import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    nn.functional.normalize(__lowerCAmelCase  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    nn.functional.normalize(__lowerCAmelCase  )
							return torch.mm(__lowerCAmelCase     ,	normalized_text_embeds.t()  )




class  _snake_case		(    UpperCAmelCase__ ):







				"""simple docstring"""






				a			=						CLIPConfig

				a			=						["CLIPEncoderLayer"]





				def __init__(      self							:							str      ,    _A							:							CLIPConfig):



											"""simple docstring"""







											super().__init__(lowerCamelCase__)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    CLIPVisionModel(config.vision_config)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    nn.Linear(config.vision_config.hidden_size      ,    config.projection_dim      ,    bias=lowerCamelCase__)

											_SCREAMING_SNAKE_CASE		:						Any			  =    nn.Parameter(torch.ones(1_7      ,    config.projection_dim)      ,    requires_grad=lowerCamelCase__)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    nn.Parameter(torch.ones(3      ,    config.projection_dim)      ,    requires_grad=lowerCamelCase__)

											_SCREAMING_SNAKE_CASE		:						str			  =    nn.Parameter(torch.ones(1_7)      ,    requires_grad=lowerCamelCase__)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    nn.Parameter(torch.ones(3)      ,    requires_grad=lowerCamelCase__)





				@torch.no_grad()
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							List[str]      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.vision_model(lowerCamelCase__)[1]  # pooled_output
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.visual_projection(lowerCamelCase__)

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    cosine_distance(lowerCamelCase__      ,    self.special_care_embeds).cpu().float().numpy()
											_SCREAMING_SNAKE_CASE		:						str			  =    cosine_distance(lowerCamelCase__      ,    self.concept_embeds).cpu().float().numpy()

											_SCREAMING_SNAKE_CASE		:						str			  =    []
											_SCREAMING_SNAKE_CASE		:						Any			  =    image_embeds.shape[0]
											for i in range(lowerCamelCase__):
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}

																		# increase this value to create a stronger `nfsw` filter
																		# at the cost of increasing the possibility of filtering benign images
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    0.0

																		for concept_idx in range(len(special_cos_dist[0])):
																									_SCREAMING_SNAKE_CASE		:						Dict			  =    special_cos_dist[i][concept_idx]
																									_SCREAMING_SNAKE_CASE		:						str			  =    self.special_care_embeds_weights[concept_idx].item()
																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    round(concept_cos - concept_threshold + adjustment      ,    3)
																									if result_img["special_scores"][concept_idx] > 0:
																																result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]})
																																_SCREAMING_SNAKE_CASE		:						Tuple			  =    0.01

																		for concept_idx in range(len(cos_dist[0])):
																									_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    cos_dist[i][concept_idx]
																									_SCREAMING_SNAKE_CASE		:						int			  =    self.concept_embeds_weights[concept_idx].item()
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    round(concept_cos - concept_threshold + adjustment      ,    3)
																									if result_img["concept_scores"][concept_idx] > 0:
																																result_img["bad_concepts"].append(lowerCamelCase__)

																		result.append(lowerCamelCase__)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [len(res["""bad_concepts"""]) > 0 for res in result]

											return images, has_nsfw_concepts





				@torch.no_grad()
				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							torch.FloatTensor      ,    _A							:							torch.FloatTensor):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.vision_model(lowerCamelCase__)[1]  # pooled_output
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.visual_projection(lowerCamelCase__)

											_SCREAMING_SNAKE_CASE		:						Any			  =    cosine_distance(lowerCamelCase__      ,    self.special_care_embeds)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    cosine_distance(lowerCamelCase__      ,    self.concept_embeds)

											# increase this value to create a stronger `nsfw` filter
											# at the cost of increasing the possibility of filtering benign images
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.0

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    special_cos_dist - self.special_care_embeds_weights + adjustment
											# special_scores = special_scores.round(decimals=3)
											_SCREAMING_SNAKE_CASE		:						int			  =    torch.any(special_scores > 0      ,    dim=1)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    special_care * 0.01
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    special_adjustment.unsqueeze(1).expand(-1      ,    cos_dist.shape[1])

											_SCREAMING_SNAKE_CASE		:						str			  =    (cos_dist - self.concept_embeds_weights) + special_adjustment
											# concept_scores = concept_scores.round(decimals=3)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.any(concept_scores > 0      ,    dim=1)

											return images, has_nsfw_concepts



"""simple docstring"""





import argparse
from collections import defaultdict



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						str			  =    F"""{file}_{class_name}_{test_name}"""
							done_test[_id] += 1

							with open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    f.readlines()

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    F"""class {class_name}("""
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    F"""{4 * " "}def {test_name}("""
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    F"""{8 * " "}{correct_line.split()[0]}"""
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    F"""{16 * " "}{correct_line.split()[0]}"""
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    False
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    False
							_SCREAMING_SNAKE_CASE		:						Any			  =    0
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0

							_SCREAMING_SNAKE_CASE		:						Dict			  =    []
							for line in lines:
														if line.startswith(__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						Any			  =    True
														elif in_class and line.startswith(__SCREAMING_SNAKE_CASE  ):
																					_SCREAMING_SNAKE_CASE		:						str			  =    True
														elif in_class and in_func and (line.startswith(__SCREAMING_SNAKE_CASE  ) or line.startswith(__SCREAMING_SNAKE_CASE  )):
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    len(line.split(correct_line.split()[0]  )[0]  )
																					count += 1

																					if count == done_test[_id]:
																												_SCREAMING_SNAKE_CASE		:						int			  =    True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												_SCREAMING_SNAKE_CASE		:						Any			  =    True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(F"""{spaces * " "}{correct_line}"""  )
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    False
														else:
																					new_lines.append(__SCREAMING_SNAKE_CASE  )

							with open(__SCREAMING_SNAKE_CASE     ,	"""w"""  ) as f:
														for line in new_lines:
																					f.write(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None  )->    Optional[Any]:
							if fail is not None:
														with open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ) as f:
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {l.strip() for l in f.readlines()}
							else:
														_SCREAMING_SNAKE_CASE		:						str			  =    None

							with open(__SCREAMING_SNAKE_CASE     ,	"""r"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						str			  =    f.readlines()

							_SCREAMING_SNAKE_CASE		:						str			  =    defaultdict(__SCREAMING_SNAKE_CASE  )
							for line in correct_lines:
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    line.split(""";"""  )
														if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
																					overwrite_file(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
			parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
			lowerCAmelCase_     =	parser.parse_args()

			main(args.correct_filename, args.fail_filename)


