





'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : list    ,       UpperCamelCase__    : list    ,       UpperCamelCase__    : int		)		->  list:
    A       :       Dict							=						len(UpperCamelCase__		)
    A       :       Any							=						[[0] * n for i in range(UpperCamelCase__		)]
    for i in range(UpperCamelCase__		):
        A       :       int							=						y_points[i]

    for i in range(2    ,       UpperCamelCase__		):
        for j in range(UpperCamelCase__    ,       UpperCamelCase__		):
            A       :       List[Any]							=						(
                (xa - x_points[j - i + 1]) * q[j][i - 1]
                - (xa - x_points[j]) * q[j - 1][i - 1]
            ) / (x_points[j] - x_points[j - i + 1])

    return [q[n - 1][n - 1], q]


if __name__ == "__main__":
     import doctest

     doctest.testmod()







'''simple docstring'''






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


snake_case_     =	["""text""", """image""", """audio"""]
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    A       :       int							=						[]

    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(3_000		)		)
        elif isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
            inputs.append(create_inputs(UpperCamelCase__		)		)
        else:
            raise ValueError(F'''Invalid type requested: {input_type}'''		)

    return inputs
def 							_lowerCamelCase(  UpperCamelCase__    : List		)		->  Tuple:
    A       :       Optional[int]							=						[]

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

    return output_types





@is_tool_test
class   _lowercase						:


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''inputs'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''outputs'''					)					)

       A       :       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					)

       A       :       Tuple							=						self.tool.outputs
       for _output in outputs:
           self.assertTrue(_output in authorized_types					)


   def 					snake_case				( self					):
       A       :       Any							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       # There is a single output
       if len(self.tool.outputs					) == 1:
           A       :       Optional[int]							=						[outputs]

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


   def 					snake_case				( self					):
       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 					snake_case				( self					):
       A       :       List[str]							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       int							=						[outputs]

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

       for output, output_type in zip(_UpperCAmelCase    ,   self.tool.outputs					):
           A       :       Tuple							=						AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)					)


   def 					snake_case				( self					):
       A       :       Tuple							=						create_inputs(self.tool.inputs					)

       A       :       Dict							=						[]

       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
       A       :       List[Any]							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       Any							=						[outputs]

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