

'''simple docstring'''





import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  Optional[int]:
						lowercase :							Optional[Any]    		=[tensor.shape for tensor in tensor_list]
						return all(shape == shapes[0] for shape in shapes[1:]						)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    StableDiffusionLatentUpscalePipeline
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    'height',
				    'width',
				    'cross_attention_kwargs',
				    'negative_prompt_embeds',
				    'prompt_embeds',
				}
				lowerCamelCase_		       =    PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowerCamelCase_		       =    frozenset(
				    []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				lowerCamelCase_		       =    frozenset([]      )

				lowerCamelCase_		       =    True







				@property
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=1
										lowercase :							Optional[Any]    		=4
										lowercase :							Optional[Any]    		=(16, 16)

										lowercase :							Union[str, Any]    		=floats_tensor((batch_size, num_channels) + sizes		,					rng=random.Random(0   )   ).to(UpperCAmelCase__   )
										return image







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							int    		=UNetaDConditionModel(
										    act_fn='''gelu'''		,					attention_head_dim=8		,					norm_num_groups=UpperCAmelCase__		,					block_out_channels=[32, 32, 64, 64]		,					time_cond_proj_dim=160		,					conv_in_kernel=1		,					conv_out_kernel=1		,					cross_attention_dim=32		,					down_block_types=(
										        '''KDownBlock2D''',
										        '''KCrossAttnDownBlock2D''',
										        '''KCrossAttnDownBlock2D''',
										        '''KCrossAttnDownBlock2D''',
										    )		,					in_channels=8		,					mid_block_type=UpperCAmelCase__		,					only_cross_attention=UpperCAmelCase__		,					out_channels=5		,					resnet_time_scale_shift='''scale_shift'''		,					time_embedding_type='''fourier'''		,					timestep_post_act='''gelu'''		,					up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')		,					)
										lowercase :							int    		=AutoencoderKL(
										    block_out_channels=[32, 32, 64, 64]		,					in_channels=3		,					out_channels=3		,					down_block_types=[
										        '''DownEncoderBlock2D''',
										        '''DownEncoderBlock2D''',
										        '''DownEncoderBlock2D''',
										        '''DownEncoderBlock2D''',
										    ]		,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,					latent_channels=4		,					)
										lowercase :							Tuple    		=EulerDiscreteScheduler(prediction_type='''sample'''   )
										lowercase :							Union[str, 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=1000		,					hidden_act='''quick_gelu'''		,					projection_dim=512		,					)
										lowercase :							Any    		=CLIPTextModel(UpperCAmelCase__   )
										lowercase :							Tuple    		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										lowercase :							Tuple    		={
										    '''unet''': model.eval(),
										    '''vae''': vae.eval(),
										    '''scheduler''': scheduler,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										}

										return components







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Tuple=0   ):
										'''simple docstring'''
										if str(UpperCAmelCase__   ).startswith('''mps'''   ):
																lowercase :							int    		=torch.manual_seed(UpperCAmelCase__   )
										else:
																lowercase :							Any    		=torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		={
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': self.dummy_image.cpu(),
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''numpy''',
										}
										return inputs







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		='''cpu'''

										lowercase :							Union[str, Any]    		=self.get_dummy_components()
										lowercase :							Tuple    		=self.pipeline_class(**UpperCAmelCase__   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							List[str]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							int    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							str    		=image[0, -3:, -3:, -1]

										self.assertEqual(image.shape		,					(1, 256, 256, 3)   )
										lowercase :							Optional[Any]    		=np.array(
										    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]   )
										lowercase :							List[str]    		=np.abs(image_slice.flatten() - expected_slice   ).max()
										self.assertLessEqual(UpperCAmelCase__		,					1E-3   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										super().test_attention_slicing_forward_pass(expected_max_diff=7E-3   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										super().test_cpu_offload_forward_pass(expected_max_diff=3E-3   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										super().test_inference_batch_single_identical(expected_max_diff=7E-3   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										super().test_save_load_local(expected_max_difference=3E-3   )







