


"""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


lowerCamelCase_  :  Any     		=    """Here is a text containing a lot of information: '''{text}'''.

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






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    "google/flan-t5-base"
 __lowerCAmelCase					   =    (
     "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					   =    "text_qa"
 __lowerCAmelCase					   =    AutoTokenizer
 __lowerCAmelCase					   =    AutoModelForSeqaSeqLM

 __lowerCAmelCase					   =    ["text", "text"]
 __lowerCAmelCase					   =    ["text"]


 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	List[str]:
     a											=QA_PROMPT.format(text=__A					,       question=__A		)
     return self.pre_processor(__A					,       return_tensors='''pt'''		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	List[Any]:
     a											=self.model.generate(**__A		)

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

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





 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	int:
     return self.pre_processor.decode(__A					,       skip_special_tokens=__A					,       clean_up_tokenization_spaces=__A		)



"""simple docstring"""







def _A		(		lowercase						):







    """simple docstring"""
    a											=[]
    a											=1

    for i in range(len(lowercase						)						):
        if i + 1 < len(lowercase						) and text[i] == text[i + 1]:
            count += 1
        else:
            encoded.append((text[i], count)						)
            a											=1

    return encoded
def _A		(		lowercase						):







    """simple docstring"""
    return "".join(char * length for char, length in encoded						)


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="""run_length_encode""", verbose=True)
       testmod(name="""run_length_decode""", verbose=True)