




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

import sentencepiece as spm

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


__a							     =					logging.get_logger(__name__)

__a							     =					'▁'

__a							     =					{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

__a							     =					{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

__a							     =					{'vinai/bartpho-syllable': 1_024}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = VOCAB_FILES_NAMES
    lowerCAmelCase         = PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase         = ['''input_ids''', '''attention_mask''']





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE="<s>"  ,_SCREAMING_SNAKE_CASE="</s>"  ,_SCREAMING_SNAKE_CASE="</s>"  ,_SCREAMING_SNAKE_CASE="<s>"  ,_SCREAMING_SNAKE_CASE="<unk>"  ,_SCREAMING_SNAKE_CASE="<pad>"  ,_SCREAMING_SNAKE_CASE="<mask>"  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						None:
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCAmelCase_     :				List[Any]  =     AddedToken(_SCREAMING_SNAKE_CASE  ,lstrip=_SCREAMING_SNAKE_CASE  ,rstrip=_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) else mask_token

         UpperCAmelCase_     :				Any  =     {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_SCREAMING_SNAKE_CASE  ,eos_token=_SCREAMING_SNAKE_CASE  ,unk_token=_SCREAMING_SNAKE_CASE  ,sep_token=_SCREAMING_SNAKE_CASE  ,cls_token=_SCREAMING_SNAKE_CASE  ,pad_token=_SCREAMING_SNAKE_CASE  ,mask_token=_SCREAMING_SNAKE_CASE  ,sp_model_kwargs=self.sp_model_kwargs  ,**_SCREAMING_SNAKE_CASE  ,)

         UpperCAmelCase_     :				str  =     vocab_file
         UpperCAmelCase_     :				Any  =     monolingual_vocab_file
         UpperCAmelCase_     :				int  =     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(_SCREAMING_SNAKE_CASE    )    )

         # Load the reduced vocab

         # Keep order of special tokens for backward compatibility
         UpperCAmelCase_     :				str  =     {}
         UpperCAmelCase_     :				Any  =     0
         for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
              if str(_SCREAMING_SNAKE_CASE    ) not in self.fairseq_tokens_to_ids:
                   UpperCAmelCase_     :				str  =     cnt
                   cnt += 1
         with open(_SCREAMING_SNAKE_CASE  ,'''r'''  ,encoding='''utf-8'''    ) as f:
              for line in f.readlines():
                   UpperCAmelCase_     :				int  =     line.strip().split()[0]
                   UpperCAmelCase_     :				Tuple  =     len(self.fairseq_tokens_to_ids    )
         if str(_SCREAMING_SNAKE_CASE    ) not in self.fairseq_tokens_to_ids:
              UpperCAmelCase_     :				Tuple  =     len(self.fairseq_tokens_to_ids    )

         UpperCAmelCase_     :				Union[str, Any]  =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}





    def __getstate__( self    )					->						str:
         UpperCAmelCase_     :				Any  =     self.__dict__.copy()
         UpperCAmelCase_     :				Optional[Any]  =     None
         UpperCAmelCase_     :				Tuple  =     self.sp_model.serialized_model_proto()
         return state





    def __setstate__( self  ,_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         UpperCAmelCase_     :				List[str]  =     d

         # for backward compatibility
         if not hasattr(self  ,'''sp_model_kwargs'''    ):
              UpperCAmelCase_     :				Optional[Any]  =     {}

         UpperCAmelCase_     :				str  =     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None    )					->						List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCAmelCase_     :				Any  =     [self.cls_token_id]
         UpperCAmelCase_     :				Optional[Any]  =     [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = False    )					->						List[int]:

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

         if token_ids_a is None:
              return [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]
         return [1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE    )) + [1]





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None    )					->						List[int]:
         UpperCAmelCase_     :				Dict  =     [self.sep_token_id]
         UpperCAmelCase_     :				Dict  =     [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]





    @property
    def 				a__ ( self    )					->						Optional[Any]:
         return len(self.fairseq_ids_to_tokens    )





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				List[str]  =     {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						List[str]:
         return self.sp_model.encode(_SCREAMING_SNAKE_CASE  ,out_type=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         else:
              return self.unk_token_id





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						int:
         return self.fairseq_ids_to_tokens[index]





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         UpperCAmelCase_     :				Union[str, Any]  =     ''''''.join(_SCREAMING_SNAKE_CASE    ).replace(_SCREAMING_SNAKE_CASE  ,''' '''    ).strip()
         return out_string





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None    )					->						Tuple[str]:
         if not os.path.isdir(_SCREAMING_SNAKE_CASE    ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
              return
         UpperCAmelCase_     :				Tuple  =     os.path.join(
             _SCREAMING_SNAKE_CASE  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
         UpperCAmelCase_     :				List[str]  =     os.path.join(
             _SCREAMING_SNAKE_CASE  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']  ,)

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

         if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
             _SCREAMING_SNAKE_CASE    ) and os.path.isfile(self.monolingual_vocab_file    ):
              copyfile(self.monolingual_vocab_file  ,_SCREAMING_SNAKE_CASE    )
         elif not os.path.isfile(self.monolingual_vocab_file    ):
              with open(_SCREAMING_SNAKE_CASE  ,'''w'''  ,encoding='''utf-8'''    ) as fp:
                   for token in self.fairseq_tokens_to_ids:
                        if token not in self.all_special_tokens:
                             fp.write(f'''{str(_SCREAMING_SNAKE_CASE    )} \n'''    )

         return out_vocab_file, out_monolingual_vocab_file





# 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     __a(	_a  ):






    """simple docstring"""





    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  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						Dict:
         requires_backends(self  ,['''vision''']    )
         super().__init__(*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         with torch.no_grad():
              UpperCAmelCase_     :				Dict  =     self.model(**_SCREAMING_SNAKE_CASE    ).logits
         return logits





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict:
         UpperCAmelCase_     :				Dict  =     outputs.cpu().detach().numpy()
         UpperCAmelCase_     :				Any  =     0
         UpperCAmelCase_     :				List[Any]  =     1
         return Image.fromarray((array * 255).astype(np.uinta    )    )