





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 		UpperCamelCase__			(				SCREAMING_SNAKE_CASE__       ):



     '''simple docstring'''

     __snake_case   :     str		     =      "naver-clova-ix/donut-base-finetuned-docvqa"
     __snake_case   :     List[Any]		     =      (
         "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."
     )
     __snake_case   :     Optional[Any]		     =      "document_qa"
     __snake_case   :     Any		     =      AutoProcessor
     __snake_case   :     List[str]		     =      VisionEncoderDecoderModel

     __snake_case   :     Union[str, Any]		     =      ["image", "text"]
     __snake_case   :     int		     =      ["text"]






     def __init__(							self						:   Any						,*lowerCamelCase__						:   Union[str, Any]						,**lowerCamelCase__						:   List[Any]	)					->		List[str]:




           '''simple docstring'''


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

           super().__init__(*lowerCamelCase__						,**lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   "Image"						,lowerCamelCase__						:   str	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
           SCREAMING_SNAKE_CASE		=						task_prompt.replace("""{user_input}"""						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.pre_processor.tokenizer(
               lowerCamelCase__						,add_special_tokens=lowerCamelCase__						,return_tensors="""pt"""	).input_ids
           SCREAMING_SNAKE_CASE		=						self.pre_processor(lowerCamelCase__						,return_tensors="""pt"""	).pixel_values

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		List[Any]:




           '''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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,lowerCamelCase__						:   Optional[int]	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.pre_processor.batch_decode(lowerCamelCase__	)[0]
           SCREAMING_SNAKE_CASE		=						sequence.replace(self.pre_processor.tokenizer.eos_token						,""""""	)
           SCREAMING_SNAKE_CASE		=						sequence.replace(self.pre_processor.tokenizer.pad_token						,""""""	)
           SCREAMING_SNAKE_CASE		=						re.sub(R"""<.*?>"""						,""""""						,lowerCamelCase__						,count=1	).strip()  # remove first task start token
           SCREAMING_SNAKE_CASE		=						self.pre_processor.tokenajson(lowerCamelCase__	)

           return sequence["answer"]









from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_						       =	TypeVar("""DatasetType""", Dataset, IterableDataset)



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "first_exhausted"			,						)			->       DatasetType:


      '''simple docstring'''


      from .arrow_dataset import Dataset
      from .iterable_dataset import IterableDataset

      if not datasets:
            raise ValueError("""Unable to interleave an empty list of datasets."""     )
      for i, dataset in enumerate(_SCREAMING_SNAKE_CASE     ):
            if not isinstance(_SCREAMING_SNAKE_CASE			,						(Dataset, IterableDataset)     ):
                  if isinstance(_SCREAMING_SNAKE_CASE			,						(DatasetDict, IterableDatasetDict)     ):
                        if not dataset:
                              raise ValueError(
                                  F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                  """is an empty dataset dictionary."""     )
                        raise ValueError(
                            F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE     )}\n"""
                            F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE     )     )}']"""     )
                  raise ValueError(
                      F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE     ).__name__}."""     )
            if i == 0:
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
                      (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  raise ValueError(
                      F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
      if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
            raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""     )
      if dataset_type is Dataset:
            return _interleave_map_style_datasets(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						stopping_strategy=_SCREAMING_SNAKE_CASE     )
      else:
            return _interleave_iterable_datasets(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						stopping_strategy=_SCREAMING_SNAKE_CASE     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = 0			,						)			->       DatasetType:


      '''simple docstring'''


      if not dsets:
            raise ValueError("""Unable to concatenate an empty list of datasets."""     )
      for i, dataset in enumerate(_SCREAMING_SNAKE_CASE     ):
            if not isinstance(_SCREAMING_SNAKE_CASE			,						(Dataset, IterableDataset)     ):
                  if isinstance(_SCREAMING_SNAKE_CASE			,						(DatasetDict, IterableDatasetDict)     ):
                        if not dataset:
                              raise ValueError(
                                  F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                  """is an empty dataset dictionary."""     )
                        raise ValueError(
                            F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE     )}\n"""
                            F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE     )     )}']"""     )
                  raise ValueError(
                      F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE     ).__name__}."""     )
            if i == 0:
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						(
                      (Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ) else (IterableDataset, Dataset)
                  )
            elif not isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  raise ValueError(
                      F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
      if dataset_type is Dataset:
            return _concatenate_map_style_datasets(_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						axis=_SCREAMING_SNAKE_CASE     )
      else:
            return _concatenate_iterable_datasets(_SCREAMING_SNAKE_CASE			,						info=_SCREAMING_SNAKE_CASE			,						split=_SCREAMING_SNAKE_CASE			,						axis=_SCREAMING_SNAKE_CASE     )


