


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 						snake_case			(       lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__	, unittest.TestCase	):
			'''simple docstring'''

			UpperCAmelCase       :       Tuple       = StableDiffusionModelEditingPipeline
			UpperCAmelCase       :       Optional[Any]       = TEXT_TO_IMAGE_PARAMS
			UpperCAmelCase       :       str       = TEXT_TO_IMAGE_BATCH_PARAMS
			UpperCAmelCase       :       Union[str, Any]       = TEXT_TO_IMAGE_IMAGE_PARAMS
			UpperCAmelCase       :       Any       = TEXT_TO_IMAGE_IMAGE_PARAMS

			def _lowercase (	self     :     Optional[int]					)					->					str:




								"""simple docstring"""






								torch.manual_seed(0					)
								SCREAMING_SNAKE_CASE_	      =       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							,							)
								SCREAMING_SNAKE_CASE_	      =       DDIMScheduler()
								torch.manual_seed(0					)
								SCREAMING_SNAKE_CASE_	      =       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					)
								SCREAMING_SNAKE_CASE_	      =       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=1_000							,							)
								SCREAMING_SNAKE_CASE_	      =       CLIPTextModel(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

								SCREAMING_SNAKE_CASE_	      =       {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components

			def _lowercase (	self     :     int							,							lowerCAmelCase_     :     Tuple							,							lowerCAmelCase_     :     int=0					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       torch.manual_seed(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       {
								    '''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 _lowercase (	self     :     int					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								SCREAMING_SNAKE_CASE_	      =       self.get_dummy_components()
								SCREAMING_SNAKE_CASE_	      =       StableDiffusionModelEditingPipeline(**lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       sd_pipe.to(lowerCAmelCase_					)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

								SCREAMING_SNAKE_CASE_	      =       np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889]					)

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

			def _lowercase (	self     :     Optional[Any]					)					->					Tuple:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								SCREAMING_SNAKE_CASE_	      =       self.get_dummy_components()
								SCREAMING_SNAKE_CASE_	      =       StableDiffusionModelEditingPipeline(**lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       sd_pipe.to(lowerCAmelCase_					)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								SCREAMING_SNAKE_CASE_	      =       self.get_dummy_inputs(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       '''french fries'''
								SCREAMING_SNAKE_CASE_	      =       sd_pipe(**lowerCAmelCase_							,							negative_prompt=lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       output.images
								SCREAMING_SNAKE_CASE_	      =       image[0, -3:, -3:, -1]

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

								SCREAMING_SNAKE_CASE_	      =       np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913]					)

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

			def _lowercase (	self     :     int					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								SCREAMING_SNAKE_CASE_	      =       self.get_dummy_components()
								SCREAMING_SNAKE_CASE_	      =       EulerAncestralDiscreteScheduler(
								    beta_start=0.00_085							,							beta_end=0.012							,							beta_schedule='''scaled_linear'''					)
								SCREAMING_SNAKE_CASE_	      =       StableDiffusionModelEditingPipeline(**lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       sd_pipe.to(lowerCAmelCase_					)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								SCREAMING_SNAKE_CASE_	      =       self.get_dummy_inputs(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       sd_pipe(**lowerCAmelCase_					).images
								SCREAMING_SNAKE_CASE_	      =       image[0, -3:, -3:, -1]

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

								SCREAMING_SNAKE_CASE_	      =       np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027]					)

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

			def _lowercase (	self     :     Optional[Any]					)					->					Tuple:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								SCREAMING_SNAKE_CASE_	      =       self.get_dummy_components()
								SCREAMING_SNAKE_CASE_	      =       PNDMScheduler()
								SCREAMING_SNAKE_CASE_	      =       StableDiffusionModelEditingPipeline(**lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       sd_pipe.to(lowerCAmelCase_					)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

			def _lowercase (	self     :     Tuple					)					->					int:




								"""simple docstring"""






								super().test_inference_batch_single_identical(expected_max_diff=5e-3					)




			def _lowercase (	self     :     List[str]					)					->					Tuple:




								"""simple docstring"""






								super().test_attention_slicing_forward_pass(expected_max_diff=5e-3					)

@slow
@require_torch_gpu
class 						snake_case			(       unittest.TestCase	):
			'''simple docstring'''

			def _lowercase (	self     :     Optional[int]					)					->					Optional[Any]:




								"""simple docstring"""






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

			def _lowercase (	self     :     int							,							lowerCAmelCase_     :     Optional[int]=0					)					->					Dict:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       torch.manual_seed(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       {
								    '''prompt''': '''A field of roses''',
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''output_type''': '''numpy''',
								}
								return inputs

			def _lowercase (	self     :     Optional[Any]					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       '''CompVis/stable-diffusion-v1-4'''
								SCREAMING_SNAKE_CASE_	      =       StableDiffusionModelEditingPipeline.from_pretrained(lowerCAmelCase_							,							safety_checker=lowerCAmelCase_					)
								pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
								pipe.enable_attention_slicing()

								SCREAMING_SNAKE_CASE_	      =       self.get_inputs()
								SCREAMING_SNAKE_CASE_	      =       pipe(**lowerCAmelCase_					).images
								SCREAMING_SNAKE_CASE_	      =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)

								SCREAMING_SNAKE_CASE_	      =       np.array(
								    [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658]					)

								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'''					)

								SCREAMING_SNAKE_CASE_	      =       pipe(**lowerCAmelCase_					).images
								SCREAMING_SNAKE_CASE_	      =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)

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




			def _lowercase (	self     :     Union[str, Any]					)					->					Optional[int]:




								"""simple docstring"""






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

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

								SCREAMING_SNAKE_CASE_	      =       self.get_inputs()
								SCREAMING_SNAKE_CASE_	      =       pipe(**lowerCAmelCase_					)

								SCREAMING_SNAKE_CASE_	      =       torch.cuda.max_memory_allocated()
								# make sure that less than 4.4 GB is allocated
								assert mem_bytes < 4.4 * 10**9






def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		int:







					'''simple docstring'''



					return (input_a, input_a).count(0      ) % 2







def 							UpperCAmelCase				(     )->		None:







					'''simple docstring'''



					assert xor_gate(0				,0      ) == 0
					assert xor_gate(0				,1      ) == 1
					assert xor_gate(1				,0      ) == 1
					assert xor_gate(1				,1      ) == 0


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


