




'''simple docstring'''





from collections import deque





def       __a      (  _UpperCamelCase:       Optional[int]       )		->					Any:

     """simple docstring"""




     _snake_case				= len(_UpperCamelCase       )
     _snake_case				= deque()
     _snake_case				= [False for _ in range(_UpperCamelCase       )]
     _snake_case				= [-1 for _ in range(_UpperCamelCase       )]
     _snake_case				= index_of[:]

     def strong_connect(_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[Any]   ,		_UpperCamelCase:       List[Any]       ):
          _snake_case				= index  # the number when this node is seen
          _snake_case				= index  # lowest rank node reachable from here
          index += 1
          stack.append(_UpperCamelCase       )
          _snake_case				= True

          for w in g[v]:
               if index_of[w] == -1:
                    _snake_case				= strong_connect(_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )
                    _snake_case				= (
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    _snake_case				= (
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               _snake_case				= []
               _snake_case				= stack.pop()
               _snake_case				= False
               component.append(_UpperCamelCase       )
               while w != v:
                    _snake_case				= stack.pop()
                    _snake_case				= False
                    component.append(_UpperCamelCase       )
               components.append(_UpperCamelCase       )
          return index

     _snake_case				= []
     for v in range(_UpperCamelCase       ):
          if index_of[v] == -1:
               strong_connect(_UpperCamelCase   ,		0   ,		_UpperCamelCase       )

     return components





def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       str       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= [[] for _ in range(_UpperCamelCase       )]
     for u, v in edges:
          g[u].append(_UpperCamelCase       )
     return g


if __name__ == "__main__":
   # Test
   UpperCamelCase_		:     Union[str, Any]          =		7
   UpperCamelCase_		:     List[Any]          =		[0, 0, 1, 2, 3, 3, 4, 4, 6]
   UpperCamelCase_		:     str          =		[1, 3, 2, 0, 1, 4, 5, 6, 5]
   UpperCamelCase_		:     List[str]          =		[(u, v) for u, v in zip(source, target)]
   UpperCamelCase_		:     Optional[int]          =		create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)








'''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


UpperCamelCase_		:     int          =		['''text''', '''image''', '''audio''']





def       __a      (  _UpperCamelCase:       List[str]       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     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       __a      (  _UpperCamelCase:       List       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     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   _a							:






     def 							_lowercase      (					self       )				->     Any:
          self.assertTrue(hasattr(self.tool   ,"inputs"       )       )
          self.assertTrue(hasattr(self.tool   ,"outputs"       )       )

          _snake_case				= self.tool.inputs
          for _input in inputs:
               if isinstance(_input   ,_SCREAMING_SNAKE_CASE       ):
                    for __input in _input:
                         self.assertTrue(__input in authorized_types       )
               else:
                    self.assertTrue(_input in authorized_types       )

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






     def 							_lowercase      (					self       )				->     Any:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

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

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






     def 							_lowercase      (					self       )				->     str:
          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 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

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

          for output, output_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.outputs       ):
               _snake_case				= AGENT_TYPE_MAPPING[output_type]
               self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )       )






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= create_inputs(self.tool.inputs       )

          _snake_case				= []

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

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

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


