




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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()






class      A     (						_lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		int       =StableDiffusionXLPipeline
     _snake_case:		List[str]       =TEXT_TO_IMAGE_PARAMS
     _snake_case:		List[Any]       =TEXT_TO_IMAGE_BATCH_PARAMS
     _snake_case:		str       =TEXT_TO_IMAGE_IMAGE_PARAMS
     _snake_case:		Tuple       =TEXT_TO_IMAGE_IMAGE_PARAMS




     def 	__snake_case				(      self					:				List[str]     )			->    int:
           torch.manual_seed(0     )
           lowerCamelCase__ 					=			UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)		,					layers_per_block=2		,					sample_size=3_2		,					in_channels=4		,					out_channels=4		,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,					attention_head_dim=(2, 4)		,					use_linear_projection=A__		,					addition_embed_type='text_time'		,					addition_time_embed_dim=8		,					transformer_layers_per_block=(1, 2)		,					projection_class_embeddings_input_dim=8_0		,					cross_attention_dim=6_4		,					)
           lowerCamelCase__ 					=			EulerDiscreteScheduler(
               beta_start=0.00085		,					beta_end=0.012		,					steps_offset=1		,					beta_schedule='scaled_linear'		,					timestep_spacing='leading'		,					)
           torch.manual_seed(0     )
           lowerCamelCase__ 					=			AutoencoderKL(
               block_out_channels=[3_2, 6_4]		,					in_channels=3		,					out_channels=3		,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,					latent_channels=4		,					sample_size=1_2_8		,					)
           torch.manual_seed(0     )
           lowerCamelCase__ 					=			CLIPTextConfig(
               bos_token_id=0		,					eos_token_id=2		,					hidden_size=3_2		,					intermediate_size=3_7		,					layer_norm_eps=1E-05		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=1_0_0_0		,					hidden_act='gelu'		,					projection_dim=3_2		,					)
           lowerCamelCase__ 					=			CLIPTextModel(A__     )
           lowerCamelCase__ 					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		,					local_files_only=A__     )

           lowerCamelCase__ 					=			CLIPTextModelWithProjection(A__     )
           lowerCamelCase__ 					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		,					local_files_only=A__     )

           lowerCamelCase__ 					=			{
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'text_encoder_2': text_encoder_a,
               'tokenizer_2': tokenizer_a,
               # "safety_checker": None,
               # "feature_extractor": None,
           }
           return components




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				List[Any]		,					A__					:				Optional[Any]=0     )			->    Tuple:
           if str(A__     ).startswith('mps'     ):
                 lowerCamelCase__ 					=			torch.manual_seed(A__     )
           else:
                 lowerCamelCase__ 					=			torch.Generator(device=A__     ).manual_seed(A__     )
           lowerCamelCase__ 					=			{
               'prompt': 'A painting of a squirrel eating a burger',
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 5.0,
               'output_type': 'numpy',
           }
           return inputs




     def 	__snake_case				(      self					:				int     )			->    Any:
           lowerCamelCase__ 					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase__ 					=			self.get_dummy_components()
           lowerCamelCase__ 					=			StableDiffusionXLPipeline(**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, 6_4, 6_4, 3)
           lowerCamelCase__ 					=			np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]     )

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




     def 	__snake_case				(      self					:				Optional[int]     )			->    Dict:
           lowerCamelCase__ 					=			self.get_dummy_components()
           lowerCamelCase__ 					=			StableDiffusionXLPipeline(**A__     )
           lowerCamelCase__ 					=			sd_pipe.to(A__     )
           lowerCamelCase__ 					=			sd_pipe.to(A__     )
           sd_pipe.set_progress_bar_config(disable=A__     )

           # forward without prompt embeds
           lowerCamelCase__ 					=			self.get_dummy_inputs(A__     )
           lowerCamelCase__ 					=			3 * ['this is a negative prompt']
           lowerCamelCase__ 					=			negative_prompt
           lowerCamelCase__ 					=			3 * [inputs['prompt']]

           lowerCamelCase__ 					=			sd_pipe(**A__     )
           lowerCamelCase__ 					=			output.images[0, -3:, -3:, -1]

