

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





def 	__a     (      A__			:  List[str]       ):
    SCREAMING_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(3000       )       )
        elif isinstance(A__			,  A__       ):
            inputs.append(create_inputs(A__       )       )
        else:
            raise ValueError(F"Invalid type requested: {input_type}"       )

    return inputs





def 	__a     (      A__			:  List       ):
    SCREAMING_SNAKE_CASE										=   []

    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      _snake_case		(       self					:			str			):
       self.assertTrue(hasattr(self.tool     , "inputs"			)			)
       self.assertTrue(hasattr(self.tool     , "outputs"			)			)

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

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





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   create_inputs(self.tool.inputs			)
       SCREAMING_SNAKE_CASE										=   self.tool(*__lowerCamelCase			)

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

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





   def      _snake_case		(       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      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   create_inputs(self.tool.inputs			)
       SCREAMING_SNAKE_CASE										=   self.tool(*__lowerCamelCase			)

       if not isinstance(__lowerCamelCase     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   [outputs]

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

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





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   create_inputs(self.tool.inputs			)

       SCREAMING_SNAKE_CASE										=   []

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

       if not isinstance(__lowerCamelCase     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   [outputs]

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


from __future__ import annotations





def 	__a     (      A__			:  list[int | str]       ):
    create_state_space_tree(A__			,  []			,  0			,  [0 for i in range(len(A__       )       )]       )





def 	__a     (      A__			:  list[int | str]			,  A__			:  list[int | str]			,  A__			:  int			,  A__			:  list[int]			,  ):

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

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

__A			:					list[int | str]						=			["A", "B", "C"]





generate_all_permutations(sequence_a)