





'''simple docstring'''

import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


__lowerCAmelCase				:      List[Any]						      =logging.get_logger(__name__)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							CLIPConfig

 __lowercase  						=							["""CLIPEncoderLayer"""]
 def __init__(							self :List[str]		,			lowercase_ :CLIPConfig				)->				Optional[int]:
     super().__init__(lowercase_				)

     A__							=     CLIPVisionModelWithProjection(config.vision_config				)

     A__							=     nn.Linear(config.vision_config.projection_dim		,			1				)
     A__							=     nn.Linear(config.vision_config.projection_dim		,			1				)



 @torch.no_grad()
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Union[str, Any]		,			lowercase_ :int		,			lowercase_ :Union[str, Any]=0.5		,			lowercase_ :Union[str, Any]=0.5				)->				List[str]:
     A__							=     self.vision_model(lowercase_				)[0]

     A__							=     self.p_head(lowercase_				)
     A__							=     nsfw_detected.flatten()
     A__							=     nsfw_detected > p_threshold
     A__							=     nsfw_detected.tolist()

     if any(lowercase_				):
         logger.warning(
             "Potential NSFW content was detected in one or more images. A black image will be returned instead."
             " Try again with a different prompt and/or seed."				)

     for idx, nsfw_detected_ in enumerate(lowercase_				):
         if nsfw_detected_:
             A__							=     np.zeros(images[idx].shape				)

     A__							=     self.w_head(lowercase_				)
     A__							=     watermark_detected.flatten()
     A__							=     watermark_detected > w_threshold
     A__							=     watermark_detected.tolist()

     if any(lowercase_				):
         logger.warning(
             "Potential watermarked content was detected in one or more images. A black image will be returned instead."
             " Try again with a different prompt and/or seed."				)

     for idx, watermark_detected_ in enumerate(lowercase_				):
         if watermark_detected_:
             A__							=     np.zeros(images[idx].shape				)

     return images, nsfw_detected, watermark_detected










'''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			)



