

from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowerCamelCase    =      'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''







class 		__magic_name__    (							lowercase_							):

   '''simple docstring'''




   lowerCamelCase__				:			Optional[int]												=     """google/flan-t5-base"""
   lowerCamelCase__				:			Union[str, Any]												=     (
       """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
       """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
   )
   lowerCamelCase__				:			Union[str, Any]												=     """text_qa"""
   lowerCamelCase__				:			int												=     AutoTokenizer
   lowerCamelCase__				:			List[str]												=     AutoModelForSeqaSeqLM

   lowerCamelCase__				:			Union[str, Any]												=     ["""text""", """text"""]
   lowerCamelCase__				:			str												=     ["""text"""]



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					str:






     """simple docstring"""






     a__						=QA_PROMPT.format(text=lowercase_,					question=lowercase_ )
     return self.pre_processor(lowercase_,					return_tensors='''pt''' )



   def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Optional[int]:






     """simple docstring"""






     a__						=self.model.generate(**lowercase_ )

     a__,   a__						=inputs['''input_ids'''].shape
     a__						=output_ids.shape[0]

     return output_ids.reshape(lowercase_,					out_b // in_b,					*output_ids.shape[1:] )[0][0]



   def 				_UpperCAmelCase				(       self,					lowercase_ )							->					List[Any]:






     """simple docstring"""






     return self.pre_processor.decode(lowercase_,					skip_special_tokens=lowercase_,					clean_up_tokenization_spaces=lowercase_ )



import os
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_xglm import XGLMTokenizer
else:
       lowerCamelCase     =      None


lowerCamelCase     =      logging.get_logger(__name__)

lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase     =      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase     =      {
    '''facebook/xglm-564M''': 2048,
}







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
   lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
   lowerCamelCase__				:			Any												=     XGLMTokenizer



   def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:






     """simple docstring"""






     a__						=7
     a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

     a__						=kwargs.get('''additional_special_tokens''',					[] )
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)

     a__						=vocab_file
     a__						=False if not self.vocab_file else True



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
     a__						=[self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     a__						=[self.sep_token_id]

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



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:






     """simple docstring"""






     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(lowercase_ ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
       return
     a__						=os.path.join(
         lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
       copyfile(self.vocab_file,					lowercase_ )

     return (out_vocab_file,)
