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#
# 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


SCREAMING_SNAKE_CASE														=  'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
class 							A_      (  __lowercase     ):






 '''simple docstring'''




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

 _SCREAMING_SNAKE_CASE							:     Optional[int]	=   ["text", "text"]
 _SCREAMING_SNAKE_CASE							:     Union[str, Any]	=   ["text"]


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

     """simple docstring"""



     _UpperCAmelCase			: int     					=			QA_PROMPT.format(text=_A							,					question=_A)
     return self.pre_processor(_A							,					return_tensors='''pt''')


 def 	snake_case__			(		self							,					_A)	->						int:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			self.model.generate(**_A)

     _UpperCAmelCase			: Optional[Any]     					=			inputs['''input_ids'''].shape
     _UpperCAmelCase			: Union[str, Any]     					=			output_ids.shape[0]

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


 def 	snake_case__			(		self							,					_A)	->						str:

     """simple docstring"""



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












from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def        _lowerCamelCase		(  __A      :							str						,						__A      :							List[str]=3	) ->   List[Any]:
    _UpperCAmelCase			: Tuple     					=			int(mask / 2	)
    # copy image size
    _UpperCAmelCase			: Any     					=			zeros_like(__A	)
    for i in range(__A						,						gray_img.shape[0] - bd	):
        for j in range(__A						,						gray_img.shape[1] - bd	):
            # get mask according with mask
            _UpperCAmelCase			: Any     					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _UpperCAmelCase			: List[str]     					=			sort(__A	)[inta(divide((multiply(__A						,						__A	))						,						2	) + 1	)]
            _UpperCAmelCase			: int     					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  SCREAMING_SNAKE_CASE								=  imread('../image_data/lena.jpg')
  # turn image in gray scale value
  SCREAMING_SNAKE_CASE								=  cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 3)
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 5)

  # show result images
  imshow('median filter with 3x3 mask', medianaxa)
  imshow('median filter with 5x5 mask', medianaxa)
  waitKey(0)




