






"""simple docstring"""






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  StableDiffusionSAGPipeline
      __UpperCamelCase	 =  TEXT_TO_IMAGE_PARAMS
      __UpperCamelCase	 =  TEXT_TO_IMAGE_BATCH_PARAMS
      __UpperCamelCase	 =  TEXT_TO_IMAGE_IMAGE_PARAMS
      __UpperCamelCase	 =  TEXT_TO_IMAGE_IMAGE_PARAMS
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  List[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_      :  Optional[int]    						=		DDIMScheduler(
              beta_start=0.0_00_85						,	beta_end=0.0_12						,	beta_schedule='''scaled_linear'''						,	clip_sample=lowercase_						,	set_alpha_to_one=lowercase_						,	)
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  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						,	)
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  str    						=		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						,	)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		CLIPTextModel(lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

          SCREAMING_SNAKE_CASE_      :  str    						=		{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''safety_checker''': None,
              '''feature_extractor''': None,
          }
          return components
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Tuple=0):




          '''simple docstring'''


          if str(lowercase_).startswith('''mps'''):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.manual_seed(lowercase_)
          else:
              SCREAMING_SNAKE_CASE_      :  str    						=		torch.Generator(device=lowercase_).manual_seed(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		{
              '''prompt''': '''.''',
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 1.0,
              '''sag_scale''': 1.0,
              '''output_type''': '''numpy''',
          }
          return inputs



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


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




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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		sag_pipe.to(lowercase_)
          sag_pipe.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		'''.'''
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		sag_pipe(
              [prompt]						,	generator=lowercase_						,	guidance_scale=7.5						,	sag_scale=1.0						,	num_inference_steps=20						,	output_type='''np''')

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		output.images

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
          SCREAMING_SNAKE_CASE_      :  Dict    						=		sag_pipe.to(lowercase_)
          sag_pipe.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		'''.'''
          SCREAMING_SNAKE_CASE_      :  str    						=		torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		sag_pipe(
              [prompt]						,	generator=lowercase_						,	guidance_scale=7.5						,	sag_scale=1.0						,	num_inference_steps=20						,	output_type='''np''')

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		output.images

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2



      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		sag_pipe.to(lowercase_)
          sag_pipe.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''.'''
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  str    						=		sag_pipe(
              [prompt]						,	width=768						,	height=512						,	generator=lowercase_						,	guidance_scale=7.5						,	sag_scale=1.0						,	num_inference_steps=20						,	output_type='''np'''						,	)

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		output.images

          assert image.shape == (1, 512, 768, 3)










"""simple docstring"""






def 					_A			(__a = 50							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		[[0] * 3 for _ in range(length + 1							)]

    for row_length in range(length + 1							):
        for tile_length in range(2    ,				5							):
            for tile_start in range(row_length - tile_length + 1							):
                different_colour_ways_number[row_length][tile_length - 2] += (
                    different_colour_ways_number[row_length - tile_start - tile_length][
                        tile_length - 2
                    ]
                    + 1
                )

    return sum(different_colour_ways_number[length]							)


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


