



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

from ...utils import logging


snake_case     :    Dict						=    logging.get_logger(__name__)
def 						snake_case__							(       __lowercase				,  __lowercase							)		->		Optional[Any]:







						"""simple docstring"""

						A__						:				List[str]         =			nn.functional.normalize(snake_case_							)
						A__						:				Union[str, Any]         =			nn.functional.normalize(snake_case_							)
						return torch.mm(snake_case_				,  normalized_text_embeds.t()							)




class 				lowerCAmelCase__	(						SCREAMING_SNAKE_CASE__    ):
					__A  :     Any     =    CLIPConfig

					__A  :     Optional[int]     =    ['CLIPEncoderLayer']







					def __init__(							self   :  str      ,		_A   :  Dict):
											super().__init__(_A)

											A__						:				Any         =			CLIPVisionModel(config.vision_config)
											A__						:				Optional[int]         =			nn.Linear(config.vision_config.hidden_size      ,		config.projection_dim      ,		bias=_A)

											A__						:				Optional[int]         =			nn.Parameter(torch.ones(17      ,		config.projection_dim)      ,		requires_grad=_A)
											A__						:				Optional[Any]         =			nn.Parameter(torch.ones(3      ,		config.projection_dim)      ,		requires_grad=_A)

											A__						:				int         =			nn.Parameter(torch.ones(17)      ,		requires_grad=_A)
											A__						:				List[Any]         =			nn.Parameter(torch.ones(3)      ,		requires_grad=_A)







					@torch.no_grad()
					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  List[str]      ,		_A   :  Optional[Any]):
											A__						:				Optional[int]         =			self.vision_model(_A)[1]  # pooled_output
											A__						:				Dict         =			self.visual_projection(_A)

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

											A__						:				Union[str, Any]         =			[]
											A__						:				List[str]         =			image_embeds.shape[0]
											for i in range(_A):
																	A__						:				Tuple         =			{"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
																	A__						:				Dict         =			0.0

																	for concept_idx in range(len(special_cos_dist[0])):
																							A__						:				Optional[int]         =			special_cos_dist[i][concept_idx]
																							A__						:				str         =			self.special_care_embeds_weights[concept_idx].item()
																							A__						:				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]})
																													A__						:				Optional[int]         =			0.01

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

																	result.append(_A)

											A__						:				Tuple         =			[len(res["bad_concepts"]) > 0 for res in result]

											for idx, has_nsfw_concept in enumerate(_A):
																	if has_nsfw_concept:
																							if torch.is_tensor(_A) or torch.is_tensor(images[0]):
																													A__						:				Optional[int]         =			torch.zeros_like(images[idx])  # black image
																							else:
																													A__						:				str         =			np.zeros(images[idx].shape)  # black image

											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.")

											return images, has_nsfw_concepts







					@torch.no_grad()
					def 			_lowercase      (							self   :  int      ,		_A   :  Tuple      ,		_A   :  Dict):
											A__						:				Optional[int]         =			self.vision_model(_A)[1]  # pooled_output
											A__						:				Any         =			self.visual_projection(_A)

											A__						:				str         =			cosine_distance(_A      ,		self.special_care_embeds)
											A__						:				Optional[Any]         =			cosine_distance(_A      ,		self.concept_embeds)

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

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

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

											A__						:				List[Any]         =			0.0  # black image

											return images, has_nsfw_concepts






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     __magic_name__				=       args.serialization_dir
     __magic_name__				=       args.pruning_method
     __magic_name__				=       args.threshold

     __magic_name__				=       torch.load(os.path.join(snake_case_ ,     '''pytorch_model.bin'''						) ,     map_location='''cpu'''						)

     __magic_name__				=       0  # Number of remaining (not pruned) params in the encoder
     __magic_name__				=       0  # Number of params in the encoder

     print('''name'''.ljust(60 ,     ''' '''						) ,     '''Remaining Weights %''' ,     '''Remaining Weight'''						)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    __magic_name__				=       TopKBinarizer.apply(snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __magic_name__				=       ThresholdBinarizer.apply(snake_case_ ,     snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "l0":
                    __magic_name__		,  __magic_name__				=       -0.1, 1.1
                    __magic_name__				=       torch.sigmoid(snake_case_						)
                    __magic_name__				=       s * (r - l) + l
                    __magic_name__				=       s_bar.clamp(min=0.0 ,     max=1.0						)
                    __magic_name__				=       (mask > 0.0).sum().item()
               else:
                    raise ValueError('''Unknown pruning method'''						)
               remaining_count += mask_ones
               print(name.ljust(60 ,     ''' '''						) ,     str(round(100 * mask_ones / param.numel() ,     3						)						).ljust(20 ,     ''' '''						) ,     str(snake_case_						)						)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(''''''						)
     print('''Remaining Weights (global) %: ''' ,     100 * remaining_count / encoder_count						)


if __name__ == "__main__":
    a_    :				Optional[Any]					=			argparse.ArgumentParser()

    parser.add_argument(
        '--pruning_method',
        choices=['l0', 'topK', 'sigmoied_threshold'],
        type=str,
        required=True,
        help=(
            'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
            ' pruning)'
        ),
    )
    parser.add_argument(
        '--threshold',
        type=float,
        required=False,
        help=(
            'For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
            'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
            'Not needed for `l0`'
        ),
    )
    parser.add_argument(
        '--serialization_dir',
        type=str,
        required=True,
        help='Folder containing the model that was previously fine-pruned',
    )

    a_    :				List[Any]					=			parser.parse_args()

    main(args)