





'''simple docstring'''

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=True				,						__UpperCAmelCase="pt"   )    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							{"""add_prefix_space""": True} if isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ) and not line.startswith(""" """   ) else {}
   __SCREAMING_SNAKE_CASE   				=							padding_side
   return tokenizer(
       [line]				,						max_length=__UpperCAmelCase				,						padding="""max_length""" if pad_to_max_length else None				,						truncation=__UpperCAmelCase				,						return_tensors=__UpperCAmelCase				,						add_special_tokens=__UpperCAmelCase				,						**__UpperCAmelCase				,						)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						)    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							input_ids.ne(__UpperCAmelCase   ).any(dim=0   )
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class __a			( _snake_case      ):





   def __init__(       self					:      List[str]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Tuple="train"					,lowerCamelCase					:      str=None					,lowerCamelCase					:      Union[str, Any]=None					,lowerCamelCase					:      str=None					,lowerCamelCase					:      Optional[int]=""					,):

      '''simple docstring'''

      super().__init__()
      __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ).joinpath(type_path + """.source"""     )
      __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ).joinpath(type_path + """.target"""     )
      __SCREAMING_SNAKE_CASE   				=							self.get_char_lens(self.src_file     )
      __SCREAMING_SNAKE_CASE   				=							max_source_length
      __SCREAMING_SNAKE_CASE   				=							max_target_length
      assert min(self.src_lens     ) > 0, f"""found empty line in {self.src_file}"""
      __SCREAMING_SNAKE_CASE   				=							tokenizer
      __SCREAMING_SNAKE_CASE   				=							prefix
      if n_obs is not None:
         __SCREAMING_SNAKE_CASE   				=							self.src_lens[:n_obs]
      __SCREAMING_SNAKE_CASE   				=							src_lang
      __SCREAMING_SNAKE_CASE   				=							tgt_lang





   def __len__(       self					:      Optional[int]     ):

      '''simple docstring'''

      return len(self.src_lens     )





   def __getitem__(       self					:      Union[str, Any]					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							index + 1  # linecache starts at 1
      __SCREAMING_SNAKE_CASE   				=							self.prefix + linecache.getline(str(self.src_file     )					,lowerCamelCase     ).rstrip("""\n"""     )
      __SCREAMING_SNAKE_CASE   				=							linecache.getline(str(self.tgt_file     )					,lowerCamelCase     ).rstrip("""\n"""     )
      assert source_line, f"""empty source line for index {index}"""
      assert tgt_line, f"""empty tgt line for index {index}"""

      # Need to add eos token manually for T5
      if isinstance(self.tokenizer					,lowerCamelCase     ):
         source_line += self.tokenizer.eos_token
         tgt_line += self.tokenizer.eos_token

      # Pad source and target to the right
      __SCREAMING_SNAKE_CASE   				=							(
          self.tokenizer.question_encoder if isinstance(self.tokenizer					,lowerCamelCase     ) else self.tokenizer
      )
      __SCREAMING_SNAKE_CASE   				=							self.tokenizer.generator if isinstance(self.tokenizer					,lowerCamelCase     ) else self.tokenizer

      __SCREAMING_SNAKE_CASE   				=							encode_line(lowerCamelCase					,lowerCamelCase					,self.max_source_length					,"""right"""     )
      __SCREAMING_SNAKE_CASE   				=							encode_line(lowerCamelCase					,lowerCamelCase					,self.max_target_length					,"""right"""     )

      __SCREAMING_SNAKE_CASE   				=							source_inputs["""input_ids"""].squeeze()
      __SCREAMING_SNAKE_CASE   				=							target_inputs["""input_ids"""].squeeze()
      __SCREAMING_SNAKE_CASE   				=							source_inputs["""attention_mask"""].squeeze()
      return {
          "input_ids": source_ids,
          "attention_mask": src_mask,
          "decoder_input_ids": target_ids,
      }





   @staticmethod
   def   UpperCAmelCase__  (       lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      return [len(lowerCamelCase     ) for x in Path(lowerCamelCase     ).open().readlines()]





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							torch.stack([x["""input_ids"""] for x in batch]     )
      __SCREAMING_SNAKE_CASE   				=							torch.stack([x["""attention_mask"""] for x in batch]     )
      __SCREAMING_SNAKE_CASE   				=							torch.stack([x["""decoder_input_ids"""] for x in batch]     )
      __SCREAMING_SNAKE_CASE   				=							(
          self.tokenizer.generator.pad_token_id
          if isinstance(self.tokenizer					,lowerCamelCase     )
          else self.tokenizer.pad_token_id
      )
      __SCREAMING_SNAKE_CASE   				=							(
          self.tokenizer.question_encoder.pad_token_id
          if isinstance(self.tokenizer					,lowerCamelCase     )
          else self.tokenizer.pad_token_id
      )
      __SCREAMING_SNAKE_CASE   				=							trim_batch(lowerCamelCase					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							trim_batch(lowerCamelCase					,lowerCamelCase					,attention_mask=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{
          """input_ids""": source_ids,
          """attention_mask""": source_mask,
          """decoder_input_ids""": y,
      }
      return batch





a   	=    getLogger(__name__)

