"""simple docstring"""







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


_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      """Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""






class     _SCREAMING_SNAKE_CASE       ( lowerCAmelCase_							):



   '''simple docstring'''



   __UpperCAmelCase  :       Optional[int]						=						"""google/flan-t5-base"""
   __UpperCAmelCase  :       Optional[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."""
   )
   __UpperCAmelCase  :       Union[str, Any]						=						"""text_qa"""
   __UpperCAmelCase  :       int						=						AutoTokenizer
   __UpperCAmelCase  :       Optional[Any]						=						AutoModelForSeqaSeqLM

   __UpperCAmelCase  :       int						=						["""text""", """text"""]
   __UpperCAmelCase  :       Optional[int]						=						["""text"""]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				QA_PROMPT.format(text=__SCREAMING_SNAKE_CASE   ,							question=__SCREAMING_SNAKE_CASE  )
      return self.pre_processor(__SCREAMING_SNAKE_CASE   ,							return_tensors='pt'  )







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.model.generate(**__SCREAMING_SNAKE_CASE  )

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

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







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







"""simple docstring"""







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '''--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'''
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/






