



"""simple docstring"""






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 			__snake_case			(      _lowercase    ,	_lowercase    ,	_lowercase    ,	unittest.TestCase):
  snake_case__							:  str         =		StableUnCLIPPipeline
  snake_case__							:  Optional[int]         =		TEXT_TO_IMAGE_PARAMS
  snake_case__							:  int         =		TEXT_TO_IMAGE_BATCH_PARAMS
  snake_case__							:  List[Any]         =		TEXT_TO_IMAGE_IMAGE_PARAMS
  snake_case__							:  Optional[Any]         =		TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  snake_case__							:  Dict         =		False




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         _lowerCamelCase		:							int            =					3_2
         _lowerCamelCase		:							Any            =					embedder_hidden_size

         # prior components

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

         torch.manual_seed(0						)
         _lowerCamelCase		:							List[Any]            =					CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0		,   eos_token_id=2		,   hidden_size=__lowerCAmelCase		,   projection_dim=__lowerCAmelCase		,   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		,   )						)

         torch.manual_seed(0						)
         _lowerCamelCase		:							Dict            =					PriorTransformer(
             num_attention_heads=2		,   attention_head_dim=1_2		,   embedding_dim=__lowerCAmelCase		,   num_layers=1		,   )

         torch.manual_seed(0						)
         _lowerCamelCase		:							Optional[int]            =					DDPMScheduler(
             variance_type='''fixed_small_log'''		,   prediction_type='''sample'''		,   num_train_timesteps=1_0_0_0		,   clip_sample=__lowerCAmelCase		,   clip_sample_range=5.0		,   beta_schedule='''squaredcos_cap_v2'''		,   )

         # regular denoising components

         torch.manual_seed(0						)
         _lowerCamelCase		:							int            =					StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase						)
         _lowerCamelCase		:							List[str]            =					DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''						)

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

         torch.manual_seed(0						)
         _lowerCamelCase		:							Optional[Any]            =					CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0		,   eos_token_id=2		,   hidden_size=__lowerCAmelCase		,   projection_dim=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		,   )						)

         torch.manual_seed(0						)
         _lowerCamelCase		:							str            =					UNetaDConditionModel(
             sample_size=3_2		,   in_channels=4		,   out_channels=4		,   down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')		,   up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')		,   block_out_channels=(3_2, 6_4)		,   attention_head_dim=(2, 4)		,   class_embed_type='''projection'''		,   projection_class_embeddings_input_dim=embedder_projection_dim * 2		,   cross_attention_dim=__lowerCAmelCase		,   layers_per_block=1		,   upcast_attention=__lowerCAmelCase		,   use_linear_projection=__lowerCAmelCase		,   )

         torch.manual_seed(0						)
         _lowerCamelCase		:							Optional[Any]            =					DDIMScheduler(
             beta_schedule='''scaled_linear'''		,   beta_start=0.0_00_85		,   beta_end=0.0_12		,   prediction_type='''v_prediction'''		,   set_alpha_to_one=__lowerCAmelCase		,   steps_offset=1		,   )

         torch.manual_seed(0						)
         _lowerCamelCase		:							Optional[Any]            =					AutoencoderKL()

         _lowerCamelCase		:							Dict            =					{
             # 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	:		Any		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		Optional[int]=0						):






         """simple docstring"""






         if str(__lowerCAmelCase						).startswith('''mps'''						):
                _lowerCamelCase		:							List[Any]            =					torch.manual_seed(__lowerCAmelCase						)
         else:
                _lowerCamelCase		:							str            =					torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)
         _lowerCamelCase		:							List[str]            =					{
             '''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	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					torch_device == '''cpu'''

         self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					torch_device in ['''cpu''', '''mps''']

         self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase						)






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''						)

         _lowerCamelCase		:							Optional[Any]            =					StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''		,   torch_dtype=torch.floataa						)
         pipe.to(__lowerCAmelCase						)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase						)
         # 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()

         _lowerCamelCase		:							List[str]            =					torch.Generator(device='''cpu'''						).manual_seed(0						)
         _lowerCamelCase		:							Tuple            =					pipe('''anime turle'''		,   generator=__lowerCAmelCase		,   output_type='''np'''						)

         _lowerCamelCase		:							Dict            =					output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__lowerCAmelCase		,   __lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






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

         _lowerCamelCase		:							Union[str, Any]            =					StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''		,   torch_dtype=torch.floataa						)
         _lowerCamelCase		:							Any            =					pipe.to(__lowerCAmelCase						)
         pipe.set_progress_bar_config(disable=__lowerCAmelCase						)
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         _lowerCamelCase		:							List[str]            =					pipe(
             '''anime turtle'''		,   prior_num_inference_steps=2		,   num_inference_steps=2		,   output_type='''np'''		,   )

         _lowerCamelCase		:							Dict            =					torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9




"""simple docstring"""





def 				A_  (__a					):

					'''simple docstring'''





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


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




