





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


__A									=				["text", "image", "audio"]





def      lowerCAmelCase_ (       __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Dict			  =[]

  for input_type in input_types:
    if input_type == "text":
      inputs.append("Text input"		)
    elif input_type == "image":
      inputs.append(
          Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"		).resize((512, 512)		)		)
    elif input_type == "audio":
      inputs.append(torch.ones(3000		)		)
    elif isinstance(__a	,    __a		):
      inputs.append(create_inputs(__a		)		)
    else:
      raise ValueError(F"""Invalid type requested: {input_type}"""		)

  return inputs





def      lowerCAmelCase_ (       __a		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Any			  =[]

  for output in outputs:
    if isinstance(__a	,    (str, AgentText)		):
      output_types.append("text"		)
    elif isinstance(__a	,    (Image.Image, AgentImage)		):
      output_types.append("image"		)
    elif isinstance(__a	,    (torch.Tensor, AgentAudio)		):
      output_types.append("audio"		)
    else:
      raise ValueError(F"""Invalid output: {output}"""		)

  return output_types






@is_tool_test
class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->List[Any]:



   '''simple docstring'''



   self.assertTrue(hasattr(self.tool       , "inputs"))
   self.assertTrue(hasattr(self.tool       , "outputs"))

   lowerCamelCase__:      List[Any]			  =self.tool.inputs
   for _input in inputs:
     if isinstance(_input       , UpperCAmelCase_):
       for __input in _input:
         self.assertTrue(__input in authorized_types)
     else:
       self.assertTrue(_input in authorized_types)

   lowerCamelCase__:      List[str]			  =self.tool.outputs
   for _output in outputs:
     self.assertTrue(_output in authorized_types)

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =create_inputs(self.tool.inputs)
   lowerCamelCase__:      Tuple			  =self.tool(*UpperCAmelCase_)

   # There is a single output
   if len(self.tool.outputs) == 1:
     lowerCamelCase__:      Tuple			  =[outputs]

   self.assertListEqual(output_types(UpperCAmelCase_)       , self.tool.outputs)

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->Dict:



   '''simple docstring'''



   self.assertTrue(hasattr(self.tool       , "description"))
   self.assertTrue(hasattr(self.tool       , "default_checkpoint"))
   self.assertTrue(self.tool.description.startswith("This is a tool that"))

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



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =create_inputs(self.tool.inputs)
   lowerCamelCase__:      Tuple			  =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_       , UpperCAmelCase_):
     lowerCamelCase__:      Optional[int]			  =[outputs]

   self.assertEqual(len(UpperCAmelCase_)       , len(self.tool.outputs))

   for output, output_type in zip(UpperCAmelCase_       , self.tool.outputs):
     lowerCamelCase__:      Optional[Any]			  =AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(UpperCAmelCase_       , UpperCAmelCase_))







 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =create_inputs(self.tool.inputs)

   lowerCamelCase__:      Union[str, Any]			  =[]

   for _input, input_type in zip(UpperCAmelCase_       , self.tool.inputs):
     if isinstance(UpperCAmelCase_       , UpperCAmelCase_):
       _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])
     else:
       _inputs.append(AGENT_TYPE_MAPPING[input_type](_input))

        # Should not raise an error
   lowerCamelCase__:      Optional[int]			  =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_       , UpperCAmelCase_):
     lowerCamelCase__:      int			  =[outputs]

   self.assertEqual(len(UpperCAmelCase_)       , len(self.tool.outputs))








from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def      lowerCAmelCase_ (       __a	,    __a	,    __a = 10**-10		)     ->		float:







  """simple docstring"""





  lowerCamelCase__:      str			  =a
  while True:
    lowerCamelCase__:      Optional[Any]			  =Decimal(__a		) - (
        Decimal(eval(__a		)		) / Decimal(eval(str(diff(__a		)		)		)		)  # noqa: S307
    )
    # This number dictates the accuracy of the answer
    if abs(eval(__a		)		) < precision:  # noqa: S307
      return float(__a		)


# Let's Execute
if __name__ == "__main__":
     # Find root of trigonometric function
     # Find value of pi
     print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
     # Find root of polynomial
     print(f'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
     # Find Square Root of 5
     print(f'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
     # Exponential Roots
     print(f'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')

