
'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


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

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

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Any								=		'''google/flan-t5-base'''
	_UpperCamelCase							:  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.'''
	)
	_UpperCamelCase							:  Dict								=		'''text_qa'''
	_UpperCamelCase							:  Optional[int]								=		AutoTokenizer
	_UpperCamelCase							:  Optional[Any]								=		AutoModelForSeqaSeqLM

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




	def 	__A      (						self				: Tuple					,						UpperCamelCase_				: str					,						UpperCamelCase_				: str      ):
			lowerCAmelCase_   :       Union[str, Any]    							=QA_PROMPT.format(text=UpperCamelCase_					,						question=UpperCamelCase_      )
			return self.pre_processor(UpperCamelCase_					,						return_tensors='''pt'''      )




	def 	__A      (						self				: str					,						UpperCamelCase_				: Tuple      ):
			lowerCAmelCase_   :       Tuple    							=self.model.generate(**UpperCamelCase_      )

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

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





	def 	__A      (						self				: Dict					,						UpperCamelCase_				: Union[str, 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_torch_available, is_vision_available


__lowercase	=    {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''DetrFeatureExtractor''']
							__lowercase	=    ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

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






