


import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


A      :    Optional[int]		=					HfApi()

A      :    List[Any]		=					{}
# fmt: off
A      :    Tuple		=					torch.tensor([
    -0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
    1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
    -1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
    0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
A      :    Tuple		=					torch.tensor([
    -2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
    1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
    -2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
    2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
A      :    Any		=					torch.tensor([
    -0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
    -0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
    -0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
    0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
A      :    Any		=					torch.tensor([
    0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
    -0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
    0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
    -0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
A      :    List[Any]		=					torch.tensor([
    0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
    -0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
    0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
    -0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
A      :    List[Any]		=					torch.tensor([
    0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
    -0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
    0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
    -0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
A      :    Tuple		=					torch.tensor([
    0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
    -0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
    0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
    -0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
A      :    Any		=					torch.tensor([
    0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
    -0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
    0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
    -0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
A      :    Any		=					torch.tensor([
    -1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
    1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
    -2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
    1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
A      :    List[str]		=					torch.tensor([
    -1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
    0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
    -2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
    1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
A      :    int		=					torch.tensor([
    -1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
    0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
    -2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
    1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
A      :    List[str]		=					torch.tensor([
    -2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
    1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
    -3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
    3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
A      :    int		=					torch.tensor([
    -2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
    1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
    -2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
    2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
A      :    Any		=					torch.tensor([
    -2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
    1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
    -3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
    3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
A      :    str		=					torch.tensor([
    -1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
    1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
    -2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
    1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on

A      :    Tuple		=					api.list_models(filter="diffusers")
for mod in models:
    if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
        A      :    Dict		=					"/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

        print(f'Started running {mod.modelId}!!!')

        if mod.modelId.startswith("CompVis"):
            A      :    Any		=					UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
        else:
            A      :    Dict		=					UNetaDModel.from_pretrained(local_checkpoint)

        torch.manual_seed(0)
        random.seed(0)

        A      :    str		=					torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
        A      :    Tuple		=					torch.tensor([10] * noise.shape[0])
        with torch.no_grad():
            A      :    int		=					model(noise, time_step).sample

        assert torch.allclose(
            logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
        )
        print(f'{mod.modelId} has passed successfully!!!')






from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


