


'''simple docstring'''

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

from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)






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







     '''simple docstring'''






     _a						=		nn.functional.normalize(__lowerCamelCase				)
     _a						=		nn.functional.normalize(__lowerCamelCase				)
     return torch.mm(__lowerCamelCase      ,  normalized_text_embeds.t()				)






class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				CLIPConfig

     UpperCAmelCase 					=				['''CLIPEncoderLayer''']






     def __init__(			self     ,							__UpperCamelCase		)       ->    Any:
          super().__init__(__UpperCamelCase		)

          _a						=		CLIPVisionModel(config.vision_config		)
          _a						=		nn.Linear(config.vision_config.hidden_size     ,							config.projection_dim     ,							bias=__UpperCamelCase		)

          _a						=		nn.Parameter(torch.ones(17     ,							config.projection_dim		)     ,							requires_grad=__UpperCamelCase		)
          _a						=		nn.Parameter(torch.ones(3     ,							config.projection_dim		)     ,							requires_grad=__UpperCamelCase		)

          _a						=		nn.Parameter(torch.ones(17		)     ,							requires_grad=__UpperCamelCase		)
          _a						=		nn.Parameter(torch.ones(3		)     ,							requires_grad=__UpperCamelCase		)






     @torch.no_grad()
     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Any:
          _a						=		self.vision_model(__UpperCamelCase		)[1]  # pooled_output
          _a						=		self.visual_projection(__UpperCamelCase		)

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

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

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

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

               result.append(__UpperCamelCase		)

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

          return images, has_nsfw_concepts






     @torch.no_grad()
     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[Any]:
          _a						=		self.vision_model(__UpperCamelCase		)[1]  # pooled_output
          _a						=		self.visual_projection(__UpperCamelCase		)

          _a						=		cosine_distance(__UpperCamelCase     ,							self.special_care_embeds		)
          _a						=		cosine_distance(__UpperCamelCase     ,							self.concept_embeds		)

          # increase this value to create a stronger `nsfw` filter
          # at the cost of increasing the possibility of filtering benign images
          _a						=		0.0

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

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

          return images, has_nsfw_concepts





'''simple docstring'''

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Dict=1024				)      ->     str:







     '''simple docstring'''






     _a			,			_a						=		[], []

     _a						=		list(zip(__lowerCamelCase      ,  __lowerCamelCase				)				)
     _a			,			_a						=		sorted_examples[0]

     def is_too_big(__lowerCamelCase		: Union[str, Any]				):
          return tok(__lowerCamelCase      ,  return_tensors="pt"				).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]				):
          _a						=		new_src + " " + src
          _a						=		new_tgt + " " + tgt
          if is_too_big(__lowerCamelCase				) or is_too_big(__lowerCamelCase				):  # cant fit, finalize example
               finished_src.append(__lowerCamelCase				)
               finished_tgt.append(__lowerCamelCase				)
               _a			,			_a						=		src, tgt
          else:  # can fit, keep adding
               _a			,			_a						=		cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(__lowerCamelCase				)
          finished_tgt.append(__lowerCamelCase				)
     return finished_src, finished_tgt






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Path      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		Path(__lowerCamelCase				)
     save_path.mkdir(exist_ok=__lowerCamelCase				)
     for split in ["train"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a			,			_a						=		pack_examples(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          print(F"packed {split} split from {len(__lowerCamelCase				)} examples -> {len(__lowerCamelCase				)}."				)
          Path(save_path / F"{split}.source"				).open("w"				).write("\n".join(__lowerCamelCase				)				)
          Path(save_path / F"{split}.target"				).open("w"				).write("\n".join(__lowerCamelCase				)				)
     for split in ["val", "test"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          shutil.copyfile(__lowerCamelCase      ,  save_path / F"{split}.source"				)
          shutil.copyfile(__lowerCamelCase      ,  save_path / F"{split}.target"				)






def __UpperCamelCase   (			)      ->     Any:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--tok_name"      ,  type=__lowerCamelCase      ,  help="like facebook/bart-large-cnn,t5-base, etc."				)
     parser.add_argument("--max_seq_len"      ,  type=__lowerCamelCase      ,  default=128				)
     parser.add_argument("--data_dir"      ,  type=__lowerCamelCase				)
     parser.add_argument("--save_path"      ,  type=__lowerCamelCase				)
     _a						=		parser.parse_args()
     _a						=		AutoTokenizer.from_pretrained(args.tok_name				)
     return pack_data_dir(__lowerCamelCase      ,  Path(args.data_dir				)      ,  args.max_seq_len      ,  args.save_path				)


if __name__ == "__main__":
   packer_cli()

