
"""simple docstring"""

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 	_lowercase       (    __UpperCAmelCase    ,	__UpperCAmelCase    ,	__UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     AltDiffusionPipeline
	lowercase_				=     TEXT_TO_IMAGE_PARAMS
	lowercase_				=     TEXT_TO_IMAGE_BATCH_PARAMS
	lowercase_				=     TEXT_TO_IMAGE_IMAGE_PARAMS
	lowercase_				=     TEXT_TO_IMAGE_IMAGE_PARAMS




	def       _UpperCamelCase   (			self )		->							int:
			torch.manual_seed(0 )
			lowerCamelCase					:      Optional[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		,			)
			lowerCamelCase					:      str          =   DDIMScheduler(
			    beta_start=0.00085		,			beta_end=0.012		,			beta_schedule='scaled_linear'		,			clip_sample=UpperCAmelCase_		,			set_alpha_to_one=UpperCAmelCase_		,			)
			torch.manual_seed(0 )
			lowerCamelCase					:      List[str]          =   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 )
			lowerCamelCase					:      Optional[int]          =   CLIPTextConfig(
			    bos_token_id=0		,			eos_token_id=2		,			hidden_size=32		,			projection_dim=32		,			intermediate_size=37		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=5002		,			)
			lowerCamelCase					:      int          =   CLIPTextModel(UpperCAmelCase_ )

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

			lowerCamelCase					:      Optional[int]          =   {
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'safety_checker': None,
			    'feature_extractor': None,
			}
			return components




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=0 )		->							str:
			if str(UpperCAmelCase_ ).startswith('mps' ):
					lowerCamelCase					:      str          =   torch.manual_seed(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[int]          =   torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   {
			    '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       _UpperCamelCase   (			self )		->							int:
			super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3 )




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			lowerCamelCase					:      int          =   'cpu'  # ensure determinism for the device-dependent torch.Generator

			lowerCamelCase					:      str          =   self.get_dummy_components()
			torch.manual_seed(0 )
			lowerCamelCase					:      Optional[Any]          =   RobertaSeriesConfig(
			    hidden_size=32		,			project_dim=32		,			intermediate_size=37		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=5002		,			)
			# TODO: remove after fixing the non-deterministic text encoder
			lowerCamelCase					:      Optional[Any]          =   RobertaSeriesModelWithTransformation(UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   text_encoder

			lowerCamelCase					:      str          =   AltDiffusionPipeline(**UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   alt_pipe.to(UpperCAmelCase_ )
			alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      Union[str, Any]          =   self.get_dummy_inputs(UpperCAmelCase_ )
			lowerCamelCase					:      str          =   'A photo of an astronaut'
			lowerCamelCase					:      Any          =   alt_pipe(**UpperCAmelCase_ )
			lowerCamelCase					:      str          =   output.images
			lowerCamelCase					:      Tuple          =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCamelCase					:      Union[str, Any]          =   np.array(
			    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] )

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




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      int          =   'cpu'  # ensure determinism for the device-dependent torch.Generator

			lowerCamelCase					:      Dict          =   self.get_dummy_components()
			lowerCamelCase					:      str          =   PNDMScheduler(skip_prk_steps=UpperCAmelCase_ )
			torch.manual_seed(0 )
			lowerCamelCase					:      Union[str, Any]          =   RobertaSeriesConfig(
			    hidden_size=32		,			project_dim=32		,			intermediate_size=37		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=5002		,			)
			# TODO: remove after fixing the non-deterministic text encoder
			lowerCamelCase					:      int          =   RobertaSeriesModelWithTransformation(UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   text_encoder
			lowerCamelCase					:      Union[str, Any]          =   AltDiffusionPipeline(**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   alt_pipe.to(UpperCAmelCase_ )
			alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      str          =   self.get_dummy_inputs(UpperCAmelCase_ )
			lowerCamelCase					:      Optional[int]          =   alt_pipe(**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   output.images
			lowerCamelCase					:      Dict          =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCamelCase					:      str          =   np.array(
			    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] )

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






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




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def       _UpperCamelCase   (			self )		->							List[Any]:
			# make sure here that pndm scheduler skips prk
			lowerCamelCase					:      Any          =   AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'		,			safety_checker=UpperCAmelCase_ )
			lowerCamelCase					:      str          =   alt_pipe.to(UpperCAmelCase_ )
			alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )

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

			lowerCamelCase					:      Optional[Any]          =   output.images

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

			assert image.shape == (1, 512, 512, 3)
			lowerCamelCase					:      int          =   np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586] )

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




	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      Optional[int]          =   DDIMScheduler.from_pretrained('BAAI/AltDiffusion'		,			subfolder='scheduler' )

			lowerCamelCase					:      Tuple          =   AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'		,			scheduler=UpperCAmelCase_		,			safety_checker=UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   alt_pipe.to(UpperCAmelCase_ )
			alt_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )

			lowerCamelCase					:      int          =   'A painting of a squirrel eating a burger'
			lowerCamelCase					:      str          =   torch.manual_seed(0 )

			lowerCamelCase					:      List[str]          =   alt_pipe([prompt]		,			generator=UpperCAmelCase_		,			num_inference_steps=2		,			output_type='numpy' )
			lowerCamelCase					:      Optional[int]          =   output.images

			lowerCamelCase					:      Union[str, Any]          =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 512, 512, 3)
			lowerCamelCase					:      Optional[int]          =   np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323] )

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








"""simple docstring"""

def 		UpperCAmelCase (		a_ = 1000      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   2**power
		lowerCamelCase					:      List[str]          =   str(a_      )
		lowerCamelCase					:      Dict          =   list(a_      )
		lowerCamelCase					:      Optional[Any]          =   0

		for i in list_num:
				sum_of_num += int(a_      )

		return sum_of_num


if __name__ == "__main__":
					_A						=			int(input('Enter the power of 2: ').strip())
					print('2 ^ ', power, ' = ', 2**power)
					_A						=			solution(power)
					print('Sum of the digits is: ', result)






