






import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 							__a							(  __A ,	__A ,	__A ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Any]				    =						AltDiffusionPipeline
		UpperCAmelCase__      :				int				    =						TEXT_TO_IMAGE_PARAMS
		UpperCAmelCase__      :				Tuple				    =						TEXT_TO_IMAGE_BATCH_PARAMS
		UpperCAmelCase__      :				str				    =						TEXT_TO_IMAGE_IMAGE_PARAMS
		UpperCAmelCase__      :				Union[str, Any]				    =						TEXT_TO_IMAGE_IMAGE_PARAMS


		def   __snake_case			(						self       ):
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE_   :	Union[str, 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') ,    cross_attention_dim=32 ,    )
						SCREAMING_SNAKE_CASE_   :	Dict													=   DDIMScheduler(
						    beta_start=0.0_00_85 ,    beta_end=0.0_12 ,    beta_schedule='scaled_linear' ,    clip_sample=UpperCamelCase__ ,    set_alpha_to_one=UpperCamelCase__ ,    )
						torch.manual_seed(0       )
						SCREAMING_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 ,    )

						# TODO: address the non-deterministic text encoder (fails for save-load tests)
						# torch.manual_seed(0)
						# text_encoder_config = RobertaSeriesConfig(
						#     hidden_size=32,
						#     project_dim=32,
						#     intermediate_size=37,
						#     layer_norm_eps=1e-05,
						#     num_attention_heads=4,
						#     num_hidden_layers=5,
						#     vocab_size=5002,
						# )
						# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   CLIPTextConfig(
						    bos_token_id=0 ,    eos_token_id=2 ,    hidden_size=32 ,    projection_dim=32 ,    intermediate_size=37 ,    layer_norm_eps=1E-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    pad_token_id=1 ,    vocab_size=5002 ,    )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   CLIPTextModel(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Any													=   XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   77

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


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=0       ):
						if str(UpperCamelCase__       ).startswith('mps'       ):
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.manual_seed(UpperCamelCase__       )
						else:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}
						return inputs


		def   __snake_case			(						self       ):
						super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )


		def   __snake_case			(						self       ):
						super().test_inference_batch_single_identical(expected_max_diff=3E-3       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   'cpu'  # ensure determinism for the device-dependent torch.Generator

						SCREAMING_SNAKE_CASE_   :	int													=   self.get_dummy_components()
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE_   :	Any													=   RobertaSeriesConfig(
						    hidden_size=32 ,    project_dim=32 ,    intermediate_size=37 ,    layer_norm_eps=1E-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    vocab_size=5002 ,    )
						# TODO: remove after fixing the non-deterministic text encoder
						SCREAMING_SNAKE_CASE_   :	int													=   RobertaSeriesModelWithTransformation(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   text_encoder

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AltDiffusionPipeline(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   alt_pipe.to(UpperCamelCase__       )
						alt_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_dummy_inputs(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   'A photo of an astronaut'
						SCREAMING_SNAKE_CASE_   :	str													=   alt_pipe(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   output.images
						SCREAMING_SNAKE_CASE_   :	Dict													=   image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						SCREAMING_SNAKE_CASE_   :	Dict													=   np.array(
						    [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]       )

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






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   'cpu'  # ensure determinism for the device-dependent torch.Generator

						SCREAMING_SNAKE_CASE_   :	Any													=   self.get_dummy_components()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   PNDMScheduler(skip_prk_steps=UpperCamelCase__       )
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   RobertaSeriesConfig(
						    hidden_size=32 ,    project_dim=32 ,    intermediate_size=37 ,    layer_norm_eps=1E-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    vocab_size=5002 ,    )
						# TODO: remove after fixing the non-deterministic text encoder
						SCREAMING_SNAKE_CASE_   :	str													=   RobertaSeriesModelWithTransformation(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   text_encoder
						SCREAMING_SNAKE_CASE_   :	Any													=   AltDiffusionPipeline(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   alt_pipe.to(UpperCamelCase__       )
						alt_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   self.get_dummy_inputs(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   alt_pipe(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   output.images
						SCREAMING_SNAKE_CASE_   :	Dict													=   image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						SCREAMING_SNAKE_CASE_   :	Dict													=   np.array(
						    [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]       )

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






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





		'''simple docstring'''



		def   __snake_case			(						self       ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()


		def   __snake_case			(						self       ):
						# make sure here that pndm scheduler skips prk
						SCREAMING_SNAKE_CASE_   :	int													=   AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion' ,    safety_checker=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   alt_pipe.to(UpperCamelCase__       )
						alt_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   'A painting of a squirrel eating a burger'
						SCREAMING_SNAKE_CASE_   :	str													=   torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE_   :	int													=   alt_pipe([prompt] ,    generator=UpperCamelCase__ ,    guidance_scale=6.0 ,    num_inference_steps=20 ,    output_type='np'       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   output.images

						SCREAMING_SNAKE_CASE_   :	Any													=   image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]       )

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






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   DDIMScheduler.from_pretrained('BAAI/AltDiffusion' ,    subfolder='scheduler'       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion' ,    scheduler=UpperCamelCase__ ,    safety_checker=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   alt_pipe.to(UpperCamelCase__       )
						alt_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   'A painting of a squirrel eating a burger'
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.manual_seed(0       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   alt_pipe([prompt] ,    generator=UpperCamelCase__ ,    num_inference_steps=2 ,    output_type='numpy'       )
						SCREAMING_SNAKE_CASE_   :	int													=   output.images

						SCREAMING_SNAKE_CASE_   :	Tuple													=   image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]       )

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







from timeit import timeit



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   0
				while number:
								number &= number - 1
								result += 1
				return result



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if number < 0:
								raise ValueError('the value of input must not be negative'      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   0
				while number:
								if number % 2 == 1:
												result += 1
								number >>= 1
				return result



def 					_lowerCamelCase(			):





				'''simple docstring'''







				def do_benchmark(lowerCAmelCase__				:		int      ) -> None:
								SCREAMING_SNAKE_CASE_   :	int													=   'import __main__ as z'
								print(F'''Benchmark when {number = }:'''      )
								print(F'''{get_set_bits_count_using_modulo_operator(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	int													=   timeit('z.get_set_bits_count_using_modulo_operator(25)'    ,    setup=lowerCAmelCase__      )
								print(F'''timeit() runs in {timing} seconds'''      )
								print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__      ) = }'''      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   timeit(
								    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'    ,    setup=lowerCAmelCase__    ,    )
								print(F'''timeit() runs in {timing} seconds'''      )

				for number in (25, 37, 58, 0):
								do_benchmark(lowerCAmelCase__      )
								print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()