


'''simple docstring'''


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


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





class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


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

							A_							:   Dict   			=						["""text""", """text"""]
							A_							:   Dict   			=						["""text"""]






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   QA_PROMPT.format(text=__snake_case					,      question=__snake_case						)
											return self.pre_processor(__snake_case					,      return_tensors="""pt"""						)






							def UpperCAmelCase_			(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.model.generate(**__snake_case						)

											_SCREAMING_SNAKE_CASE				: Any  	=   inputs["""input_ids"""].shape
											_SCREAMING_SNAKE_CASE				: List[str]  	=   output_ids.shape[0]

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






							def UpperCAmelCase_			(					self					,      __snake_case						):
											return self.pre_processor.decode(__snake_case					,      skip_special_tokens=__snake_case					,      clean_up_tokenization_spaces=__snake_case						)








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_	:     Union[str, Any]							     =	{'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Dict							     =	['PegasusTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	['PegasusTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	[
				    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'PegasusForCausalLM',
				    'PegasusForConditionalGeneration',
				    'PegasusModel',
				    'PegasusPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[Any]							     =	[
				    'TFPegasusForConditionalGeneration',
				    'TFPegasusModel',
				    'TFPegasusPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'FlaxPegasusForConditionalGeneration',
				    'FlaxPegasusModel',
				    'FlaxPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus import PegasusTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus_fast import PegasusTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_pegasus import (
								    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    PegasusForCausalLM,
								    PegasusForConditionalGeneration,
								    PegasusModel,
								    PegasusPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_pegasus import (
								    FlaxPegasusForConditionalGeneration,
								    FlaxPegasusModel,
								    FlaxPegasusPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     List[str]							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




