


import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCAmelCase   :    Union[str, Any]     =		{
    "E": 1_2.7_0,
    "T": 9.0_6,
    "A": 8.1_7,
    "O": 7.5_1,
    "I": 6.9_7,
    "N": 6.7_5,
    "S": 6.3_3,
    "H": 6.0_9,
    "R": 5.9_9,
    "D": 4.2_5,
    "L": 4.0_3,
    "C": 2.7_8,
    "U": 2.7_6,
    "M": 2.4_1,
    "W": 2.3_6,
    "F": 2.2_3,
    "G": 2.0_2,
    "Y": 1.9_7,
    "P": 1.9_3,
    "B": 1.2_9,
    "V": 0.9_8,
    "K": 0.7_7,
    "J": 0.1_5,
    "X": 0.1_5,
    "Q": 0.1_0,
    "Z": 0.0_7,
}
UpperCAmelCase   :    Union[str, Any]     =		"ETAOINSHRDLCUMWFGYPBVKJXQZ"
UpperCAmelCase   :    Union[str, Any]     =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			lowerCamelCase    		=  {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						(							lowerCamelCase__		:						tuple      ):





			'''simple docstring'''




			return x[0]






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			lowerCamelCase    		=  get_letter_count(lowerCamelCase__      )
			lowerCamelCase    		=  {
			    freq: [] for letter, freq in letter_to_freq.items()
			}
			for letter in LETTERS:
						freq_to_letter[letter_to_freq[letter]].append(lowerCamelCase__      )

			lowerCamelCase    		=  {}

			for freq in freq_to_letter:
						freq_to_letter[freq].sort(key=ETAOIN.find  ,							reverse=lowerCamelCase__      )
						lowerCamelCase    		=  """""".join(freq_to_letter[freq]      )

			lowerCamelCase    		=  list(freq_to_letter_str.items()      )
			freq_pairs.sort(key=lowerCamelCase__  ,							reverse=lowerCamelCase__      )

			lowerCamelCase    		=  [freq_pair[1] for freq_pair in freq_pairs]

			return "".join(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			lowerCamelCase    		=  get_frequency_order(lowerCamelCase__      )
			lowerCamelCase    		=  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()





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

					# set step values
					self.scheduler.set_timesteps(A	)

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

