
from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class      _SCREAMING_SNAKE_CASE				(		UpperCamelCase__				):





				def __init__(		self			:		Any       ,   snake_case_			:		Tuple       ,   snake_case_			:		str				):


								"""simple docstring"""

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






				@torch.no_grad()
				def __call__(		self			:		Optional[Any]       ,   snake_case_			:		int = 1       ,   snake_case_			:		Optional[torch.Generator] = None       ,   snake_case_			:		int = 50       ,   snake_case_			:		Optional[str] = "pil"       ,   snake_case_			:		bool = True       ,   **snake_case_			:		str       ,   ):


								"""simple docstring"""

								A  :	Union[str, Any]				=					torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,   generator=lowercase_       ,   )
								A  :	Optional[int]				=					image.to(self.device				)

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

								for t in self.progress_bar(self.scheduler.timesteps				):
												# 1. predict noise model_output
												A  :	int				=					self.unet(lowercase_       ,   lowercase_				).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
												A  :	Optional[Any]				=					self.scheduler.step(lowercase_       ,   lowercase_       ,   lowercase_				).prev_sample

								A  :	Any				=					(image / 2 + 0.5).clamp(0       ,   1				)
								A  :	Dict				=					image.cpu().permute(0       ,   2       ,   3       ,   1				).numpy()
								if output_type == "pil":
												A  :	Union[str, Any]				=					self.numpy_to_pil(lowercase_				)

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

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






'''simple docstring'''

import string
from math import logaa





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     document.translate(
        str.maketrans(""		,			""		,			string.punctuation			)			).replace("\n"		,			""			)
    A__							=     document_without_punctuation.split(" "			)  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     corpus.lower().translate(
        str.maketrans(""		,			""		,			string.punctuation			)			)  # strip all punctuation and replace it with ''
    A__							=     corpus_without_punctuation.split("\n"			)
    A__							=     term.lower()
    return (len([doc for doc in docs if term in doc]			), len(_lowerCamelCase			))





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple=False			):
    if smoothing:
        if n == 0:
            raise ValueError("log10(0) is undefined."			)
        return round(1 + logaa(n / (1 + df)			)		,			3			)

    if df == 0:
        raise ZeroDivisionError("df must be > 0"			)
    elif n == 0:
        raise ValueError("log10(0) is undefined."			)
    return round(logaa(n / df			)		,			3			)





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    return round(tf * idf		,			3			)



