

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


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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''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(_lowerCamelCase ,   _lowerCamelCase   ):
            inputs.append(create_inputs(_lowerCamelCase   )   )
        else:
            raise ValueError(f"""Invalid type requested: {input_type}"""   )

    return inputs

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    []

    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    :

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

      _lowerCAmelCase       :   str  =    self.tool.inputs
      for _input in inputs:
          if isinstance(_input    ,_A      ):
              for __input in _input:
                  self.assertTrue(__input in authorized_types      )
          else:
              self.assertTrue(_input in authorized_types      )

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    create_inputs(self.tool.inputs      )
      _lowerCAmelCase       :   str  =    self.tool(*_A      )

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

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

  def __lowerCamelCase		(		self      ):
      '''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 __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    create_inputs(self.tool.inputs      )
      _lowerCAmelCase       :   List[Any]  =    self.tool(*_A      )

      if not isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   List[Any]  =    [outputs]

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

      for output, output_type in zip(_A    ,self.tool.outputs      ):
          _lowerCAmelCase       :   Dict  =    AGENT_TYPE_MAPPING[output_type]
          self.assertTrue(isinstance(_A    ,_A      )      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    create_inputs(self.tool.inputs      )

      _lowerCAmelCase       :   int  =    []

      for _input, input_type in zip(_A    ,self.tool.inputs      ):
          if isinstance(_A    ,_A      ):
              _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(*_A      )

      if not isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   Dict  =    [outputs]

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








"""simple docstring"""






from __future__ import annotations

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    create_state_space_tree(_lowerCamelCase ,   [] ,   0 ,   [0 for i in range(len(_lowerCamelCase   )   )]   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    if index == len(_lowerCamelCase   ):
        print(_lowerCamelCase   )
        return

    for i in range(len(_lowerCamelCase   )   ):
        if not index_used[i]:
            current_sequence.append(sequence[i]   )
            _lowerCAmelCase       :   List[str]  =    True
            create_state_space_tree(_lowerCamelCase ,   _lowerCamelCase ,   index + 1 ,   _lowerCamelCase   )
            current_sequence.pop()
            _lowerCAmelCase       :   int  =    False
_lowerCAmelCase						=		[3, 1, 2, 4]
generate_all_permutations(sequence)

_lowerCAmelCase						=		["A", "B", "C"]

generate_all_permutations(sequence_a)





