




'''simple docstring'''
from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
A							:				List[str]			     =	TypeVar("""T""", bound="""Enum""")






@unique
class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					Dict   = 80
       snake_case_			:					Optional[Any]   = 70
       snake_case_			:					List[str]   = 60
       snake_case_			:					Tuple   = 50
       snake_case_			:					Optional[int]   = 40
       snake_case_			:					List[Any]   = 30
       snake_case_			:					Any   = 20
       snake_case_			:					Optional[Any]   = 10






@unique
class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					List[str]   = 24
       snake_case_			:					Optional[int]   = 21
       snake_case_			:					Tuple   = 18
       snake_case_			:					Tuple   = 15
       snake_case_			:					Dict   = 12
       snake_case_			:					Optional[int]   = 9
       snake_case_			:					List[Any]   = 6
       snake_case_			:					Optional[Any]   = 3
       snake_case_			:					Optional[int]   = 2
       snake_case_			:					str   = 1
       snake_case_			:					Union[str, Any]   = -1
       snake_case_			:					Tuple   = -2
       snake_case_			:					List[Any]   = -3
       snake_case_			:					Optional[int]   = -6
       snake_case_			:					List[str]   = -9
       snake_case_			:					Union[str, Any]   = -12
       snake_case_			:					List[Any]   = -15
       snake_case_			:					List[Any]   = -18
       snake_case_			:					Dict   = -21
       snake_case_			:					List[str]   = -24

       @classmethod
       def    snake_case__      (   cls					)   -> dict:

           """simple docstring"""



           return {unit.name: unit.value for unit in cls if unit.value > 0}




       @classmethod
       def    snake_case__      (   cls					)   -> dict:

           """simple docstring"""



           return {unit.name: unit.value for unit in cls if unit.value < 0}

def     snake_case_  (       a__		:     float      ):



    """simple docstring"""




    __lowercase          =						SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
    for name_prefix, value_prefix in prefixes.items():
        __lowercase          =						value / (10**value_prefix)
        if numerical_part > 1:
            return f'{numerical_part!s} {name_prefix}'
    return str(a__      )
def     snake_case_  (       a__		:     float      ):



    """simple docstring"""




    for prefix in BinaryUnit:
        __lowercase          =						value / (2**prefix.value)
        if numerical_part > 1:
            return f'{numerical_part!s} {prefix.name}'
    return str(a__      )


if __name__ == "__main__":
  import doctest

  doctest.testmod()










'''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


A							:				Dict			     =	"""Here is a text containing a lot of information: '''{text}'''.

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






class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					List[Any]   = """google/flan-t5-base"""
       snake_case_			:					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."""
       )
       snake_case_			:					Optional[int]   = """text_qa"""
       snake_case_			:					List[Any]   = AutoTokenizer
       snake_case_			:					List[str]   = AutoModelForSeqaSeqLM

       snake_case_			:					int   = ["""text""", """text"""]
       snake_case_			:					Dict   = ["""text"""]

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						QA_PROMPT.format(text=lowerCamelCase__ ,		question=lowerCamelCase__					)
           return self.pre_processor(lowerCamelCase__ ,		return_tensors="""pt"""					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> int:

           """simple docstring"""



           __lowercase          =						self.model.generate(**lowerCamelCase__					)

           __lowercase    ,__lowercase          =						inputs["""input_ids"""].shape
           __lowercase          =						output_ids.shape[0]

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




       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Tuple:

           """simple docstring"""



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




