



'''simple docstring'''






import os
def    lowerCamelCase__ (		):
  with open(os.path.dirname(_A       ) + '/p022_names.txt'       ) as file:
    a		:      Union[str, Any]       =  str(file.readlines()[0]       )
    a		:      Union[str, Any]       =  names.replace('"'				,    ''       ).split(','       )

  names.sort()

  a		:      Tuple       =  0
  a		:      List[str]       =  0

  for i, name in enumerate(_A       ):
    for letter in name:
      name_score += ord(_A       ) - 64

    total_score += (i + 1) * name_score
    a		:      Tuple       =  0
  return total_score


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




'''simple docstring'''






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
     import onnxruntime as ort

class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Union[str, Any]=0			):
      a		:      List[str]       =  floats_tensor((1, 3, 1_28, 1_28)		,    rng=random.Random(__snake_case			)			)
      a		:      Optional[Any]       =  np.random.RandomState(__snake_case			)
      a		:      List[str]       =  {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 3,
          'strength': 0.75,
          'guidance_scale': 7.5,
          'output_type': 'numpy',
      }
      return inputs







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Any       =  self.get_dummy_inputs()
      a		:      int       =  pipe(**__snake_case			).images
      a		:      List[Any]       =  image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]			)
      assert np.abs(image_slice - expected_slice			).max() < 1e-1







    def     lowercase_ (   self				:						str			):
      a		:      List[Any]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Dict       =  PNDMScheduler.from_config(pipe.scheduler.config		,    skip_prk_steps=__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Any       =  self.get_dummy_inputs()
      a		:      str       =  pipe(**__snake_case			).images
      a		:      Union[str, Any]       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]			)

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







    def     lowercase_ (   self				:						List[str]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      List[Any]       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      # warmup pass to apply optimizations
      a		:      Any       =  pipe(**self.get_dummy_inputs()			)

      a		:      Optional[int]       =  self.get_dummy_inputs()
      a		:      Optional[int]       =  pipe(**__snake_case			).images
      a		:      Any       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Any       =  np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]			)

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







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Any       =  EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[str]       =  self.get_dummy_inputs()
      a		:      Optional[int]       =  pipe(**__snake_case			).images
      a		:      Any       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Tuple			):
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[Any]       =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Dict       =  self.get_dummy_inputs()
      a		:      Union[str, Any]       =  pipe(**__snake_case			).images
      a		:      int       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[int]       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Tuple       =  self.get_dummy_inputs()
      a		:      Optional[Any]       =  pipe(**__snake_case			).images
      a		:      str       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Optional[int]       =  np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]			)

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

@nightly
@require_onnxruntime
@require_torch_gpu
class 							a__(      unittest.TestCase			):







    @property
    def     lowercase_ (   self				:						List[Any]			):
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )







    @property
    def     lowercase_ (   self				:						Dict			):
      a		:      Tuple       =  ort.SessionOptions()
      a		:      Optional[Any]       =  False
      return options







    def     lowercase_ (   self				:						List[str]			):
      a		:      Dict       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      Optional[int]       =  init_image.resize((7_68, 5_12)			)
      # using the PNDM scheduler by default
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'		,    revision='onnx'		,    safety_checker=__snake_case		,    feature_extractor=__snake_case		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Union[str, Any]       =  'A fantasy landscape, trending on artstation'

      a		:      Dict       =  np.random.RandomState(0			)
      a		:      Optional[int]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=10		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      str       =  output.images
      a		:      Optional[int]       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      List[str]       =  np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]			)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







    def     lowercase_ (   self				:						str			):
      a		:      Optional[Any]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      List[str]       =  init_image.resize((7_68, 5_12)			)
      a		:      Union[str, Any]       =  LMSDiscreteScheduler.from_pretrained(
          'runwayml/stable-diffusion-v1-5'		,    subfolder='scheduler'		,    revision='onnx'			)
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'		,    revision='onnx'		,    scheduler=__snake_case		,    safety_checker=__snake_case		,    feature_extractor=__snake_case		,    provider=self.gpu_provider		,    sess_options=self.gpu_options		,    )
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Optional[int]       =  'A fantasy landscape, trending on artstation'

      a		:      str       =  np.random.RandomState(0			)
      a		:      List[str]       =  pipe(
          prompt=__snake_case		,    image=__snake_case		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=20		,    generator=__snake_case		,    output_type='np'		,    )
      a		:      str       =  output.images
      a		:      Tuple       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      Union[str, Any]       =  np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]			)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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