
"""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 				a			(					lowercase						,		lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   str           =     AltDiffusionPipeline
      UpperCamelCase							:   Optional[Any]           =     TEXT_TO_IMAGE_PARAMS
      UpperCamelCase							:   Optional[Any]           =     TEXT_TO_IMAGE_BATCH_PARAMS
      UpperCamelCase							:   str           =     TEXT_TO_IMAGE_IMAGE_PARAMS
      UpperCamelCase							:   Tuple           =     TEXT_TO_IMAGE_IMAGE_PARAMS



      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Dict     							=					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     ,	)
          UpperCAmelCase__	:      List[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      )
          UpperCAmelCase__	:      Union[str, Any]     							=					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      )
          UpperCAmelCase__	:      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=5_002     ,	)
          UpperCAmelCase__	:      Union[str, Any]     							=					CLIPTextModel(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'      )
          UpperCAmelCase__	:      Tuple     							=					77

          UpperCAmelCase__	:      Any     							=					{
              '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'      ):
              UpperCAmelCase__	:      List[Any]     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Any     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					{
              '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      ):
          UpperCAmelCase__	:      Dict     							=					'cpu'  # ensure determinism for the device-dependent torch.Generator

          UpperCAmelCase__	:      Optional[int]     							=					self.get_dummy_components()
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[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=5_002     ,	)
          # TODO: remove after fixing the non-deterministic text encoder
          UpperCAmelCase__	:      Any     							=					RobertaSeriesModelWithTransformation(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[int]     							=					text_encoder

          UpperCAmelCase__	:      Optional[Any]     							=					AltDiffusionPipeline(**UpperCamelCase_      )
          UpperCAmelCase__	:      Any     							=					alt_pipe.to(UpperCamelCase_      )
          alt_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

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

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase__	:      Optional[int]     							=					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  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					'cpu'  # ensure determinism for the device-dependent torch.Generator

          UpperCAmelCase__	:      Dict     							=					self.get_dummy_components()
          UpperCAmelCase__	:      Optional[int]     							=					PNDMScheduler(skip_prk_steps=UpperCamelCase_      )
          torch.manual_seed(0      )
          UpperCAmelCase__	:      List[str]     							=					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=5_002     ,	)
          # TODO: remove after fixing the non-deterministic text encoder
          UpperCAmelCase__	:      List[str]     							=					RobertaSeriesModelWithTransformation(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					text_encoder
          UpperCAmelCase__	:      Optional[Any]     							=					AltDiffusionPipeline(**UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					alt_pipe.to(UpperCamelCase_      )
          alt_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					self.get_dummy_inputs(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					alt_pipe(**UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					output.images
          UpperCAmelCase__	:      Optional[int]     							=					image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase__	:      List[Any]     							=					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 				a			(					unittest.TestCase      ):



      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
          UpperCAmelCase__	:      Dict     							=					AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'     ,	safety_checker=UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[int]     							=					alt_pipe.to(UpperCamelCase_      )
          alt_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

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

          UpperCAmelCase__	:      int     							=					output.images

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

          assert image.shape == (1, 512, 512, 3)
          UpperCAmelCase__	:      Optional[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  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					DDIMScheduler.from_pretrained('BAAI/AltDiffusion'     ,	subfolder='scheduler'      )

          UpperCAmelCase__	:      Optional[int]     							=					AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'     ,	scheduler=UpperCamelCase_     ,	safety_checker=UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					alt_pipe.to(UpperCamelCase_      )
          alt_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					'A painting of a squirrel eating a burger'
          UpperCAmelCase__	:      Any     							=					torch.manual_seed(0      )

          UpperCAmelCase__	:      Union[str, Any]     							=					alt_pipe([prompt]     ,	generator=UpperCamelCase_     ,	num_inference_steps=2     ,	output_type='numpy'      )
          UpperCAmelCase__	:      Dict     							=					output.images

          UpperCAmelCase__	:      List[Any]     							=					image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCAmelCase__	:      Dict     							=					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"""



from itertools import permutations
def 					lowerCamelCase   (     _snake_case ):
    if num[3] % 2 != 0:
        return False

    if (num[2] + num[3] + num[4]) % 3 != 0:
        return False

    if num[5] % 5 != 0:
        return False

    UpperCAmelCase__	:      List[str]     							=					[7, 11, 13, 17]
    for i, test in enumerate(_snake_case ):
        if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
            return False
    return True
def 					lowerCamelCase   (     _snake_case = 10 ):
    return sum(
        int(''.join(map(_snake_case    ,_snake_case ) ) )
        for num in permutations(range(_snake_case ) )
        if is_substring_divisible(_snake_case ) )


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

