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


UpperCamelCase    				=		'''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

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



class     snake_case_			(						lowerCAmelCase_			):
   __A      :						Any   =							'''google/flan-t5-base'''
   __A      :						Optional[int]   =							(
       '''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.'''
   )
   __A      :						Dict   =							'''text_qa'''
   __A      :						Optional[int]   =							AutoTokenizer
   __A      :						Optional[Any]   =							AutoModelForSeqaSeqLM

   __A      :						List[Any]   =							['''text''', '''text''']
   __A      :						Dict   =							['''text''']






   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	str      ,		lowercase_							:	str    )   ->		Union[str, Any]:
       lowercase__					:  Union[str, Any]          =							QA_PROMPT.format(text=UpperCamelCase_      ,		question=UpperCamelCase_    )
       return self.pre_processor(UpperCamelCase_      ,		return_tensors="pt"    )






   def  __UpperCamelCase	( self							:	str      ,		lowercase_							:	Tuple    )   ->		Union[str, Any]:
       lowercase__					:  Tuple          =							self.model.generate(**UpperCamelCase_    )

       lowercase__					:  List[str]          =							inputs['''input_ids'''].shape
       lowercase__					:  Dict          =							output_ids.shape[0]

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







   def  __UpperCamelCase	( self							:	Dict      ,		lowercase_							:	Union[str, Any]    )   ->		Optional[Any]:
       return self.pre_processor.decode(UpperCamelCase_      ,		skip_special_tokens=UpperCamelCase_      ,		clean_up_tokenization_spaces=UpperCamelCase_    )





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__lowercase	=    {
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''XLMProphetNetTokenizer''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMProphetNetDecoder''',
							    '''XLMProphetNetEncoder''',
							    '''XLMProphetNetForCausalLM''',
							    '''XLMProphetNetForConditionalGeneration''',
							    '''XLMProphetNetModel''',
							    '''XLMProphetNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_prophetnet import (
														    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMProphetNetDecoder,
														    XLMProphetNetEncoder,
														    XLMProphetNetForCausalLM,
														    XLMProphetNetForConditionalGeneration,
														    XLMProphetNetModel,
														    XLMProphetNetPreTrainedModel,
														)

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






