

# 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


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

class      _a				( __UpperCAmelCase							):
      _lowerCamelCase					: List[str]					=					'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					: Any					=					'text_qa'
      _lowerCamelCase					: Tuple					=					AutoTokenizer
      _lowerCamelCase					: str					=					AutoModelForSeqaSeqLM

      _lowerCamelCase					: Optional[Any]					=					['text', 'text']
      _lowerCamelCase					: List[str]					=					['text']





      def 		__A  (      self       :							str	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Tuple  ):
            A_	     =							QA_PROMPT.format(text=lowercase_	, question=lowercase_  )
            return self.pre_processor(lowercase_	, return_tensors="pt"  )





      def 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							Union[str, Any]  ):
            A_	     =							self.model.generate(**lowercase_  )

            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 		__A  (      self       :							int	, UpperCAmelCase       :							List[Any]  ):
            return self.pre_processor.decode(lowercase_	, skip_special_tokens=lowercase_	, clean_up_tokenization_spaces=lowercase_  )


import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


__a						:Optional[int]            = logging.get_logger(__name__)

class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							List[Any]	, *UpperCAmelCase       :							Any	, **UpperCAmelCase       :							int  ):
            warnings.warn(
                "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                " Please use MobileNetV2ImageProcessor instead."	, UpperCAmelCase	, )
            super().__init__(*UpperCAmelCase	, **UpperCAmelCase  )