import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__lowerCamelCase			: Any							   =		logging.get_logger(__name__)

__lowerCamelCase			: Optional[int]							   =		'''▁'''

__lowerCamelCase			: Tuple							   =		{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

__lowerCamelCase			: Any							   =		{
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

__lowerCamelCase			: Optional[int]							   =		{
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

__lowerCamelCase			: str							   =		['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

__lowerCamelCase			: Tuple							   =		{'''mustc''': MUSTC_LANGS}







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							MAX_MODEL_INPUT_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]

   lowerCAmelCase_														=							[]




   def __init__(    self			:				Any       ,	_lowercase			:				Tuple       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Dict="<s>"       ,	_lowercase			:				Dict="</s>"       ,	_lowercase			:				Tuple="<pad>"       ,	_lowercase			:				str="<unk>"       ,	_lowercase			:				Optional[Any]=False       ,	_lowercase			:				Any=False       ,	_lowercase			:				List[str]=None       ,	_lowercase			:				Optional[Any]=None       ,	_lowercase			:				Optional[Dict[str, Any]] = None       ,	**_lowercase			:				List[Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=_lowercase       ,	eos_token=_lowercase       ,	unk_token=_lowercase       ,	pad_token=_lowercase       ,	do_upper_case=_lowercase       ,	do_lower_case=_lowercase       ,	tgt_lang=_lowercase       ,	lang_codes=_lowercase       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**_lowercase       ,	)
     SCREAMING_SNAKE_CASE__										=      do_upper_case
     SCREAMING_SNAKE_CASE__										=      do_lower_case

     SCREAMING_SNAKE_CASE__										=      load_json(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      {v: k for k, v in self.encoder.items()}
     SCREAMING_SNAKE_CASE__										=      spm_file
     SCREAMING_SNAKE_CASE__										=      load_spm(_lowercase       ,	self.sp_model_kwargs      )

     if lang_codes is not None:
       SCREAMING_SNAKE_CASE__										=      lang_codes
       SCREAMING_SNAKE_CASE__										=      LANGUAGES[lang_codes]
       SCREAMING_SNAKE_CASE__										=      [f"""<lang:{lang}>""" for lang in self.langs]
       SCREAMING_SNAKE_CASE__										=      {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""      ) for lang in self.langs}

       SCREAMING_SNAKE_CASE__										=      self.lang_tokens
       SCREAMING_SNAKE_CASE__										=      tgt_lang if tgt_lang is not None else self.langs[0]

       self.set_tgt_lang_special_tokens(self._tgt_lang      )
     else:
       SCREAMING_SNAKE_CASE__										=      {}




   @property
   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     return len(self.encoder      )




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return self._tgt_lang




   @tgt_lang.setter
   def 	__a						(    self			:				str       ,	_lowercase			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      new_tgt_lang
     self.set_tgt_lang_special_tokens(_lowercase      )




   def 	__a						(    self			:				Dict       ,	_lowercase			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.lang_code_to_id[tgt_lang]
     SCREAMING_SNAKE_CASE__										=      [lang_code_id]




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				str      ):



     """simple docstring"""






     return self.sp_model.encode(_lowercase       ,	out_type=_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				List[str]      ):



     """simple docstring"""






     return self.encoder.get(_lowercase       ,	self.encoder[self.unk_token]      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				int      ):



     """simple docstring"""






     return self.decoder.get(_lowercase       ,	self.unk_token      )




   def 	__a						(    self			:				str       ,	_lowercase			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      []
     SCREAMING_SNAKE_CASE__										=      """"""
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         SCREAMING_SNAKE_CASE__										=      self.sp_model.decode(_lowercase      )
         out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
         SCREAMING_SNAKE_CASE__										=      []
       else:
         current_sub_tokens.append(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      self.sp_model.decode(_lowercase      )
     out_string += decoded.upper() if self.do_upper_case else decoded
     return out_string.strip()




   def 	__a						(    self			:				int       ,	_lowercase			:				str       ,	_lowercase			:				List[Any]=None      ):



     """simple docstring"""






     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + [self.eos_token_id]
     # 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.eos_token_id]




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				List[int]       ,	_lowercase			:				Optional[List[int]] = None       ,	_lowercase			:				bool = False      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      [1] * len(self.prefix_tokens      )
     SCREAMING_SNAKE_CASE__										=      [1]
     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			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.encoder.copy()
     vocab.update(self.added_tokens_encoder      )
     return vocab




   def __getstate__(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.__dict__.copy()
     SCREAMING_SNAKE_CASE__										=      None
     return state




   def __setstate__(    self			:				Dict       ,	_lowercase			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      d

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

     SCREAMING_SNAKE_CASE__										=      load_spm(self.spm_file       ,	self.sp_model_kwargs      )




   def 	__a						(    self			:				int       ,	_lowercase			:				str       ,	_lowercase			:				Optional[str] = None      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      Path(_lowercase      )
     assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
     SCREAMING_SNAKE_CASE__										=      save_dir / (
         (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
     )
     SCREAMING_SNAKE_CASE__										=      save_dir / (
         (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
     )

     save_json(self.encoder       ,	_lowercase      )

     if os.path.abspath(self.spm_file      ) != os.path.abspath(_lowercase      ) and os.path.isfile(self.spm_file      ):
       copyfile(self.spm_file       ,	_lowercase      )
     elif not os.path.isfile(self.spm_file      ):
       with open(_lowercase       ,	"""wb"""      ) as fi:
         SCREAMING_SNAKE_CASE__										=      self.sp_model.serialized_model_proto()
         fi.write(_lowercase      )

     return (str(_lowercase      ), str(_lowercase      ))





def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       Dict[str, Any]	)							->					sentencepiece.SentencePieceProcessor:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      sentencepiece.SentencePieceProcessor(**__UpperCamelCase	)
  spm.Load(str(__UpperCamelCase	)	)
  return spm



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str	)							->					Union[Dict, List]:







  """simple docstring"""





  with open(__UpperCamelCase ,     """r"""	) as f:
    return json.load(__UpperCamelCase	)






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       str	)							->					None:







  """simple docstring"""





  with open(__UpperCamelCase ,     """w"""	) as f:
    json.dump(__UpperCamelCase ,     __UpperCamelCase ,     indent=2	)

# 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 typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"Salesforce/blip-image-captioning-base"
   lowerCAmelCase_														=							(
       "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
       "image to caption, and returns a text that contains the description in English."
   )
   lowerCAmelCase_														=							"image_captioner"
   lowerCAmelCase_														=							AutoModelForVisionaSeq

   lowerCAmelCase_														=							["image"]
   lowerCAmelCase_														=							["text"]




   def __init__(    self			:				List[Any]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     requires_backends(self       ,	["""vision"""]      )
     super().__init__(*_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				"Image"      ):



     """simple docstring"""






     return self.pre_processor(images=_lowercase       ,	return_tensors="""pt"""      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     return self.model.generate(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Any      ):



     """simple docstring"""






     return self.pre_processor.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )[0].strip()
