


'''simple docstring'''







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    StableDiffusionLDMaDPipeline
     __magic_name__    			=    TEXT_TO_IMAGE_PARAMS
     __magic_name__    			=    TEXT_TO_IMAGE_BATCH_PARAMS
     __magic_name__    			=    TEXT_TO_IMAGE_IMAGE_PARAMS







     def a	( self   ):

            '''simple docstring'''

            torch.manual_seed(0   )
            _lowerCAmelCase    :							Dict   			=       UNetaDConditionModel(
                block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=4      ,	out_channels=4      ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,	cross_attention_dim=32      ,	)
            _lowerCAmelCase    :							Union[str, Any]   			=       DDIMScheduler(
                beta_start=0.0_0085      ,	beta_end=0.012      ,	beta_schedule='scaled_linear'      ,	clip_sample=snake_case__      ,	set_alpha_to_one=snake_case__      ,	)
            torch.manual_seed(0   )
            _lowerCAmelCase    :							Union[str, Any]   			=       AutoencoderKL(
                block_out_channels=[32, 64]      ,	in_channels=6      ,	out_channels=6      ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,	latent_channels=4      ,	)
            torch.manual_seed(0   )
            _lowerCAmelCase    :							List[Any]   			=       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      ,	)
            _lowerCAmelCase    :							Any   			=       CLIPTextModel(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

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







     def a	( self      ,	snake_case__      ,	snake_case__=0   ):

            '''simple docstring'''

            if str(snake_case__   ).startswith('mps'   ):
                   _lowerCAmelCase    :							Dict   			=       torch.manual_seed(snake_case__   )
            else:
                   _lowerCAmelCase    :							Union[str, Any]   			=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       {
                'prompt': 'A painting of a squirrel eating a burger',
                'generator': generator,
                'num_inference_steps': 2,
                'guidance_scale': 6.0,
                'output_type': 'numpy',
            }
            return inputs







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       'cpu'  # ensure determinism for the device-dependent torch.Generator

            _lowerCAmelCase    :							List[str]   			=       self.get_dummy_components()
            _lowerCAmelCase    :							Optional[int]   			=       StableDiffusionLDMaDPipeline(**snake_case__   )
            _lowerCAmelCase    :							int   			=       ldmad_pipe.to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							str   			=       self.get_dummy_inputs(snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase    :							Any   			=       output.rgb, output.depth

            _lowerCAmelCase    :							Dict   			=       rgb[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Dict   			=       depth[0, -3:, -1]

            assert rgb.shape == (1, 64, 64, 3)
            assert depth.shape == (1, 64, 64)

            _lowerCAmelCase    :							Optional[int]   			=       np.array(
                [0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262]   )
            _lowerCAmelCase    :							Tuple   			=       np.array([103.4_6727, 85.81_2004, 87.84_9236]   )

            assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb   ).max() < 1E-2
            assert np.abs(image_slice_depth.flatten() - expected_slice_depth   ).max() < 1E-2







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.get_dummy_components()
            _lowerCAmelCase    :							Tuple   			=       StableDiffusionLDMaDPipeline(**snake_case__   )
            _lowerCAmelCase    :							int   			=       ldmad_pipe.to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       self.get_dummy_inputs(snake_case__   )
            _lowerCAmelCase    :							str   			=       3 * [inputs['prompt']]

            # forward
            _lowerCAmelCase    :							List[str]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase    :							Any   			=       output.rgb, output.depth
            _lowerCAmelCase    :							str   			=       rgb_slice_a[0, -3:, -3:, -1]
            _lowerCAmelCase    :							int   			=       depth_slice_a[0, -3:, -1]

            _lowerCAmelCase    :							int   			=       self.get_dummy_inputs(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       3 * [inputs.pop('prompt'   )]

            _lowerCAmelCase    :							Dict   			=       ldmad_pipe.tokenizer(
                snake_case__      ,	padding='max_length'      ,	max_length=ldmad_pipe.tokenizer.model_max_length      ,	truncation=snake_case__      ,	return_tensors='pt'      ,	)
            _lowerCAmelCase    :							int   			=       text_inputs['input_ids'].to(snake_case__   )

            _lowerCAmelCase    :							str   			=       ldmad_pipe.text_encoder(snake_case__   )[0]

            _lowerCAmelCase    :							List[Any]   			=       prompt_embeds

            # forward
            _lowerCAmelCase    :							Optional[int]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase    :							str   			=       output.rgb, output.depth
            _lowerCAmelCase    :							str   			=       rgb_slice_a[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Any   			=       depth_slice_a[0, -3:, -1]

            assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()   ).max() < 1E-4
            assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()   ).max() < 1E-4







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       'cpu'  # ensure determinism for the device-dependent torch.Generator
            _lowerCAmelCase    :							List[Any]   			=       self.get_dummy_components()
            _lowerCAmelCase    :							Optional[Any]   			=       PNDMScheduler(skip_prk_steps=snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       StableDiffusionLDMaDPipeline(**snake_case__   )
            _lowerCAmelCase    :							int   			=       ldmad_pipe.to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							int   			=       self.get_dummy_inputs(snake_case__   )
            _lowerCAmelCase    :							Dict   			=       'french fries'
            _lowerCAmelCase    :							Optional[Any]   			=       ldmad_pipe(**snake_case__      ,	negative_prompt=snake_case__   )