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										super().test_save_load_optional_components(expected_max_difference=3E-3   )







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=[
										    '''DDIMScheduler''',
										    '''DDPMScheduler''',
										    '''PNDMScheduler''',
										    '''HeunDiscreteScheduler''',
										    '''EulerAncestralDiscreteScheduler''',
										    '''KDPM2DiscreteScheduler''',
										    '''KDPM2AncestralDiscreteScheduler''',
										    '''DPMSolverSDEScheduler''',
										]
										lowercase :							Optional[int]    		=self.get_dummy_components()
										lowercase :							Any    		=self.pipeline_class(**UpperCAmelCase__   )

										# make sure that PNDM does not need warm-up
										pipe.scheduler.register_to_config(skip_prk_steps=UpperCAmelCase__   )

										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										lowercase :							Optional[int]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Any    		=2

										lowercase :							Any    		=[]
										for scheduler_enum in KarrasDiffusionSchedulers:
																if scheduler_enum.name in skip_schedulers:
																						# no sigma schedulers are not supported
																						# no schedulers
																						continue

																lowercase :							Optional[int]    		=getattr(UpperCAmelCase__		,					scheduler_enum.name   )
																lowercase :							Tuple    		=scheduler_cls.from_config(pipe.scheduler.config   )
																lowercase :							Any    		=pipe(**UpperCAmelCase__   )[0]
																outputs.append(UpperCAmelCase__   )

										assert check_same_shape(UpperCAmelCase__   )





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







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=torch.manual_seed(33   )

										lowercase :							Tuple    		=StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''		,					torch_dtype=torch.floataa   )
										pipe.to('''cuda'''   )

										lowercase :							List[Any]    		=StableDiffusionLatentUpscalePipeline.from_pretrained(
										    '''stabilityai/sd-x2-latent-upscaler'''		,					torch_dtype=torch.floataa   )
										upscaler.to('''cuda'''   )

										lowercase :							Tuple    		='''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

										lowercase :							List[Any]    		=pipe(UpperCAmelCase__		,					generator=UpperCAmelCase__		,					output_type='''latent'''   ).images

										lowercase :							str    		=upscaler(
										    prompt=UpperCAmelCase__		,					image=UpperCAmelCase__		,					num_inference_steps=20		,					guidance_scale=0		,					generator=UpperCAmelCase__		,					output_type='''np'''		,					).images[0]

										lowercase :							Any    		=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''   )
										assert np.abs((expected_image - image).mean()   ) < 5E-2







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							int    		=torch.manual_seed(33   )

										lowercase :							Any    		=StableDiffusionLatentUpscalePipeline.from_pretrained(
										    '''stabilityai/sd-x2-latent-upscaler'''		,					torch_dtype=torch.floataa   )
										upscaler.to('''cuda'''   )

										lowercase :							Tuple    		='''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

										lowercase :							int    		=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''   )

										lowercase :							str    		=upscaler(
										    prompt=UpperCAmelCase__		,					image=UpperCAmelCase__		,					num_inference_steps=20		,					guidance_scale=0		,					generator=UpperCAmelCase__		,					output_type='''np'''		,					).images[0]

										lowercase :							Optional[Any]    		=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''   )
										assert np.abs((expected_image - image).max()   ) < 5E-2











'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return int((input_a, input_a).count(0      ) == 0      )


def 						__magic_name__       (      )						->   None:

					'''simple docstring'''

					assert and_gate(0, 0      ) == 0
					assert and_gate(0, 1      ) == 0
					assert and_gate(1, 0      ) == 0
					assert and_gate(1, 1      ) == 1


if __name__ == "__main__":
							test_and_gate()
							print(and_gate(1, 0))
							print(and_gate(0, 0))
							print(and_gate(0, 1))
							print(and_gate(1, 1))

