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

from ...utils import logging


SCREAMING_SNAKE_CASE				:Optional[int]									=							logging.get_logger(__name__)





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	CLIPConfig

	snake_case_          =	["CLIPEncoderLayer"]






	def __init__(			self   :					Tuple				,A   :					CLIPConfig					):
					super().__init__(A					)

					__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   :					Tuple				,A   :					List[str]				,A   :					Any				,A   :					Optional[Any]=0.5				,A   :					List[Any]=0.5					):
					__A       =					self.vision_model(A					)[0]

					__A       =					self.p_head(A					)
					__A       =					nsfw_detected.flatten()
					__A       =					nsfw_detected > p_threshold
					__A       =					nsfw_detected.tolist()

					if any(A					):
									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(A					):
									if nsfw_detected_:
													__A       =					np.zeros(images[idx].shape					)

					__A       =					self.w_head(A					)
					__A       =					watermark_detected.flatten()
					__A       =					watermark_detected > w_threshold
					__A       =					watermark_detected.tolist()

					if any(A					):
									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(A					):
									if watermark_detected_:
													__A       =					np.zeros(images[idx].shape					)

					return images, nsfw_detected, watermark_detected



"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      analyze_text(A_					)
	lowerCAmelCase__      :		Any    							=      list(''' ''' + ascii_lowercase					)
	# what is our total sum of probabilities.
	lowerCAmelCase__      :		Optional[Any]    							=      sum(single_char_strings.values()					)

	# one length string
	lowerCAmelCase__      :		int    							=      0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			lowerCAmelCase__      :		Union[str, Any]    							=      single_char_strings[ch]
			lowerCAmelCase__      :		Dict    							=      my_str / all_sum
			my_fir_sum += prob * math.loga(A_					)  # entropy formula.

    # print entropy
	print(f'{round(-1 * my_fir_sum					):.1f}'					)

	# two len string
	lowerCAmelCase__      :		List[Any]    							=      sum(two_char_strings.values()					)
	lowerCAmelCase__      :		Optional[int]    							=      0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			lowerCAmelCase__      :		int    							=      cha + cha
			if sequence in two_char_strings:
				lowerCAmelCase__      :		Optional[Any]    							=      two_char_strings[sequence]
				lowerCAmelCase__      :		int    							=      int(A_					) / all_sum
				my_sec_sum += prob * math.loga(A_					)

    # print second entropy
	print(f'{round(-1 * my_sec_sum					):.1f}'					)

	# print the difference between them
	print(f'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		List[str]    							=      Counter()  # type: ignore
	lowerCAmelCase__      :		Optional[int]    							=      Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0		,	len(A_					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings

def     __SCREAMING_SNAKE_CASE    ( ):
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
							main()




