
SCREAMING_SNAKE_CASE				:Optional[Any]									=							range(2, 20 + 1)
SCREAMING_SNAKE_CASE				:Optional[int]									=							[10**k for k in range(ks[-1] + 1)]
SCREAMING_SNAKE_CASE				:dict[int, dict[int, list[list[int]]]]									=							{}


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



				__A       =					sum(a_i[j] for j in range(a_						,  len(a_		)		)		)
				__A       =					sum(a_i[j] * base[j] for j in range(min(len(a_		)						,  a_		)		)		)

				__A     ,				__A       =					0, 0
				__A       =					n - i

				__A       =					memo.get(a_		)

				if sub_memo is not None:
								__A       =					sub_memo.get(a_		)

								if jumps is not None and len(a_		) > 0:
												# find and make the largest jump without going over
												__A       =					-1
												for _k in range(len(a_		) - 1						,  -1						,  -1		):
																if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																				__A       =					_k
																				break

												if max_jump >= 0:
																__A     ,				__A     ,				__A       =					jumps[max_jump]
																# since the difference between jumps is cached, add c
																__A       =					diff + c
																for j in range(min(a_						,  len(a_		)		)		):
																				__A     ,				__A       =					divmod(a_						,  1_0		)
																if new_c > 0:
																				add(a_						,  a_						,  a_		)

								else:
												__A       =					[]
				else:
								__A       =					{c: []}
								__A       =					sub_memo

				if dn >= max_dn or c + diff >= base[k]:
								return diff, dn

				if k > ks[0]:
								while True:
												# keep doing smaller jumps
												__A     ,				__A       =					next_term(a_						,  k - 1						,  i + dn						,  a_		)
												diff += _diff
												dn += terms_jumped

												if dn >= max_dn or c + diff >= base[k]:
																break
				else:
								# would be too small a jump, just compute sequential terms instead
								__A     ,				__A       =					compute(a_						,  a_						,  i + dn						,  a_		)
								diff += _diff
								dn += terms_jumped

				__A       =					sub_memo[c]

				# keep jumps sorted by # of terms skipped
				__A       =					0
				while j < len(a_		):
								if jumps[j][1] > dn:
												break
								j += 1

				# cache the jump for this value digitsum(b) and c
				sub_memo[c].insert(a_						,  (diff, dn, k)		)
				return (diff, dn)


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



				if i >= n:
								return 0, i
				if k > len(a_		):
								a_i.extend([0 for _ in range(k - len(a_		)		)]		)

				# note: a_i -> b * 10^k + c
				# ds_b -> digitsum(b)
				# ds_c -> digitsum(c)
				__A       =					i
				__A     ,				__A     ,				__A       =					0, 0, 0
				for j in range(len(a_		)		):
								if j >= k:
												ds_b += a_i[j]
								else:
												ds_c += a_i[j]

				while i < n:
								i += 1
								__A       =					ds_c + ds_b
								diff += addend
								__A       =					0
								for j in range(a_		):
												__A       =					a_i[j] + addend
												__A     ,				__A       =					divmod(a_						,  1_0		)

												ds_c += a_i[j]

								if addend > 0:
												break

				if addend > 0:
								add(a_						,  a_						,  a_		)
				return diff, i - start_i


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Any:







				"""simple docstring"""



				for j in range(a_						,  len(a_		)		):
								__A       =					digits[j] + addend
								if s >= 1_0:
												__A     ,				__A       =					divmod(a_						,  1_0		)
												__A       =					addend // 1_0 + quotient
								else:
												__A       =					s
												__A       =					addend // 1_0

								if addend == 0:
												break

				while addend > 0:
								__A     ,				__A       =					divmod(a_						,  1_0		)
								digits.append(a_		)


def 		UpperCAmelCase     (      a_ = 1_0**1_5		)						->      int:







				"""simple docstring"""



				__A       =					[1]
				__A       =					1
				__A       =					0
				while True:
								__A     ,				__A       =					next_term(a_						,  2_0						,  i + dn						,  a_		)
								dn += terms_jumped
								if dn == n - i:
												break

				__A       =					0
				for j in range(len(a_		)		):
								a_n += digits[j] * 1_0**j
				return a_n


if __name__ == "__main__":
					print(f'''{solution() = }''')







"""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    _lowerCAmelCase  (	a    ,  a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   StableUnCLIPPipeline
       __magic_name__	:int						    =   TEXT_TO_IMAGE_PARAMS
       __magic_name__	:List[str]						    =   TEXT_TO_IMAGE_BATCH_PARAMS
       __magic_name__	:Optional[int]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS
       __magic_name__	:Union[str, Any]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				3_2
         lowerCAmelCase__     :List[Any]											=				embedder_hidden_size

         # prior components

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

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=__UpperCAmelCase       ,					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__     :int											=				PriorTransformer(
             num_attention_heads=2       ,					attention_head_dim=1_2       ,					embedding_dim=__UpperCAmelCase       ,					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=__UpperCAmelCase       ,					clip_sample_range=5.0       ,					beta_schedule='squaredcos_cap_v2'       ,					)

         # regular denoising components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Dict											=				StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				DDPMScheduler(beta_schedule='squaredcos_cap_v2' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :List[Any]											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					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__     :Optional[int]											=				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=__UpperCAmelCase       ,					layers_per_block=1       ,					upcast_attention=__UpperCAmelCase       ,					use_linear_projection=__UpperCAmelCase       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Tuple											=				DDIMScheduler(
             beta_schedule='scaled_linear'       ,					beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					prediction_type='v_prediction'       ,					set_alpha_to_one=__UpperCAmelCase       ,					steps_offset=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				AutoencoderKL()

         lowerCAmelCase__     :Optional[int]											=				{
             # 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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :str											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Optional[int]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				{
             '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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				torch_device in ['cpu', 'mps']

         self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase )






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




       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				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__     :List[Any]											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         # 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__     :List[str]											=				pipe('anime turle'       ,					generator=__UpperCAmelCase       ,					output_type='np' )

         lowerCAmelCase__     :Union[str, Any]											=				output.images[0]

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

         assert_mean_pixel_difference(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

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

         lowerCAmelCase__     :int											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         lowerCAmelCase__     :List[str]											=				pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :Tuple											=				pipe(
             'anime turtle'       ,					prior_num_inference_steps=2       ,					num_inference_steps=2       ,					output_type='np'       ,					)

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