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







   return list(itertools.chain.from_iterable(__UpperCAmelCase   )   )

def     __magic_name__							(				__UpperCAmelCase   )    ->       None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							get_git_info()
   save_json(__UpperCAmelCase				,						os.path.join(__UpperCAmelCase				,						"""git_log.json"""   )   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=4				,						**__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   with open(__UpperCAmelCase				,						"""w"""   ) as f:
      json.dump(__UpperCAmelCase				,						__UpperCAmelCase				,						indent=__UpperCAmelCase				,						**__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   with open(__UpperCAmelCase   ) as f:
      return json.load(__UpperCAmelCase   )

def     __magic_name__							(				)    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							git.Repo(search_parent_directories=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							{
       """repo_id""": str(__UpperCAmelCase   ),
       """repo_sha""": str(repo.head.object.hexsha   ),
       """repo_branch""": str(repo.active_branch   ),
       """hostname""": str(socket.gethostname()   ),
   }
   return repo_infos

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List:

   '''simple docstring'''







   return list(map(__UpperCAmelCase				,						__UpperCAmelCase   )   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   with open(__UpperCAmelCase				,						"""wb"""   ) as f:
      return pickle.dump(__UpperCAmelCase				,						__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   def remove_articles(__UpperCAmelCase   ):
      return re.sub(R"""\b(a|an|the)\b"""				,						""" """				,						__UpperCAmelCase   )

   def white_space_fix(__UpperCAmelCase   ):
      return " ".join(text.split()   )

   def remove_punc(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							set(string.punctuation   )
      return "".join(ch for ch in text if ch not in exclude   )

   def lower(__UpperCAmelCase   ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(__UpperCAmelCase   )   )   )   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							normalize_answer(__UpperCAmelCase   ).split()
   __SCREAMING_SNAKE_CASE   				=							normalize_answer(__UpperCAmelCase   ).split()
   __SCREAMING_SNAKE_CASE   				=							Counter(__UpperCAmelCase   ) & Counter(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							sum(common.values()   )
   if num_same == 0:
      return 0
   __SCREAMING_SNAKE_CASE   				=							1.0 * num_same / len(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							1.0 * num_same / len(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							(2 * precision * recall) / (precision + recall)
   return fa

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   return normalize_answer(__UpperCAmelCase   ) == normalize_answer(__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   assert len(__UpperCAmelCase   ) == len(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							0
   for hypo, pred in zip(__UpperCAmelCase				,						__UpperCAmelCase   ):
      em += exact_match_score(__UpperCAmelCase				,						__UpperCAmelCase   )
   if len(__UpperCAmelCase   ) > 0:
      em /= len(__UpperCAmelCase   )
   return {"em": em}

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







   return model_prefix.startswith("""rag"""   )







def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							{p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   __SCREAMING_SNAKE_CASE   				=							"""dropout_rate"""
   for p in extra_params:
      if getattr(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   ):
         if not hasattr(__UpperCAmelCase				,						__UpperCAmelCase   ) and not hasattr(__UpperCAmelCase				,						equivalent_param[p]   ):
            logger.info("""config doesn't have a `{}` attribute""".format(__UpperCAmelCase   )   )
            delattr(__UpperCAmelCase				,						__UpperCAmelCase   )
            continue
         __SCREAMING_SNAKE_CASE   				=							p if hasattr(__UpperCAmelCase				,						__UpperCAmelCase   ) else equivalent_param[p]
         setattr(__UpperCAmelCase				,						__UpperCAmelCase				,						getattr(__UpperCAmelCase				,						__UpperCAmelCase   )   )
         delattr(__UpperCAmelCase				,						__UpperCAmelCase   )
   return hparams, config








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

from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)
def 	a (     A__      :  Optional[int] ,  A__      :  Union[str, Any]	)       -> int:





      """simple docstring"""



      _lowercase      			=nn.functional.normalize(A__	)
      _lowercase      			=nn.functional.normalize(A__	)
      return torch.mm(A__ ,  normalized_text_embeds.t()	)







class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    CLIPConfig

  _a		     =    ["""CLIPEncoderLayer"""]




  def __init__(							self				,  lowerCAmelCase   )    ->					Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase   )

        _lowercase      			=CLIPVisionModel(config.vision_config   )
        _lowercase      			=nn.Linear(config.vision_config.hidden_size				,  config.projection_dim				,  bias=lowerCAmelCase   )

        _lowercase      			=nn.Parameter(torch.ones(17				,  config.projection_dim   )				,  requires_grad=lowerCAmelCase   )
        _lowercase      			=nn.Parameter(torch.ones(3				,  config.projection_dim   )				,  requires_grad=lowerCAmelCase   )

        _lowercase      			=nn.Parameter(torch.ones(17   )				,  requires_grad=lowerCAmelCase   )
        _lowercase      			=nn.Parameter(torch.ones(3   )				,  requires_grad=lowerCAmelCase   )




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.vision_model(lowerCAmelCase   )[1]  # pooled_output
        _lowercase      			=self.visual_projection(lowerCAmelCase   )

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

        _lowercase      			=[]
        _lowercase      			=image_embeds.shape[0]
        for i in range(lowerCAmelCase   ):
              _lowercase      			={'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
              _lowercase      			=0.0

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

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

              result.append(lowerCAmelCase   )

        _lowercase      			=[len(res['bad_concepts']   ) > 0 for res in result]

        return images, has_nsfw_concepts




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=self.vision_model(lowerCAmelCase   )[1]  # pooled_output
        _lowercase      			=self.visual_projection(lowerCAmelCase   )

        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.special_care_embeds   )
        _lowercase      			=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
        _lowercase      			=0.0

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

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

        return images, has_nsfw_concepts


