

import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
    from PIL import Image
class        _A						(     a__):
  SCREAMING_SNAKE_CASE			:					Optional[Any]				=     '''naver-clova-ix/donut-base-finetuned-docvqa'''
  SCREAMING_SNAKE_CASE			:					Dict				=     (
      '''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
      '''should be the document containing the information, as well as a `question` that is the question about the '''
      '''document. It returns a text that contains the answer to the question.'''
  )
  SCREAMING_SNAKE_CASE			:					Dict				=     '''document_qa'''
  SCREAMING_SNAKE_CASE			:					str				=     AutoProcessor
  SCREAMING_SNAKE_CASE			:					int				=     VisionEncoderDecoderModel

  SCREAMING_SNAKE_CASE			:					List[str]				=     ['''image''', '''text''']
  SCREAMING_SNAKE_CASE			:					List[str]				=     ['''text''']

  def __init__( self       ,    *_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        if not is_vision_available():
              raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'    )

        super().__init__(*__lowerCAmelCase       ,    **__lowerCAmelCase    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[Any]							=   '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
        SCREAMING_SNAKE_CASE_ :     str							=   task_prompt.replace('{user_input}'       ,    __lowerCAmelCase    )
        SCREAMING_SNAKE_CASE_ :     Any							=   self.pre_processor.tokenizer(
            __lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_tensors='pt'    ).input_ids
        SCREAMING_SNAKE_CASE_ :     Dict							=   self.pre_processor(__lowerCAmelCase       ,    return_tensors='pt'    ).pixel_values

        return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        return self.model.generate(
            inputs['pixel_values'].to(self.device    )       ,    decoder_input_ids=inputs['decoder_input_ids'].to(self.device    )       ,    max_length=self.model.decoder.config.max_position_embeddings       ,    early_stopping=__lowerCAmelCase       ,    pad_token_id=self.pre_processor.tokenizer.pad_token_id       ,    eos_token_id=self.pre_processor.tokenizer.eos_token_id       ,    use_cache=__lowerCAmelCase       ,    num_beams=1       ,    bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]       ,    return_dict_in_generate=__lowerCAmelCase       ,    ).sequences



  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Any							=   self.pre_processor.batch_decode(__lowerCAmelCase    )[0]
        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   sequence.replace(self.pre_processor.tokenizer.eos_token       ,    ''    )
        SCREAMING_SNAKE_CASE_ :     List[str]							=   sequence.replace(self.pre_processor.tokenizer.pad_token       ,    ''    )
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   re.sub(r'<.*?>'       ,    ''       ,    __lowerCAmelCase       ,    count=1    ).strip()  # remove first task start token
        SCREAMING_SNAKE_CASE_ :     Tuple							=   self.pre_processor.tokenajson(__lowerCAmelCase    )

        return sequence["answer"]







'''simple docstring'''

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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  __a: Tuple          =   None


__a: Tuple          =   logging.get_logger(__name__)

__a: Optional[Any]          =   {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__a: Optional[Any]          =   {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__a: Tuple          =   {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE							=    PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE							=    ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE							=    TaTokenizer

       SCREAMING_SNAKE_CASE							=    []

       def __init__(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase="</s>"	,   __lowerCAmelCase="<unk>"	,   __lowerCAmelCase="<pad>"	,   __lowerCAmelCase=100	,   __lowerCAmelCase=None	,   **__lowerCAmelCase	,   )  ->  Union[str, Any]:
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
         lowercase__				:    Union[str, Any]  =					[F"""<extra_id_{i}>""" for i in range(__lowerCAmelCase )]
        elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra special tokens
         lowercase__				:    Dict  =					len(set(filter(lambda __lowerCAmelCase : bool('''extra_id_''' in str(__lowerCAmelCase ) )	,   __lowerCAmelCase ) ) )
         if extra_tokens != extra_ids:
          raise ValueError(
              F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
              ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
              ''' tokens''' )

        super().__init__(
            __lowerCAmelCase	,   tokenizer_file=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   extra_ids=__lowerCAmelCase	,   additional_special_tokens=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

        lowercase__				:    Union[str, Any]  =					vocab_file
        lowercase__				:    Optional[int]  =					False if not self.vocab_file else True
        lowercase__				:    Any  =					extra_ids

       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Union[str, Any]:
        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         lowercase__				:    Any  =					TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
         if init_max_model_length is not None and init_max_model_length != max_model_length:
          return init_max_model_length
         elif init_max_model_length is None:
          warnings.warn(
              '''This tokenizer was incorrectly instantiated with a model max length of'''
              F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
              ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
              ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
              F""" {pretrained_model_name_or_path} automatically truncating your input to"""
              F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
              F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
              ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
              ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''	,   __lowerCAmelCase	,   )

        return max_model_length

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  Tuple[str]:
        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__				:    List[Any]  =					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 )
         logger.info(F"""Copy vocab file to {out_vocab_file}""" )

        return (out_vocab_file,)

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Any  =					token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
        else:
         lowercase__				:    Dict  =					token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Optional[int]  =					[self.eos_token_id]

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

       def _lowerCAmelCase(						self )  ->  List[Any]:
        return list(
            set(filter(lambda __lowerCAmelCase : bool(re.search(r'''<extra_id_\d+>'''	,   __lowerCAmelCase ) ) is not None	,   self.additional_special_tokens ) ) )


       def _lowerCAmelCase(						self )  ->  Tuple:
        return [self.convert_tokens_to_ids(__lowerCAmelCase ) for token in self.get_sentinel_tokens()]

