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

from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		return torch.mm(lowerCAmelCase						,  normalized_text_embeds.t() )







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Tuple	=		CLIPConfig

						UpperCamelCase_							: str	=		["CLIPEncoderLayer"]

						def __init__(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__(a				)

								_UpperCamelCase						    =	CLIPVisionModel(config.vision_config				)
								_UpperCamelCase						    =	nn.Linear(config.vision_config.hidden_size       ,							config.projection_dim       ,							bias=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17       ,							config.projection_dim				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3       ,							config.projection_dim				)       ,							requires_grad=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3				)       ,							requires_grad=a				)

						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				).cpu().float().numpy()
								_UpperCamelCase						    =	cosine_distance(a       ,							self.concept_embeds				).cpu().float().numpy()

								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	image_embeds.shape[0]
								for i in range(a				):
										_UpperCamelCase						    =	{"""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
										_UpperCamelCase						    =	0.0

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

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

										result.append(a				)

								_UpperCamelCase						    =	[len(res["""bad_concepts"""]				) > 0 for res in result]

								return images, has_nsfw_concepts







						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				)
								_UpperCamelCase						    =	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
								_UpperCamelCase						    =	0.0

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

								_UpperCamelCase						    =	(cos_dist - self.concept_embeds_weights) + special_adjustment
								# concept_scores = concept_scores.round(decimals=3)
								_UpperCamelCase						    =	torch.any(concept_scores > 0       ,							dim=1				)

								return images, has_nsfw_concepts





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		if "model" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""model."""						,  """""" )
		if "norm1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm1"""						,  """attention.output.LayerNorm""" )
		if "norm2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm2"""						,  """output.LayerNorm""" )
		if "norm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm"""						,  """LayerNorm""" )
		if "transformer" in orig_key:
				_UpperCamelCase						    =	orig_key.split(""".""" )[0].split("""_""" )[-1]
				_UpperCamelCase						    =	orig_key.replace(F'transformer_{layer_num}'						,  F'encoder.layer.{layer_num}' )
		if "mha.attn" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha.attn"""						,  """attention.self""" )
		if "mha" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha"""						,  """attention""" )
		if "W_q" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_q"""						,  """self.query""" )
		if "W_k" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_k"""						,  """self.key""" )
		if "W_v" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_v"""						,  """self.value""" )
		if "ff1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff1"""						,  """intermediate.dense""" )
		if "ff2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff2"""						,  """output.dense""" )
		if "ff" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff"""						,  """output.dense""" )
		if "mlm_class" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm.mlm_class"""						,  """cls.predictions.decoder""" )
		if "mlm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm"""						,  """cls.predictions.transform""" )
		if "cls" not in orig_key:
				_UpperCamelCase						    =	"""yoso.""" + orig_key

		return orig_key


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				_UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						_UpperCamelCase						    =	val

		_UpperCamelCase						    =	orig_state_dict["""cls.predictions.decoder.bias"""]
		_UpperCamelCase						    =	torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2

		return orig_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )["""model_state_dict"""]
		_UpperCamelCase						    =	YosoConfig.from_json_file(lowerCAmelCase )
		_UpperCamelCase						    =	YosoForMaskedLM(lowerCAmelCase )

		_UpperCamelCase						    =	convert_checkpoint_helper(config.max_position_embeddings						,  lowerCAmelCase )

		print(model.load_state_dict(lowerCAmelCase ) )
		model.eval()
		model.save_pretrained(lowerCAmelCase )

		print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



