





"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			=							logging.get_logger(__name__)

lowerCAmelCase			=							'''▁'''

lowerCAmelCase			=							{'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase			=							{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCAmelCase			=							{
    '''facebook/nllb-200-distilled-600M''': 1_024,
}

# fmt: off
lowerCAmelCase			=							['''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					(      _UpperCAmelCase							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  ["""input_ids""", """attention_mask"""]

       lowerCAmelCase__						 =  []
       lowerCAmelCase__						 =  []






       def __init__(   self    ,  a__    ,  a__="<s>"    ,  a__="</s>"    ,  a__="</s>"    ,  a__="<s>"    ,  a__="<unk>"    ,  a__="<pad>"    ,  a__="<mask>"    ,  a__=None    ,  a__=None    ,  a__=None    ,  a__ = None    ,  a__=None    ,  a__=False    ,  **a__    ,  ):
              _UpperCAmelCase     =     AddedToken(lowercase__    ,  lstrip=lowercase__    ,  rstrip=lowercase__     ) if isinstance(lowercase__    ,  lowercase__     ) else mask_token

              _UpperCAmelCase     =     {} if sp_model_kwargs is None else sp_model_kwargs
              _UpperCAmelCase     =     legacy_behaviour

              super().__init__(
                  bos_token=lowercase__    ,  eos_token=lowercase__    ,  unk_token=lowercase__    ,  sep_token=lowercase__    ,  cls_token=lowercase__    ,  pad_token=lowercase__    ,  mask_token=lowercase__    ,  tokenizer_file=lowercase__    ,  src_lang=lowercase__    ,  tgt_lang=lowercase__    ,  additional_special_tokens=lowercase__    ,  sp_model_kwargs=self.sp_model_kwargs    ,  legacy_behaviour=lowercase__    ,  **lowercase__    ,  )

              _UpperCAmelCase     =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
              self.sp_model.Load(str(lowercase__     )     )
              _UpperCAmelCase     =     vocab_file
              # Original fairseq vocab and spm vocab must be "aligned":
              # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
              # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
              # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
              # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

              # Mimic fairseq token-to-id alignment for the first 4 token
              _UpperCAmelCase     =     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

              # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
              _UpperCAmelCase     =     1

              _UpperCAmelCase     =     len(self.sp_model     )
              _UpperCAmelCase     =     {
                  code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__     )
              }
              _UpperCAmelCase     =     {v: k for k, v in self.lang_code_to_id.items()}
              _UpperCAmelCase     =     len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

              self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
              _UpperCAmelCase     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
              _UpperCAmelCase     =     list(self.lang_code_to_id.keys()     )

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

              _UpperCAmelCase     =     src_lang if src_lang is not None else "eng_Latn"
              _UpperCAmelCase     =     self.lang_code_to_id[self._src_lang]
              _UpperCAmelCase     =     tgt_lang
              self.set_src_lang_special_tokens(self._src_lang     )






       def __getstate__(   self     ):
              _UpperCAmelCase     =     self.__dict__.copy()
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     self.sp_model.serialized_model_proto()
              return state






       def __setstate__(   self    ,  a__     ):
              _UpperCAmelCase     =     d

              # for backward compatibility
              if not hasattr(self    ,  'sp_model_kwargs'     ):
                     _UpperCAmelCase     =     {}

              _UpperCAmelCase     =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )






       @property
       def 							__A (   self     ):
              return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token






       @property
       def 							__A (   self     ):
              return self._src_lang






       @src_lang.setter
       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     new_src_lang
              self.set_src_lang_special_tokens(self._src_lang     )






       def 							__A (   self    ,  a__    ,  a__ = None    ,  a__ = False     ):






              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=lowercase__    ,  token_ids_a=lowercase__    ,  already_has_special_tokens=lowercase__     )

              _UpperCAmelCase     =     [1] * len(self.prefix_tokens     )
              _UpperCAmelCase     =     [1] * len(self.suffix_tokens     )
              if token_ids_a is None:
                     return prefix_ones + ([0] * len(lowercase__     )) + suffix_ones
              return prefix_ones + ([0] * len(lowercase__     )) + ([0] * len(lowercase__     )) + suffix_ones






       def 							__A (   self    ,  a__    ,  a__ = None     ):