            _lowerCAmelCase    :							Dict   			=       output.rgb, output.depth
            _lowerCAmelCase    :							Union[str, Any]   			=       rgb[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Dict   			=       depth[0, -3:, -1]

            assert rgb.shape == (1, 64, 64, 3)
            assert depth.shape == (1, 64, 64)

            _lowerCAmelCase    :							Optional[Any]   			=       np.array(
                [0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217]   )
            _lowerCAmelCase    :							Dict   			=       np.array([107.8_4738, 84.6_2802, 89.96_2135]   )
            assert np.abs(rgb_slice.flatten() - expected_slice_rgb   ).max() < 1E-2
            assert np.abs(depth_slice.flatten() - expected_slice_depth   ).max() < 1E-2
@slow
@require_torch_gpu
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__="cpu"      ,	snake_case__=torch.floataa      ,	snake_case__=0   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
            _lowerCAmelCase    :							Dict   			=       np.random.RandomState(snake_case__   ).standard_normal((1, 4, 64, 64)   )
            _lowerCAmelCase    :							Any   			=       torch.from_numpy(snake_case__   ).to(device=snake_case__      ,	dtype=snake_case__   )
            _lowerCAmelCase    :							str   			=       {
                '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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'   )
            _lowerCAmelCase    :							List[str]   			=       ldmad_pipe.to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Dict   			=       self.get_inputs(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase    :							str   			=       output.rgb, output.depth
            _lowerCAmelCase    :							int   			=       rgb[0, -3:, -3:, -1].flatten()
            _lowerCAmelCase    :							List[Any]   			=       rgb[0, -3:, -1].flatten()

            assert rgb.shape == (1, 512, 512, 3)
            assert depth.shape == (1, 512, 512)

            _lowerCAmelCase    :							List[str]   			=       np.array(
                [0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706]   )
            _lowerCAmelCase    :							str   			=       np.array(
                [0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706]   )
            assert np.abs(rgb_slice - expected_slice_rgb   ).max() < 3E-3
            assert np.abs(depth_slice - expected_slice_depth   ).max() < 3E-3
@nightly
@require_torch_gpu
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__="cpu"      ,	snake_case__=torch.floataa      ,	snake_case__=0   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       np.random.RandomState(snake_case__   ).standard_normal((1, 4, 64, 64)   )
            _lowerCAmelCase    :							List[Any]   			=       torch.from_numpy(snake_case__   ).to(device=snake_case__      ,	dtype=snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       {
                'prompt': 'a photograph of an astronaut riding a horse',
                'latents': latents,
                'generator': generator,
                'num_inference_steps': 50,
                'guidance_scale': 7.5,
                'output_type': 'numpy',
            }
            return inputs







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'   ).to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       self.get_inputs(snake_case__   )
            _lowerCAmelCase    :							int   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase    :							str   			=       output.rgb, output.depth

            _lowerCAmelCase    :							List[str]   			=       0.49_5586
            _lowerCAmelCase    :							int   			=       0.3379_5515
            _lowerCAmelCase    :							int   			=       112.4_8518
            _lowerCAmelCase    :							Optional[int]   			=       98.48_9746
            assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1E-3
            assert np.abs(expected_rgb_std - rgb.std()   ) < 1E-3
            assert np.abs(expected_depth_mean - depth.mean()   ) < 1E-3
            assert np.abs(expected_depth_std - depth.std()   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'   ).to(snake_case__   )
            ldmad_pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       self.get_inputs(snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       ldmad_pipe(**snake_case__   )
            _lowerCAmelCase    :							List[str]   			=       output.rgb, output.depth

            _lowerCAmelCase    :							str   			=       0.419_4127
            _lowerCAmelCase    :							str   			=       0.3537_5586
            _lowerCAmelCase    :							Optional[int]   			=       0.563_8502
            _lowerCAmelCase    :							List[str]   			=       0.3468_6103

            assert rgb.shape == (1, 512, 512, 3)
            assert depth.shape == (1, 512, 512, 1)
            assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1E-3
            assert np.abs(expected_rgb_std - rgb.std()   ) < 1E-3
            assert np.abs(expected_depth_mean - depth.mean()   ) < 1E-3
            assert np.abs(expected_depth_std - depth.std()   ) < 1E-3






'''simple docstring'''







def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Dict   			=       len(_A					)
       while cur > 1:
              # Find the maximum number in arr
              _lowerCAmelCase    :							int   			=       arr.index(max(arr[0:cur]					)					)
              # Reverse from 0 to mi
              _lowerCAmelCase    :							Dict   			=       arr[mi::-1] + arr[mi + 1 : len(_A					)]
              # Reverse whole list
              _lowerCAmelCase    :							Optional[int]   			=       arr[cur - 1 :: -1] + arr[cur : len(_A					)]
              cur -= 1
       return arr


if __name__ == "__main__":
       lowerCAmelCase    :     Tuple					=						input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase    :     Tuple					=						[int(item) for item in user_input.split(""",""")]
       print(pancake_sort(unsorted))

