




"""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 					__magic_name__							( snake_case_       ):






				'''simple docstring'''
				__UpperCamelCase    						=		CLIPConfig

				__UpperCamelCase    						=		["""CLIPEncoderLayer"""]





				def __init__( self	,				_a				):





							"""simple docstring"""
							super().__init__(_a				)

							lowerCamelCase       =      CLIPVisionModelWithProjection(config.vision_config				)

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





				@torch.no_grad()
				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a=0.5	,				_a=0.5				):





							"""simple docstring"""
							lowerCamelCase       =      self.vision_model(_a				)[0]

							lowerCamelCase       =      self.p_head(_a				)
							lowerCamelCase       =      nsfw_detected.flatten()
							lowerCamelCase       =      nsfw_detected > p_threshold
							lowerCamelCase       =      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_:
													lowerCamelCase       =      np.zeros(images[idx].shape				)

							lowerCamelCase       =      self.w_head(_a				)
							lowerCamelCase       =      watermark_detected.flatten()
							lowerCamelCase       =      watermark_detected > w_threshold
							lowerCamelCase       =      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_:
													lowerCamelCase       =      np.zeros(images[idx].shape				)

							return images, nsfw_detected, watermark_detected









"""simple docstring"""




from __future__ import annotations

lowerCAmelCase							:    str  							=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase							:    Dict  							=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      []
			lowerCamelCase       =      len(snake_case__       )

			for i in range(snake_case__       ):
						lowerCamelCase       =      -1
						for j in range(i + 1 ,				snake_case__       ):
									if arr[i] < arr[j]:
												lowerCamelCase       =      arr[j]
												break
						result.append(snake_case__       )
			return result



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      []
			for i, outer in enumerate(snake_case__       ):
						lowerCamelCase       =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												lowerCamelCase       =      inner
												break
						result.append(snake_case__       )
			return result



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      len(snake_case__       )
			lowerCamelCase       =      []
			lowerCamelCase       =      [-1] * arr_size

			for index in reversed(range(snake_case__       )       ):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									lowerCamelCase       =      stack[-1]
						stack.append(arr[index]       )
			return result


if __name__ == "__main__":
					from doctest import testmod
					from timeit import timeit

					testmod()
					print(next_greatest_element_slow(arr))
					print(next_greatest_element_fast(arr))
					print(next_greatest_element(arr))

					lowerCAmelCase							:    Dict  							=							(
					    """from __main__ import arr, next_greatest_element_slow, """
					    """next_greatest_element_fast, next_greatest_element"""
					)
					print(
					    """next_greatest_element_slow():""",
					    timeit("""next_greatest_element_slow(arr)""", setup=setup),
					)
					print(
					    """next_greatest_element_fast():""",
					    timeit("""next_greatest_element_fast(arr)""", setup=setup),
					)
					print(
					    """     next_greatest_element():""",
					    timeit("""next_greatest_element(arr)""", setup=setup),
					)