              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    ,  a__    ,  a__ = None     ):
              _UpperCAmelCase     =     [self.sep_token_id]
              _UpperCAmelCase     =     [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    ,  a__    ,  a__    ,  a__    ,  a__    ,  **a__     ):






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






       def 							__A (   self     ):
              _UpperCAmelCase     =     {self.convert_ids_to_tokens(lowercase__     ): i for i in range(self.vocab_size     )}
              vocab.update(self.added_tokens_encoder     )
              return vocab






       def 							__A (   self    ,  a__     ):
              return self.sp_model.encode(lowercase__    ,  out_type=lowercase__     )






       def 							__A (   self    ,  a__     ):






              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              _UpperCAmelCase     =     self.sp_model.PieceToId(lowercase__     )

              # Need to return unknown token if the SP model returned 0
              return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






       def 							__A (   self    ,  a__     ):






              if index in self.fairseq_ids_to_tokens:
                     return self.fairseq_ids_to_tokens[index]
              return self.sp_model.IdToPiece(index - self.fairseq_offset     )






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     "".join(lowercase__     ).replace(lowercase__    ,  ' '     ).strip()
              return out_string






       def 							__A (   self    ,  a__    ,  a__ = None     ):






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

              if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowercase__     ) and os.path.isfile(self.vocab_file     ):
                     copyfile(self.vocab_file    ,  lowercase__     )
              elif not os.path.isfile(self.vocab_file     ):
                     with open(lowercase__    ,  'wb'     ) as fi:
                            _UpperCAmelCase     =     self.sp_model.serialized_model_proto()
                            fi.write(lowercase__     )

              return (out_vocab_file,)






       def 							__A (   self    ,  a__    ,  a__ = "eng_Latn"    ,  a__ = None    ,  a__ = "fra_Latn"    ,  **a__    ,  ):
              _UpperCAmelCase     =     src_lang
              _UpperCAmelCase     =     tgt_lang
              return super().prepare_seqaseq_batch(lowercase__    ,  lowercase__    ,  **lowercase__     )






       def 							__A (   self     ):
              return self.set_src_lang_special_tokens(self.src_lang     )






       def 							__A (   self     ):
              return self.set_tgt_lang_special_tokens(self.tgt_lang     )






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     self.lang_code_to_id[src_lang]
              if self.legacy_behaviour:
                     _UpperCAmelCase     =     []
                     _UpperCAmelCase     =     [self.eos_token_id, self.cur_lang_code]
              else:
                     _UpperCAmelCase     =     [self.cur_lang_code]
                     _UpperCAmelCase     =     [self.eos_token_id]






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     self.lang_code_to_id[lang]
              if self.legacy_behaviour:
                     _UpperCAmelCase     =     []
                     _UpperCAmelCase     =     [self.eos_token_id, self.cur_lang_code]
              else:
                     _UpperCAmelCase     =     [self.cur_lang_code]
                     _UpperCAmelCase     =     [self.eos_token_id]













"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
    from PIL import Image



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  (
           """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
           """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
           """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
       )
       lowerCAmelCase__						 =  """CIDAS/clipseg-rd64-refined"""
       lowerCAmelCase__						 =  """image_segmenter"""
       lowerCAmelCase__						 =  CLIPSegForImageSegmentation

       lowerCAmelCase__						 =  ["""image""", """text"""]
       lowerCAmelCase__						 =  ["""image"""]






       def __init__(   self    ,  *a__    ,  **a__     ):
              requires_backends(self    ,  ['vision']     )
              super().__init__(*a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__     ):
              return self.pre_processor(text=[label]    ,  images=[image]    ,  padding=a__    ,  return_tensors='pt'     )






       def 							__A (   self    ,  a__     ):
              with torch.no_grad():
                     _UpperCAmelCase     =     self.model(**a__     ).logits
              return logits






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     outputs.cpu().detach().numpy()
              _UpperCAmelCase     =     0
              _UpperCAmelCase     =     1
              return Image.fromarray((array * 2_55).astype(np.uinta     )     )






