
'''simple docstring'''





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()

@skip_mps
class     a (					__magic_name__					,__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			StableDiffusionModelEditingPipeline
				_snake_case		     =			TEXT_TO_IMAGE_PARAMS
				_snake_case		     =			TEXT_TO_IMAGE_BATCH_PARAMS
				_snake_case		     =			TEXT_TO_IMAGE_IMAGE_PARAMS
				_snake_case		     =			TEXT_TO_IMAGE_IMAGE_PARAMS




				def 						__snake_case				(				self						:					Any   ):
										torch.manual_seed(0   )
										snake_case							:  List[str]			=     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,     )
										snake_case							:  int			=     DDIMScheduler()
										torch.manual_seed(0   )
										snake_case							:  int			=     AutoencoderKL(
										    block_out_channels=[32, 64],     in_channels=3,     out_channels=3,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],     latent_channels=4,     )
										torch.manual_seed(0   )
										snake_case							:  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=10_00,     )
										snake_case							:  int			=     CLIPTextModel(SCREAMING_SNAKE_CASE_   )
										snake_case							:  str			=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										snake_case							:  Union[str, Any]			=     {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=0   ):
										snake_case							:  Union[str, Any]			=     torch.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[str]			=     {
										    '''prompt''': '''A field of roses''',
										    '''generator''': generator,
										    # Setting height and width to None to prevent OOMs on CPU.
										    '''height''': None,
										    '''width''': None,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  int			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case							:  Dict			=     self.get_dummy_components()
										snake_case							:  Dict			=     StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[str]			=     sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Dict			=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  str			=     sd_pipe(**SCREAMING_SNAKE_CASE_   ).images
										snake_case							:  Any			=     image[0, -3:, -3:, -1]
										assert image.shape == (1, 64, 64, 3)

										snake_case							:  Union[str, Any]			=     np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]   )

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




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Union[str, Any]			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case							:  Tuple			=     self.get_dummy_components()
										snake_case							:  Dict			=     StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     '''french fries'''
										snake_case							:  Tuple			=     sd_pipe(**SCREAMING_SNAKE_CASE_,     negative_prompt=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     output.images
										snake_case							:  Dict			=     image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										snake_case							:  Tuple			=     np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]   )

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




				def 						__snake_case				(				self						:					Tuple   ):
										snake_case							:  Union[str, Any]			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case							:  int			=     self.get_dummy_components()
										snake_case							:  int			=     EulerAncestralDiscreteScheduler(
										    beta_start=0.00085,     beta_end=0.012,     beta_schedule='''scaled_linear'''   )
										snake_case							:  List[Any]			=     StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Dict			=     sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     sd_pipe(**SCREAMING_SNAKE_CASE_   ).images
										snake_case							:  List[Any]			=     image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										snake_case							:  List[Any]			=     np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]   )

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




				def 						__snake_case				(				self						:					Tuple   ):
										snake_case							:  Tuple			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case							:  str			=     self.get_dummy_components()
										snake_case							:  Optional[int]			=     PNDMScheduler()
										snake_case							:  List[str]			=     StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										# the pipeline does not expect pndm so test if it raises error.
										with self.assertRaises(SCREAMING_SNAKE_CASE_   ):
																snake_case							:  Any			=     sd_pipe(**SCREAMING_SNAKE_CASE_   ).images




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										super().test_inference_batch_single_identical(expected_max_diff=5e-3   )




				def 						__snake_case				(				self						:					List[str]   ):
										super().test_attention_slicing_forward_pass(expected_max_diff=5e-3   )




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




				def 						__snake_case				(				self						:					Dict   ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any=0   ):
										snake_case							:  List[Any]			=     torch.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     {
										    '''prompt''': '''A field of roses''',
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  int			=     '''CompVis/stable-diffusion-v1-4'''
										snake_case							:  Union[str, Any]			=     StableDiffusionModelEditingPipeline.from_pretrained(SCREAMING_SNAKE_CASE_,     safety_checker=SCREAMING_SNAKE_CASE_   )
										pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
										pipe.enable_attention_slicing()

										snake_case							:  str			=     self.get_inputs()
										snake_case							:  List[Any]			=     pipe(**SCREAMING_SNAKE_CASE_   ).images
										snake_case							:  Tuple			=     image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_12, 5_12, 3)

										snake_case							:  Dict			=     np.array(
										    [0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658]   )

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

										# make sure image changes after editing
										pipe.edit_model('''A pack of roses''',     '''A pack of blue roses'''   )

										snake_case							:  List[str]			=     pipe(**SCREAMING_SNAKE_CASE_   ).images
										snake_case							:  Optional[int]			=     image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_12, 5_12, 3)

										assert np.abs(expected_slice - image_slice   ).max() > 1e-1




				def 						__snake_case				(				self						:					int   ):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										snake_case							:  int			=     '''CompVis/stable-diffusion-v1-4'''
										snake_case							:  int			=     DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_,     subfolder='''scheduler'''   )
										snake_case							:  Tuple			=     StableDiffusionModelEditingPipeline.from_pretrained(
										    SCREAMING_SNAKE_CASE_,     scheduler=SCREAMING_SNAKE_CASE_,     safety_checker=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
										pipe.enable_attention_slicing(1   )
										pipe.enable_sequential_cpu_offload()

										snake_case							:  List[Any]			=     self.get_inputs()
										snake_case							:  Optional[int]			=     pipe(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     torch.cuda.max_memory_allocated()
										# make sure that less than 4.4 GB is allocated
										assert mem_bytes < 4.4 * 10**9



'''simple docstring'''





def   A			(							A_				:       int							):
						return n * (2 * n - 1)

def   A			(							A_				:       int							):
						snake_case							:  Optional[Any]			=     (1 + 24 * n) ** 0.5
						return ((1 + root) / 6) % 1 == 0

def   A			(							A_				:       int = 144							):
						snake_case							:  Any			=     start
						snake_case							:  Any			=     hexagonal_num(A_							)
						while not is_pentagonal(A_							):
												n += 1
												snake_case							:  Optional[Any]			=     hexagonal_num(A_							)
						return num


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

