





import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 		A_    (					_lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : Any	     =       [tensor.shape for tensor in tensor_list]
 return all(shape == shapes[0] for shape in shapes[1:]					)



class  A__       (	__snake_case	,       __snake_case	,       __snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :List[str]						   =       StableDiffusionLatentUpscalePipeline
      _UpperCAmelCase     :Union[str, Any]						   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          'height',
          'width',
          'cross_attention_kwargs',
          'negative_prompt_embeds',
          'prompt_embeds',
      }
      _UpperCAmelCase     :Optional[int]						   =       PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
      _UpperCAmelCase     :List[Any]						   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      _UpperCAmelCase     :Union[str, Any]						   =       frozenset(
          []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      _UpperCAmelCase     :int						   =       frozenset([]      )

      _UpperCAmelCase     :Any						   =       True





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       1
       UpperCamelCase     : Dict	     =       4
       UpperCamelCase     : Union[str, Any]	     =       (16, 16)

       UpperCamelCase     : Union[str, Any]	     =       floats_tensor((batch_size, num_channels) + sizes	,							rng=random.Random(0       )       ).to(A_       )
       return image





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       torch.manual_seed(0       )
       UpperCamelCase     : List[str]	     =       UNetaDConditionModel(
           act_fn="gelu"	,							attention_head_dim=8	,							norm_num_groups=A_	,							block_out_channels=[32, 32, 64, 64]	,							time_cond_proj_dim=160	,							conv_in_kernel=1	,							conv_out_kernel=1	,							cross_attention_dim=32	,							down_block_types=(
               "KDownBlock2D",
               "KCrossAttnDownBlock2D",
               "KCrossAttnDownBlock2D",
               "KCrossAttnDownBlock2D",
           )	,							in_channels=8	,							mid_block_type=A_	,							only_cross_attention=A_	,							out_channels=5	,							resnet_time_scale_shift="scale_shift"	,							time_embedding_type="fourier"	,							timestep_post_act="gelu"	,							up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")	,							)
       UpperCamelCase     : Tuple	     =       AutoencoderKL(
           block_out_channels=[32, 32, 64, 64]	,							in_channels=3	,							out_channels=3	,							down_block_types=[
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
           ]	,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]	,							latent_channels=4	,							)
       UpperCamelCase     : List[Any]	     =       EulerDiscreteScheduler(prediction_type="sample"       )
       UpperCamelCase     : Dict	     =       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	,							hidden_act="quick_gelu"	,							projection_dim=512	,							)
       UpperCamelCase     : Union[str, Any]	     =       CLIPTextModel(A_       )
       UpperCamelCase     : List[str]	     =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

       UpperCamelCase     : Dict	     =       {
           "unet": model.eval(),
           "vae": vae.eval(),
           "scheduler": scheduler,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
       }

       return components





      def 	__UpperCamelCase(			self	,							A_	,							A_=0       ):
       '''simple docstring'''




       if str(A_       ).startswith("mps"       ):
        UpperCamelCase     : Dict	     =       torch.manual_seed(A_       )
       else:
        UpperCamelCase     : Optional[int]	     =       torch.Generator(device=A_       ).manual_seed(A_       )
       UpperCamelCase     : Union[str, Any]	     =       {
           "prompt": "A painting of a squirrel eating a burger",
           "image": self.dummy_image.cpu(),
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }
       return inputs





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       "cpu"

       UpperCamelCase     : str	     =       self.get_dummy_components()
       UpperCamelCase     : List[Any]	     =       self.pipeline_class(**A_       )
       pipe.to(A_       )
       pipe.set_progress_bar_config(disable=A_       )

