





'''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__		:    Union[str, Any] =       'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  Optional[Any]		       =					'google/flan-t5-base'
       _snake_case			:  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.'
       )
       _snake_case			:  Optional[int]		       =					'text_qa'
       _snake_case			:  int		       =					AutoTokenizer
       _snake_case			:  Optional[Any]		       =					AutoModelForSeqaSeqLM

       _snake_case			:  Tuple		       =					['text', 'text']
       _snake_case			:  Any		       =					['text']





       def snake_case__		(							self     :	Optional[Any] ,				lowerCAmelCase__     :	str ,				lowerCAmelCase__     :	str  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= QA_PROMPT.format(text=lowerCAmelCase__ ,				question=lowerCAmelCase__  )
            return self.pre_processor(lowerCAmelCase__ ,				return_tensors='''pt'''  )





       def snake_case__		(							self     :	int ,				lowerCAmelCase__     :	Dict  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase       					= self.model.generate(**lowerCAmelCase__  )

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

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







       def snake_case__		(							self     :	Optional[int] ,				lowerCAmelCase__     :	Optional[int]  )					->  Union[str, Any]:

            '''simple docstring'''




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










'''simple docstring'''

import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput
@dataclass
class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  Union[List[PIL.Image.Image], np.ndarray]

class        __lowerCAmelCase  (					unittest.TestCase				):






       """simple docstring"""





       def snake_case__		(							self     :	Any  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= CustomOutput(images=np.random.rand(1 ,				3 ,				4 ,				4  )  )

            # check every way of getting the attribute
            assert isinstance(outputs.images ,				np.ndarray  )
            assert outputs.images.shape == (1, 3, 4, 4)
            assert isinstance(outputs['''images'''] ,				np.ndarray  )
            assert outputs["images"].shape == (1, 3, 4, 4)
            assert isinstance(outputs[0] ,				np.ndarray  )
            assert outputs[0].shape == (1, 3, 4, 4)

            # test with a non-tensor attribute
            _UpperCamelCase       					= CustomOutput(images=[PIL.Image.new('''RGB''' ,				(4, 4)  )]  )

            # check every way of getting the attribute
            assert isinstance(outputs.images ,				lowerCAmelCase__  )
            assert isinstance(outputs.images[0] ,				PIL.Image.Image  )
            assert isinstance(outputs['''images'''] ,				lowerCAmelCase__  )
            assert isinstance(outputs['''images'''][0] ,				PIL.Image.Image  )
            assert isinstance(outputs[0] ,				lowerCAmelCase__  )
            assert isinstance(outputs[0][0] ,				PIL.Image.Image  )







       def snake_case__		(							self     :	List[str]  )					->  Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       					= CustomOutput({'''images''': np.random.rand(1 ,				3 ,				4 ,				4  )}  )

            # check every way of getting the attribute
            assert isinstance(outputs.images ,				np.ndarray  )
            assert outputs.images.shape == (1, 3, 4, 4)
            assert isinstance(outputs['''images'''] ,				np.ndarray  )
            assert outputs["images"].shape == (1, 3, 4, 4)
            assert isinstance(outputs[0] ,				np.ndarray  )
            assert outputs[0].shape == (1, 3, 4, 4)

            # test with a non-tensor attribute
            _UpperCamelCase       					= CustomOutput({'''images''': [PIL.Image.new('''RGB''' ,				(4, 4)  )]}  )

            # check every way of getting the attribute
            assert isinstance(outputs.images ,				lowerCAmelCase__  )
            assert isinstance(outputs.images[0] ,				PIL.Image.Image  )
            assert isinstance(outputs['''images'''] ,				lowerCAmelCase__  )
            assert isinstance(outputs['''images'''][0] ,				PIL.Image.Image  )
            assert isinstance(outputs[0] ,				lowerCAmelCase__  )
            assert isinstance(outputs[0][0] ,				PIL.Image.Image  )



