1	, 0	, -1    ):
								lowerCamelCase_	,    lowerCamelCase_						=			array[0], array[i]
								heapify(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return array





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				if (array[first_index] > array[middle_index]) != (
				    array[first_index] > array[last_index]
				):
								return array[first_index]
				elif (array[middle_index] > array[first_index]) != (
				    array[middle_index] > array[last_index]
				):
								return array[middle_index]
				else:
								return array[last_index]





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			low
				lowerCamelCase_						=			high
				while True:
								while array[i] < pivot:
												i += 1
								j -= 1
								while pivot < array[j]:
												j -= 1
								if i >= j:
												return i
								lowerCamelCase_	,    lowerCamelCase_						=			array[j], array[i]
								i += 1





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if len(lowerCamelCase__    ) == 0:
								return array
				lowerCamelCase_						=			2 * math.ceil(math.loga(len(lowerCamelCase__    )    )    )
				lowerCamelCase_						=			1_6
				return intro_sort(lowerCamelCase__	, 0	, len(lowerCamelCase__    )	, lowerCamelCase__	, lowerCamelCase__    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				while end - start > size_threshold:
								if max_depth == 0:
												return heap_sort(lowerCamelCase__    )
								max_depth -= 1
								lowerCamelCase_						=			median_of_a(lowerCamelCase__	, lowerCamelCase__	, start + ((end - start) // 2) + 1	, end - 1    )
								lowerCamelCase_						=			partition(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
								intro_sort(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
								lowerCamelCase_						=			p
				return insertion_sort(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__A				=input('''Enter numbers separated by a comma : ''').strip()
			__A				=[float(item) for item in user_input.split(''',''')]
			print(sort(unsorted))


