





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase							:			List[str]        =							logging.get_logger(__name__)

_UpperCAmelCase							:			List[str]        =							{"""vocab_file""": """spiece.model"""}

_UpperCAmelCase							:			Tuple        =							{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

_UpperCAmelCase							:			List[str]        =							{"""bert_for_seq_generation""": 5_12}

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						VOCAB_FILES_NAMES
 UpperCAmelCase__						=						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase__						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase__						=						[]
 UpperCAmelCase__						=						["""input_ids""", """attention_mask"""]
 def __init__( self    : Union[str, Any]     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : int="<s>"     ,	UpperCAmelCase    : Any="</s>"     ,	UpperCAmelCase    : List[Any]="<unk>"     ,	UpperCAmelCase    : Any="<pad>"     ,	UpperCAmelCase    : Dict="<::::>"     ,	UpperCAmelCase    : Optional[Dict[str, Any]] = None     ,	**UpperCAmelCase    : Any     ,	)     ->      None:
     lowerCamelCase__       : str   					=						{} if sp_model_kwargs is None else sp_model_kwargs

     # Add extra_ids to the special token list
     super().__init__(
         bos_token=UpperCAmelCase     ,	eos_token=UpperCAmelCase     ,	unk_token=UpperCAmelCase     ,	pad_token=UpperCAmelCase     ,	sep_token=UpperCAmelCase     ,	sp_model_kwargs=self.sp_model_kwargs     ,	**UpperCAmelCase     ,	)

     lowerCamelCase__       : List[str]   					=						vocab_file

     lowerCamelCase__       : List[str]   					=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(UpperCAmelCase      )
 @property
 def 	A_      ( self    : List[str]      )     ->      Optional[int]:
     return self.sp_model.get_piece_size()
 def 	A_      ( self    : Tuple      )     ->      Any:
     lowerCamelCase__       : Tuple   					=						{self.convert_ids_to_tokens(UpperCAmelCase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab
 def __getstate__( self    : Union[str, Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : List[str]   					=						self.__dict__.copy()
     lowerCamelCase__       : List[Any]   					=						None
     return state
 def __setstate__( self    : Dict     ,	UpperCAmelCase    : Optional[int]      )     ->      List[Any]:
     lowerCamelCase__       : Optional[int]   					=						d

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

     lowerCamelCase__       : Optional[int]   					=						spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(self.vocab_file      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : str      )     ->      List[str]:
     return self.sp_model.encode(UpperCAmelCase     ,	out_type=UpperCAmelCase      )
 def 	A_      ( self    : str     ,	UpperCAmelCase    : Tuple      )     ->      Union[str, Any]:
     return self.sp_model.piece_to_id(UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : Tuple      )     ->      Dict:
     lowerCamelCase__       : str   					=						self.sp_model.IdToPiece(UpperCAmelCase      )
     return token
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : int      )     ->      Union[str, Any]:
     lowerCamelCase__       : str   					=						[]
     lowerCamelCase__       : List[str]   					=						''
     for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
             out_string += self.sp_model.decode(UpperCAmelCase      ) + token
             lowerCamelCase__       : Tuple   					=						[]
         else:
             current_sub_tokens.append(UpperCAmelCase      )
     out_string += self.sp_model.decode(UpperCAmelCase      )
     return out_string.strip()







 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : Optional[str] = None      )     ->      Tuple[str]:
     if not os.path.isdir(UpperCAmelCase      ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
         return
     lowerCamelCase__       : Any   					=						os.path.join(
         UpperCAmelCase     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

     return (out_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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""philschmid/bart-large-cnn-samsum"""
 UpperCAmelCase__						=						(
     """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
     """and returns a summary of the text."""
 )
 UpperCAmelCase__						=						"""summarizer"""
 UpperCAmelCase__						=						AutoTokenizer
 UpperCAmelCase__						=						AutoModelForSeqaSeqLM

 UpperCAmelCase__						=						["""text"""]
 UpperCAmelCase__						=						["""text"""]
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      List[str]:
     return self.pre_processor(UpperCAmelCase     ,	return_tensors='pt'     ,	truncation=UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : Dict      )     ->      str:
     return self.model.generate(**UpperCAmelCase      )[0]







 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      Union[str, Any]:
     return self.pre_processor.decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase     ,	clean_up_tokenization_spaces=UpperCAmelCase      )





