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

from ...utils import logging


_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			logging.get_logger(__name__)







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				CLIPConfig

	_lowerCAmelCase							       =				["CLIPEncoderLayer"]





	def __init__(			self				,       UpperCamelCase__		):
					super().__init__(UpperCamelCase__		)

					A__  :	str         =							CLIPVisionModelWithProjection(config.vision_config		)

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





	@torch.no_grad()
	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=0.5				,       UpperCamelCase__=0.5		):
					A__  :	Optional[Any]         =							self.vision_model(UpperCamelCase__		)[0]

					A__  :	int         =							self.p_head(UpperCamelCase__		)
					A__  :	int         =							nsfw_detected.flatten()
					A__  :	List[Any]         =							nsfw_detected > p_threshold
					A__  :	Dict         =							nsfw_detected.tolist()

					if any(UpperCamelCase__		):
									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(UpperCamelCase__		):
									if nsfw_detected_:
													A__  :	List[str]         =							np.zeros(images[idx].shape		)

					A__  :	Union[str, Any]         =							self.w_head(UpperCamelCase__		)
					A__  :	List[Any]         =							watermark_detected.flatten()
					A__  :	Optional[int]         =							watermark_detected > w_threshold
					A__  :	Optional[int]         =							watermark_detected.tolist()

					if any(UpperCamelCase__		):
									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(UpperCamelCase__		):
									if watermark_detected_:
													A__  :	int         =							np.zeros(images[idx].shape		)

					return images, nsfw_detected, watermark_detected







def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				A__  :	Optional[Any]         =							len(__UpperCamelCase       )

				for i in range(n - 1       ):
								for j in range(i + 1							,						__UpperCamelCase       ):
												if arr[i] > arr[j]:
																num_inversions += 1

				return num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Tuple:

				"""simple docstring"""







				if len(__UpperCamelCase       ) <= 1:
								return arr, 0
				A__  :	Optional[int]         =							len(__UpperCamelCase       ) // 2
				A__  :	List[str]         =							arr[0:mid]
				A__  :	Union[str, Any]         =							arr[mid:]

				A__       ,							A__  :	List[Any]         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							_count_cross_inversions(__UpperCamelCase							,						__UpperCamelCase       )

				A__  :	Any         =							inversion_p + inversions_q + cross_inversions
				return c, num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						List[Any]       ) ->       Dict:

				"""simple docstring"""







				A__  :	str         =							[]
				A__  :	Tuple         =							0
				while i < len(__UpperCamelCase       ) and j < len(__UpperCamelCase       ):
								if p[i] > q[j]:
												# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
												# These are all inversions. The claim emerges from the
												# property that P is sorted.
												num_inversion += len(__UpperCamelCase       ) - i
												r.append(q[j]       )
												j += 1
								else:
												r.append(p[i]       )
												i += 1

				if i < len(__UpperCamelCase       ):
								r.extend(p[i:]       )
				else:
								r.extend(q[j:]       )

				return r, num_inversion

def 				SCREAMING_SNAKE_CASE     (      ) ->       Tuple:

				"""simple docstring"""







				A__  :	List[str]         =							[10, 2, 1, 5, 5, 2, 11]

				# this arr has 8 inversions:
				# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

				A__  :	int         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 8

				print('''number of inversions = '''							,						__UpperCamelCase       )

				# testing an array with zero inversion (a sorted arr_1)

				arr_a.sort()
				A__  :	Optional[Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )

				# an empty list should also have zero inversions
				A__  :	Union[str, Any]         =							[]
				A__  :	Union[str, Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Any         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()