
'''simple docstring'''







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 				lowerCAmelCase__     (						lowerCamelCase_					,		lowerCamelCase_					,		unittest.TestCase  ):
					lowerCAmelCase_       =  StableDiffusionXLPipeline
					lowerCAmelCase_       =  TEXT_TO_IMAGE_PARAMS
					lowerCAmelCase_       =  TEXT_TO_IMAGE_BATCH_PARAMS
					lowerCAmelCase_       =  TEXT_TO_IMAGE_IMAGE_PARAMS
					lowerCAmelCase_       =  TEXT_TO_IMAGE_IMAGE_PARAMS






					def 			_snake_case					(     self	):


											"""simple docstring"""





											torch.manual_seed(0	)
											lowercase_			:  List[Any]									= 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''')       ,							attention_head_dim=(2, 4)       ,							use_linear_projection=__SCREAMING_SNAKE_CASE       ,							addition_embed_type='''text_time'''       ,							addition_time_embed_dim=8       ,							transformer_layers_per_block=(1, 2)       ,							projection_class_embeddings_input_dim=80       ,							cross_attention_dim=64       ,							)
											lowercase_			:  List[str]									= EulerDiscreteScheduler(
											    beta_start=0.00_085       ,							beta_end=0.012       ,							steps_offset=1       ,							beta_schedule='''scaled_linear'''       ,							timestep_spacing='''leading'''       ,							)
											torch.manual_seed(0	)
											lowercase_			:  Tuple									= 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       ,							sample_size=1_28       ,							)
											torch.manual_seed(0	)
											lowercase_			:  Dict									= CLIPTextConfig(
											    bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=32       ,							intermediate_size=37       ,							layer_norm_eps=1E-0_5       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=10_00       ,							hidden_act='''gelu'''       ,							projection_dim=32       ,							)
											lowercase_			:  List[str]									= CLIPTextModel(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Tuple									= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,							local_files_only=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  List[str]									= CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Tuple									= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,							local_files_only=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  Union[str, Any]									= {
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''text_encoder_2''': text_encoder_a,
											    '''tokenizer_2''': tokenizer_a,
											    # "safety_checker": None,
											    # "feature_extractor": None,
											}
											return components






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=0	):


											"""simple docstring"""





											if str(__SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
																	lowercase_			:  Optional[Any]									= torch.manual_seed(__SCREAMING_SNAKE_CASE	)
											else:
																	lowercase_			:  int									= torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[Any]									= {
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 5.0,
											    '''output_type''': '''numpy''',
											}
											return inputs






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  List[Any]									= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowercase_			:  str									= self.get_dummy_components()
											lowercase_			:  Union[str, Any]									= StableDiffusionXLPipeline(**__SCREAMING_SNAKE_CASE	)
											lowercase_			:  int									= sd_pipe.to(__SCREAMING_SNAKE_CASE	)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  Union[str, Any]									= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  int									= sd_pipe(**__SCREAMING_SNAKE_CASE	).images
											lowercase_			:  List[str]									= image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											lowercase_			:  str									= np.array([0.5_873, 0.6_128, 0.4_797, 0.5_122, 0.5_674, 0.4_639, 0.5_227, 0.5_149, 0.4_747]	)

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






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  List[Any]									= self.get_dummy_components()
											lowercase_			:  int									= StableDiffusionXLPipeline(**__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[Any]									= sd_pipe.to(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[str]									= sd_pipe.to(__SCREAMING_SNAKE_CASE	)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

											# forward without prompt embeds
											lowercase_			:  Dict									= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  str									= 3 * ['''this is a negative prompt''']
											lowercase_			:  Union[str, Any]									= negative_prompt
											lowercase_			:  Optional[Any]									= 3 * [inputs['''prompt''']]

											lowercase_			:  int									= sd_pipe(**__SCREAMING_SNAKE_CASE	)
											lowercase_			:  str									= output.images[0, -3:, -3:, -1]

											# forward with prompt embeds
											lowercase_			:  List[str]									= self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Any									= 3 * ['''this is a negative prompt''']
											lowercase_			:  str									= 3 * [inputs.pop('''prompt'''	)]

											(
											    (
											    lowercase_
											)		,				(
											    lowercase_
											)		,				(
											    lowercase_
											)		,				(
											    lowercase_
											)		,				
											)			:  Any									= sd_pipe.encode_prompt(__SCREAMING_SNAKE_CASE       ,							negative_prompt=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  str									= sd_pipe(
											    **__SCREAMING_SNAKE_CASE       ,							prompt_embeds=__SCREAMING_SNAKE_CASE       ,							negative_prompt_embeds=__SCREAMING_SNAKE_CASE       ,							pooled_prompt_embeds=__SCREAMING_SNAKE_CASE       ,							negative_pooled_prompt_embeds=__SCREAMING_SNAKE_CASE       ,							)
											lowercase_			:  Union[str, Any]									= output.images[0, -3:, -3:, -1]

											# make sure that it's equal
											assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1E-4






					def 			_snake_case					(     self	):


											"""simple docstring"""





											super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







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






					def 			_snake_case					(     self	):


											"""simple docstring"""





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






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE="cpu"       ,							__SCREAMING_SNAKE_CASE=torch.floataa       ,							__SCREAMING_SNAKE_CASE=0	):


											"""simple docstring"""





											lowercase_			:  List[Any]									= torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Dict									= np.random.RandomState(__SCREAMING_SNAKE_CASE	).standard_normal((1, 4, 64, 64)	)
											lowercase_			:  Any									= torch.from_numpy(__SCREAMING_SNAKE_CASE	).to(device=__SCREAMING_SNAKE_CASE       ,							dtype=__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[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 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Union[str, Any]									= DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''	)
											pipe.to(__SCREAMING_SNAKE_CASE	)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  Any									= self.get_inputs(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Dict									= pipe(**__SCREAMING_SNAKE_CASE	).images
											lowercase_			:  List[Any]									= image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_12, 5_12, 3)
											lowercase_			:  Dict									= np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]	)
											assert np.abs(image_slice - expected_slice	).max() < 7E-3





'''simple docstring'''







def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 998001							):



						"""simple docstring"""





						lowercase_			:  Dict									= 0
						for i in range(999   ,   99   ,   -1							):  # 3 digit numbers range from 999 down to 100
												for j in range(999   ,   99   ,   -1							):
																		lowercase_			:  int									= str(i * j							)
																		if product_string == product_string[::-1] and i * j < n:
																								lowercase_			:  str									= max(__SCREAMING_SNAKE_CASE   ,   i * j							)
						return answer


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



