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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Tuple:

				"""simple docstring"""







				A__  :	Union[str, 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((5_12, 5_12)       )       )
								elif input_type == "audio":
												inputs.append(torch.ones(30_00       )       )
								elif isinstance(__UpperCamelCase							,						__UpperCamelCase       ):
												inputs.append(create_inputs(__UpperCamelCase       )       )
								else:
												raise ValueError(F"Invalid type requested: {input_type}"       )

				return inputs

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List       ) ->       Tuple:

				"""simple docstring"""







				A__  :	Any         =							[]

				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  UpperCamelCase__		:







	'''simple docstring'''





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

					A__  :	Dict         =							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__  :	int         =							self.tool.outputs
					for _output in outputs:
									self.assertTrue(_output in authorized_types		)





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							create_inputs(self.tool.inputs		)
					A__  :	Tuple         =							self.tool(*UpperCamelCase__		)

					# There is a single output
					if len(self.tool.outputs		) == 1:
									A__  :	Union[str, Any]         =							[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__  :	Any         =							create_inputs(self.tool.inputs		)
					A__  :	Optional[Any]         =							self.tool(*UpperCamelCase__		)

					if not isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	List[Any]         =							[outputs]

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

					for output, output_type in zip(UpperCamelCase__				,       self.tool.outputs		):
									A__  :	List[Any]         =							AGENT_TYPE_MAPPING[output_type]
									self.assertTrue(isinstance(UpperCamelCase__				,       UpperCamelCase__		)		)





	def   __snake_case				(			self		):
					A__  :	Dict         =							create_inputs(self.tool.inputs		)

					A__  :	str         =							[]

					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__  :	Union[str, Any]         =							self.tool(*UpperCamelCase__		)

					if not isinstance(UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	List[Any]         =							[outputs]

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

def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				for i in range(1							,						10_01       ):
								total += i**i
				return str(__UpperCamelCase       )[-10:]


if __name__ == "__main__":
					print(solution())