

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_nllb import NllbTokenizer
else:
  lowercase_   							=  None


lowercase_   							=  logging.get_logger(__name__)


lowercase_   							=  {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase_   							=  {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

lowercase_   							=  {
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
lowercase_   							=  ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    VOCAB_FILES_NAMES
  _a		     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a		     =    PRETRAINED_VOCAB_FILES_MAP
  _a		     =    ["""input_ids""", """attention_mask"""]
  _a		     =    NllbTokenizer

  _a		     =    []
  _a		     =    []




  def __init__(							self				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase="<s>"				,  lowerCAmelCase="</s>"				,  lowerCAmelCase="</s>"				,  lowerCAmelCase="<s>"				,  lowerCAmelCase="<unk>"				,  lowerCAmelCase="<pad>"				,  lowerCAmelCase="<mask>"				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase=False				,  **lowerCAmelCase				,  )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=AddedToken(lowerCAmelCase				,  lstrip=lowerCAmelCase				,  rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase				,  lowerCAmelCase   ) else mask_token
        _lowercase      			=legacy_behaviour
        super().__init__(
            vocab_file=lowerCAmelCase				,  tokenizer_file=lowerCAmelCase				,  bos_token=lowerCAmelCase				,  eos_token=lowerCAmelCase				,  sep_token=lowerCAmelCase				,  cls_token=lowerCAmelCase				,  unk_token=lowerCAmelCase				,  pad_token=lowerCAmelCase				,  mask_token=lowerCAmelCase				,  src_lang=lowerCAmelCase				,  tgt_lang=lowerCAmelCase				,  additional_special_tokens=lowerCAmelCase				,  legacy_behaviour=lowerCAmelCase				,  **lowerCAmelCase				,  )

        _lowercase      			=vocab_file
        _lowercase      			=False if not self.vocab_file else True

        _lowercase      			=FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              _additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}   )
        _lowercase      			={
            lang_code: self.convert_tokens_to_ids(lowerCAmelCase   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        _lowercase      			=src_lang if src_lang is not None else 'eng_Latn'
        _lowercase      			=self.convert_tokens_to_ids(self._src_lang   )
        _lowercase      			=tgt_lang
        self.set_src_lang_special_tokens(self._src_lang   )




  @property
  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        return self._src_lang




  @src_lang.setter
  def     A__	(							self				,  lowerCAmelCase   )    ->					None:
        '''simple docstring'''






        _lowercase      			=new_src_lang
        self.set_src_lang_special_tokens(self._src_lang   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = None   )    ->					List[int]:
        '''simple docstring'''






        if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = None   )    ->					List[int]:
        '''simple docstring'''






        _lowercase      			=[self.sep_token_id]
        _lowercase      			=[self.cls_token_id]

        if token_ids_a is None:
              return len(cls + token_ids_a + sep   ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        if src_lang is None or tgt_lang is None:
              raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
        _lowercase      			=src_lang
        _lowercase      			=self(lowerCAmelCase				,  add_special_tokens=lowerCAmelCase				,  return_tensors=lowerCAmelCase				,  **lowerCAmelCase   )
        _lowercase      			=self.convert_tokens_to_ids(lowerCAmelCase   )
        _lowercase      			=tgt_lang_id
        return inputs




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = "eng_Latn"				,  lowerCAmelCase = None				,  lowerCAmelCase = "fra_Latn"				,  **lowerCAmelCase				,  )    ->					BatchEncoding:
        '''simple docstring'''






        _lowercase      			=src_lang
        _lowercase      			=tgt_lang
        return super().prepare_seqaseq_batch(lowerCAmelCase				,  lowerCAmelCase				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        return self.set_src_lang_special_tokens(self.src_lang   )




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        return self.set_tgt_lang_special_tokens(self.tgt_lang   )




  def     A__	(							self				,  lowerCAmelCase   )    ->					None:
        '''simple docstring'''






        _lowercase      			=self.convert_tokens_to_ids(lowerCAmelCase   )

        if self.legacy_behaviour:
              _lowercase      			=[]
              _lowercase      			=[self.eos_token_id, self.cur_lang_code]
        else:
              _lowercase      			=[self.cur_lang_code]
              _lowercase      			=[self.eos_token_id]

        _lowercase      			=self.convert_ids_to_tokens(self.prefix_tokens   )
        _lowercase      			=self.convert_ids_to_tokens(self.suffix_tokens   )

        _lowercase      			=processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str				,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str				,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,  self.prefix_tokens + self.suffix_tokens   )   )				,  )




  def     A__	(							self				,  lowerCAmelCase   )    ->					None:
        '''simple docstring'''






        _lowercase      			=self.convert_tokens_to_ids(lowerCAmelCase   )
        if self.legacy_behaviour:
              _lowercase      			=[]
              _lowercase      			=[self.eos_token_id, self.cur_lang_code]
        else:
              _lowercase      			=[self.cur_lang_code]
              _lowercase      			=[self.eos_token_id]

        _lowercase      			=self.convert_ids_to_tokens(self.prefix_tokens   )
        _lowercase      			=self.convert_ids_to_tokens(self.suffix_tokens   )

        _lowercase      			=processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str				,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str				,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,  self.prefix_tokens + self.suffix_tokens   )   )				,  )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = None   )    ->					Tuple[str]:
        '''simple docstring'''






        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                  'tokenizer.'   )

        if not os.path.isdir(lowerCAmelCase   ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''   )
              return
        _lowercase      			=os.path.join(
            lowerCAmelCase				,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

        if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase   ):
              copyfile(self.vocab_file				,  lowerCAmelCase   )

        return (out_vocab_file,)





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