       UpperCamelCase     : str	     =       self.get_dummy_inputs(A_       )
       UpperCamelCase     : Dict	     =       pipe(**A_       ).images
       UpperCamelCase     : List[str]	     =       image[0, -3:, -3:, -1]

       self.assertEqual(image.shape	,							(1, 256, 256, 3)       )
       UpperCamelCase     : Union[str, Any]	     =       np.array(
           [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]       )
       UpperCamelCase     : Tuple	     =       np.abs(image_slice.flatten() - expected_slice       ).max()
       self.assertLessEqual(A_	,							1e-3       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().test_inference_batch_single_identical(expected_max_diff=7e-3       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().test_save_load_local(expected_max_difference=3e-3       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().test_save_load_optional_components(expected_max_difference=3e-3       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       [
           "DDIMScheduler",
           "DDPMScheduler",
           "PNDMScheduler",
           "HeunDiscreteScheduler",
           "EulerAncestralDiscreteScheduler",
           "KDPM2DiscreteScheduler",
           "KDPM2AncestralDiscreteScheduler",
           "DPMSolverSDEScheduler",
       ]
       UpperCamelCase     : int	     =       self.get_dummy_components()
       UpperCamelCase     : Tuple	     =       self.pipeline_class(**A_       )

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=A_       )

       pipe.to(A_       )
       pipe.set_progress_bar_config(disable=A_       )
       UpperCamelCase     : Dict	     =       self.get_dummy_inputs(A_       )
       UpperCamelCase     : Optional[Any]	     =       2

       UpperCamelCase     : Tuple	     =       []
       for scheduler_enum in KarrasDiffusionSchedulers:
        if scheduler_enum.name in skip_schedulers:
         # no sigma schedulers are not supported
         # no schedulers
         continue

        UpperCamelCase     : Optional[Any]	     =       getattr(A_	,							scheduler_enum.name       )
        UpperCamelCase     : Dict	     =       scheduler_cls.from_config(pipe.scheduler.config       )
        UpperCamelCase     : int	     =       pipe(**A_       )[0]
        outputs.append(A_       )

       assert check_same_shape(A_       )



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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       torch.manual_seed(33       )

       UpperCamelCase     : Tuple	     =       StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	,							torch_dtype=torch.floataa       )
       pipe.to("cuda"       )

       UpperCamelCase     : List[str]	     =       StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"	,							torch_dtype=torch.floataa       )
       upscaler.to("cuda"       )

       UpperCamelCase     : Tuple	     =       "a photo of an astronaut high resolution, unreal engine, ultra realistic"

       UpperCamelCase     : Tuple	     =       pipe(A_	,							generator=A_	,							output_type="latent"       ).images

       UpperCamelCase     : Union[str, Any]	     =       upscaler(
           prompt=A_	,							image=A_	,							num_inference_steps=20	,							guidance_scale=0	,							generator=A_	,							output_type="np"	,							).images[0]

       UpperCamelCase     : Tuple	     =       load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"       )
       assert np.abs((expected_image - image).mean()       ) < 5e-2





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       torch.manual_seed(33       )

       UpperCamelCase     : List[Any]	     =       StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"	,							torch_dtype=torch.floataa       )
       upscaler.to("cuda"       )

       UpperCamelCase     : Dict	     =       "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

       UpperCamelCase     : int	     =       load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"       )

       UpperCamelCase     : int	     =       upscaler(
           prompt=A_	,							image=A_	,							num_inference_steps=20	,							guidance_scale=0	,							generator=A_	,							output_type="np"	,							).images[0]

       UpperCamelCase     : str	     =       load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"       )
       assert np.abs((expected_image - image).max()       ) < 5e-2







import os



def 		A_    (					)						->			int:
 UpperCamelCase     : List[str]	     =       os.path.dirname(os.path.realpath(_lowerCAmelCase					)					)
 UpperCamelCase     : Any	     =       os.path.join(_lowerCAmelCase							,       "triangle.txt"					)

 with open(_lowerCAmelCase					) as f:
  UpperCamelCase     : Optional[Any]	     =       f.readlines()

 UpperCamelCase     : Tuple	     =       []
 for line in triangle:
  UpperCamelCase     : List[str]	     =       []
  for number in line.strip().split(" "					):
   numbers_from_line.append(int(_lowerCAmelCase					)					)
  a.append(_lowerCAmelCase					)

 for i in range(1							,       len(_lowerCAmelCase					)					):
  for j in range(len(a[i]					)					):
   UpperCamelCase     : List[Any]	     =       a[i - 1][j] if j != len(a[i - 1]					) else 0
   UpperCamelCase     : Dict	     =       a[i - 1][j - 1] if j > 0 else 0
   a[i][j] += max(_lowerCAmelCase							,       _lowerCAmelCase					)
 return max(a[-1]					)


if __name__ == "__main__":
    print(solution())
