


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class 		__magic_name__						(snake_case_   ,snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       StableUnCLIPPipeline
     __lowercase				:							Any	   =       TEXT_TO_IMAGE_PARAMS
     __lowercase				:							List[str]	   =       TEXT_TO_IMAGE_BATCH_PARAMS
     __lowercase				:							Optional[int]	   =       TEXT_TO_IMAGE_IMAGE_PARAMS
     __lowercase				:							Any	   =       TEXT_TO_IMAGE_IMAGE_PARAMS

     # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
     __lowercase				:							List[Any]	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 32
       snake_case__						= embedder_hidden_size

       # prior components

       torch.manual_seed(0   )
       snake_case__						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       torch.manual_seed(0   )
       snake_case__						= CLIPTextModelWithProjection(
           CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=_a     ,		projection_dim=_a     ,		intermediate_size=37     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=10_00     ,		)   )

       torch.manual_seed(0   )
       snake_case__						= PriorTransformer(
           num_attention_heads=2     ,		attention_head_dim=12     ,		embedding_dim=_a     ,		num_layers=1     ,		)

       torch.manual_seed(0   )
       snake_case__						= DDPMScheduler(
           variance_type='''fixed_small_log'''     ,		prediction_type='''sample'''     ,		num_train_timesteps=10_00     ,		clip_sample=_a     ,		clip_sample_range=5.0     ,		beta_schedule='''squaredcos_cap_v2'''     ,		)

       # regular denoising components

       torch.manual_seed(0   )
       snake_case__						= StableUnCLIPImageNormalizer(embedding_dim=_a   )
       snake_case__						= DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''   )

       torch.manual_seed(0   )
       snake_case__						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       torch.manual_seed(0   )
       snake_case__						= CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=_a     ,		projection_dim=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=10_00     ,		)   )

       torch.manual_seed(0   )
       snake_case__						= UNetaDConditionModel(
           sample_size=32     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')     ,		up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')     ,		block_out_channels=(32, 64)     ,		attention_head_dim=(2, 4)     ,		class_embed_type='''projection'''     ,		projection_class_embeddings_input_dim=embedder_projection_dim * 2     ,		cross_attention_dim=_a     ,		layers_per_block=1     ,		upcast_attention=_a     ,		use_linear_projection=_a     ,		)

       torch.manual_seed(0   )
       snake_case__						= DDIMScheduler(
           beta_schedule='''scaled_linear'''     ,		beta_start=0.00085     ,		beta_end=0.012     ,		prediction_type='''v_prediction'''     ,		set_alpha_to_one=_a     ,		steps_offset=1     ,		)

       torch.manual_seed(0   )
       snake_case__						= AutoencoderKL()

       snake_case__						= {
           # prior components
           '''prior_tokenizer''': prior_tokenizer,
           '''prior_text_encoder''': prior_text_encoder,
           '''prior''': prior,
           '''prior_scheduler''': prior_scheduler,
           # image noising components
           '''image_normalizer''': image_normalizer,
           '''image_noising_scheduler''': image_noising_scheduler,
           # regular denoising components
           '''tokenizer''': tokenizer,
           '''text_encoder''': text_encoder,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
       }

       return components

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:Tuple     ,		_a:Any=0   ):
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )
       snake_case__						= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''prior_num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= torch_device == '''cpu'''

       self._test_attention_slicing_forward_pass(test_max_difference=_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= torch_device in ['''cpu''', '''mps''']

       self._test_inference_batch_single_identical(test_max_difference=_a   )




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





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''   )

       snake_case__						= StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''     ,		torch_dtype=torch.floataa   )
       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       snake_case__						= torch.Generator(device='''cpu'''   ).manual_seed(0   )
       snake_case__						= pipe('''anime turle'''     ,		generator=_a     ,		output_type='''np'''   )

       snake_case__						= output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(_a     ,		_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case__						= StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''     ,		torch_dtype=torch.floataa   )
       snake_case__						= pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       snake_case__						= pipe(
           '''anime turtle'''     ,		prior_num_inference_steps=2     ,		num_inference_steps=2     ,		output_type='''np'''     ,		)

       snake_case__						= torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 10**9





def   _lowercase(					__a				:						list[int]     ):
							a__      =len(__a     )
							for i in range(__a     ):
														for j in range(i + 1    ,     __a     ):
																					if numbers[j] < numbers[i]:
																												a__    ,     a__      =numbers[j], numbers[i]
							return numbers


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

