


"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__snake_case             =   {
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
__snake_case             =   """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
__snake_case             =   """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						dict[str, int]:


    """simple docstring"""





    snake_case   :				Any				 =   {letter: 0 for letter in string.ascii_uppercase}
    for letter in message.upper():
        if letter in LETTERS:
            letter_count[letter] += 1

    return letter_count







def   __lowerCAmelCase						(						lowercase  :  tuple			)     ->						str:


    """simple docstring"""





    return x[0]







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						str:


    """simple docstring"""





    snake_case   :				Tuple				 =   get_letter_count(lowercase			)
    snake_case   :				dict[int, list[str]]				 =   {
        freq: [] for letter, freq in letter_to_freq.items()
    }
    for letter in LETTERS:
        freq_to_letter[letter_to_freq[letter]].append(lowercase			)

    snake_case   :				dict[int, str]				 =   {}

    for freq in freq_to_letter:
        freq_to_letter[freq].sort(key=ETAOIN.find						,							reverse=lowercase			)
        snake_case   :				Optional[int]				 =   "".join(freq_to_letter[freq]			)

    snake_case   :				Optional[int]				 =   list(freq_to_letter_str.items()			)
    freq_pairs.sort(key=lowercase						,							reverse=lowercase			)

    snake_case   :				list[str]				 =   [freq_pair[1] for freq_pair in freq_pairs]

    return "".join(lowercase			)







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						int:


    """simple docstring"""





    snake_case   :				str				 =   get_frequency_order(lowercase			)
    snake_case   :				Optional[Any]				 =   0
    for common_letter in ETAOIN[:6]:
        if common_letter in freq_order[:6]:
            match_score += 1

    for uncommon_letter in ETAOIN[-6:]:
        if uncommon_letter in freq_order[-6:]:
            match_score += 1

    return match_score


if __name__ == "__main__":
  import doctest

  doctest.testmod()









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase    (				unittest.TestCase     ):







   @property
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       torch.manual_seed(0						)
       snake_case   :				int				 =   UNetaDModel(
           block_out_channels=(32, 64)						,     layers_per_block=2						,     sample_size=32						,     in_channels=3						,     out_channels=3						,     down_block_types=("DownBlock2D", "AttnDownBlock2D")						,     up_block_types=("AttnUpBlock2D", "UpBlock2D")						,     )
       return model







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Any				 =   self.dummy_uncond_unet
       snake_case   :				Tuple				 =   KarrasVeScheduler()

       snake_case   :				int				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Optional[Any]				 =   torch.manual_seed(0						)
       snake_case   :				List[Any]				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Dict				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						,     return_dict=UpperCamelCase__						)[0]

       snake_case   :				Tuple				 =   image[0, -3:, -3:, -1]
       snake_case   :				Tuple				 =   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case   :				int				 =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2







@slow
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   "google/ncsnpp-celebahq-256"
       snake_case   :				List[str]				 =   UNetaDModel.from_pretrained(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   KarrasVeScheduler()

       snake_case   :				Optional[int]				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Union[str, Any]				 =   pipe(num_inference_steps=20						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				int				 =   image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       snake_case   :				Any				 =   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2





