"""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


SCREAMING_SNAKE_CASE__   :      List[str]							=['text', 'image', 'audio']



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->Optional[int]:
    _lowerCamelCase	: Optional[Any]       =       []

    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(lowerCamelCase_						,	lowerCamelCase_ ):
            inputs.append(create_inputs(lowerCamelCase_ ) )
        else:
            raise ValueError(F'''Invalid type requested: {input_type}''' )

    return inputs



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->List[str]:
    _lowerCamelCase	: List[str]       =       []

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

    return output_types



@is_tool_test
class      _UpperCAmelCase						:






    """simple docstring"""







    def 				a__					(			self					)	->	Any:
        self.assertTrue(hasattr(self.tool    , '''inputs'''					)					)
        self.assertTrue(hasattr(self.tool    , '''outputs'''					)					)

        _lowerCamelCase	: List[str]       =       self.tool.inputs
        for _input in inputs:
            if isinstance(_input    , _lowercase					):
                for __input in _input:
                    self.assertTrue(__input in authorized_types					)
            else:
                self.assertTrue(_input in authorized_types					)

        _lowerCamelCase	: int       =       self.tool.outputs
        for _output in outputs:
            self.assertTrue(_output in authorized_types					)







    def 				a__					(			self					)	->	Any:
        _lowerCamelCase	: Optional[int]       =       create_inputs(self.tool.inputs					)
        _lowerCamelCase	: Any       =       self.tool(*_lowercase					)

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

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







    def 				a__					(			self					)	->	Dict:
        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 				a__					(			self					)	->	List[str]:
        _lowerCamelCase	: Optional[int]       =       create_inputs(self.tool.inputs					)
        _lowerCamelCase	: Optional[Any]       =       self.tool(*_lowercase					)

        if not isinstance(_lowercase    , _lowercase					):
            _lowerCamelCase	: str       =       [outputs]

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

        for output, output_type in zip(_lowercase    , self.tool.outputs					):
            _lowerCamelCase	: Optional[int]       =       AGENT_TYPE_MAPPING[output_type]
            self.assertTrue(isinstance(_lowercase    , _lowercase					)					)







    def 				a__					(			self					)	->	List[Any]:
        _lowerCamelCase	: Tuple       =       create_inputs(self.tool.inputs					)

        _lowerCamelCase	: List[str]       =       []

        for _input, input_type in zip(_lowercase    , self.tool.inputs					):
            if isinstance(_lowercase    , _lowercase					):
                _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	: List[Any]       =       self.tool(*_lowercase					)

        if not isinstance(_lowercase    , _lowercase					):
            _lowerCamelCase	: Union[str, Any]       =       [outputs]

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










def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     len(lowerCamelCase_							)
				lowercase__  					=     [[False] * (required_sum + 1) for _ in range(arr_len + 1							)]

				# for each arr value, a sum of zero(0) can be formed by not taking any element
				# hence True/1
				for i in range(arr_len + 1							):
								lowercase__  					=     True

				# sum is not zero and set is empty then false
				for i in range(1						,  required_sum + 1							):
								lowercase__  					=     False

				for i in range(1						,  arr_len + 1							):
								for j in range(1						,  required_sum + 1							):
												if arr[i - 1] > j:
																lowercase__  					=     subset[i - 1][j]
												if arr[i - 1] <= j:
																lowercase__  					=     subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

				return subset[arr_len][required_sum]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




