





'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							42
 __lowercase  						=							42
 __lowercase  						=							None






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 __lowercase  						=							2
 @register_to_config
 def __init__(							self :Any		,			lowercase_ :float = 0.0_2		,			lowercase_ :float = 1_00		,			lowercase_ :float = 1.0_0_7		,			lowercase_ :float = 80		,			lowercase_ :float = 0.0_5		,			lowercase_ :float = 50		,			)->				str:
     # standard deviation of the initial noise distribution
     A__							=     sigma_max

     # setable values
     A__							=     None
     A__							=     None
     A__							=     None  # sigma(t_i)
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :torch.FloatTensor		,			lowercase_ :Optional[int] = None				)->				torch.FloatTensor:
     return sample
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :int		,			lowercase_ :Union[str, torch.device] = None				)->				Dict:
     A__							=     num_inference_steps
     A__							=     np.arange(0		,			self.num_inference_steps				)[::-1].copy()
     A__							=     torch.from_numpy(lowercase_				).to(lowercase_				)
     A__							=     [
         (
             self.config.sigma_max**2
             * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
         )
         for i in self.timesteps
     ]
     A__							=     torch.tensor(lowercase_		,			dtype=torch.floataa		,			device=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :torch.FloatTensor		,			lowercase_ :float		,			lowercase_ :Optional[torch.Generator] = None				)->				Tuple[torch.FloatTensor, float]:
     if self.config.s_min <= sigma <= self.config.s_max:
         A__							=     min(self.config.s_churn / self.num_inference_steps		,			2**0.5 - 1				)
     else:
         A__							=     0

     # sample eps ~ N(0, S_noise^2 * I)
     A__							=     self.config.s_noise * randn_tensor(sample.shape		,			generator=lowercase_				).to(sample.device				)
     A__							=     sigma + gamma * sigma
     A__							=     sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

     return sample_hat, sigma_hat
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :torch.FloatTensor		,			lowercase_ :float		,			lowercase_ :float		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True		,			)->				Union[KarrasVeOutput, Tuple]:
     A__							=     sample_hat + sigma_hat * model_output
     A__							=     (sample_hat - pred_original_sample) / sigma_hat
     A__							=     sample_hat + (sigma_prev - sigma_hat) * derivative

     if not return_dict:
         return (sample_prev, derivative)

     return KarrasVeOutput(
         prev_sample=lowercase_		,			derivative=lowercase_		,			pred_original_sample=lowercase_				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :torch.FloatTensor		,			lowercase_ :float		,			lowercase_ :float		,			lowercase_ :torch.FloatTensor		,			lowercase_ :torch.FloatTensor		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True		,			)->				Union[KarrasVeOutput, Tuple]:
     A__							=     sample_prev + sigma_prev * model_output
     A__							=     (sample_prev - pred_original_sample) / sigma_prev
     A__							=     sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

     if not return_dict:
         return (sample_prev, derivative)

     return KarrasVeOutput(
         prev_sample=lowercase_		,			derivative=lowercase_		,			pred_original_sample=lowercase_				)



 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :List[str]		,			lowercase_ :int		,			lowercase_ :Dict				)->				str:
     raise NotImplementedError()










'''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			)