           # forward with prompt embeds
           lowerCamelCase__ 					=			self.get_dummy_inputs(A__     )
           lowerCamelCase__ 					=			3 * ['this is a negative prompt']
           lowerCamelCase__ 					=			3 * [inputs.pop('prompt'     )]

           (
               (
               lowerCamelCase__						
           )					,						(
               lowerCamelCase__						
           )					,						(
               lowerCamelCase__						
           )					,						(
               lowerCamelCase__						
           )					,												
           ) 					=			sd_pipe.encode_prompt(A__		,					negative_prompt=A__     )

           lowerCamelCase__ 					=			sd_pipe(
               **A__		,					prompt_embeds=A__		,					negative_prompt_embeds=A__		,					pooled_prompt_embeds=A__		,					negative_pooled_prompt_embeds=A__		,					)
           lowerCamelCase__ 					=			output.images[0, -3:, -3:, -1]

           # make sure that it's equal
           assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()     ).max() < 1E-4




     def 	__snake_case				(      self					:				str     )			->    Dict:
           super().test_attention_slicing_forward_pass(expected_max_diff=3E-3     )




     def 	__snake_case				(      self					:				Tuple     )			->    List[str]:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3     )






@slow
@require_torch_gpu
class      A     (						unittest.TestCase						):
     """simple docstring"""

     def 	__snake_case				(      self					:				Optional[Any]     )			->    List[Any]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




     def 	__snake_case				(      self					:				int		,					A__					:				Optional[Any]		,					A__					:				List[str]="cpu"		,					A__					:				Any=torch.floataa		,					A__					:				int=0     )			->    List[str]:
           lowerCamelCase__ 					=			torch.Generator(device=A__     ).manual_seed(A__     )
           lowerCamelCase__ 					=			np.random.RandomState(A__     ).standard_normal((1, 4, 6_4, 6_4)     )
           lowerCamelCase__ 					=			torch.from_numpy(A__     ).to(device=A__		,					dtype=A__     )
           lowerCamelCase__ 					=			{
               'prompt': 'a photograph of an astronaut riding a horse',
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs




     def 	__snake_case				(      self					:				Optional[int]     )			->    Tuple:
           lowerCamelCase__ 					=			DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'     )
           pipe.to(A__     )
           pipe.set_progress_bar_config(disable=A__     )

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

           assert image.shape == (1, 5_1_2, 5_1_2, 3)
           lowerCamelCase__ 					=			np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]     )
           assert np.abs(image_slice - expected_slice     ).max() < 7E-3





def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if right <= left:
            return
      lowerCamelCase__ 					=			lowerCamelCase__ 					=			left
      lowerCamelCase__ 					=			right
      lowerCamelCase__ 					=			sorting[left]
      while i <= b:
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[a]
                  a += 1
                  i += 1
            elif sorting[i] > pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[b]
                  b -= 1
            else:
                  i += 1
      quick_sort_apartition(lowerCamelCase       ,			lowerCamelCase       ,			a - 1  )
      quick_sort_apartition(lowerCamelCase       ,			b + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if left < right:
            lowerCamelCase__ 					=			lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			pivot_index - 1  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			pivot_index + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			sorting[right]
      lowerCamelCase__ 					=			left
      for i in range(lowerCamelCase       ,			lowerCamelCase  ):
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[store_index]
                  store_index += 1
      lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[store_index], sorting[right]
      return store_index

def      lowerCAmelCase_     (					lowerCamelCase:    list  )   ->     list:


      '''simple docstring'''




      if len(lowerCamelCase  ) <= 1:
            return sorting
      return (
          three_way_radix_quicksort([i for i in sorting if i < sorting[0]]  )
          + [i for i in sorting if i == sorting[0]]
          + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]  )
      )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)

      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =input("""Enter numbers separated by a comma:\n""").strip()
      __SCREAMING_SNAKE_CASE      :				str				      =[int(item) for item in user_input.split(""",""")]
      quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
      print(unsorted)