






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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str]     )	->      Optional[int]:

   """simple docstring"""
   __a					  =     []

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

   return inputs



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List     )	->      Union[str, Any]:

   """simple docstring"""
   __a					  =     []

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

   return output_types



@is_tool_test
class 				__SCREAMING_SNAKE_CASE							:




      def 			__UpperCamelCase				(	self   )				->Dict:



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

         __a					  =     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   )

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




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     create_inputs(self.tool.inputs   )
         __a					  =     self.tool(*lowerCamelCase   )

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

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




      def 			__UpperCamelCase				(	self   )				->int:



         '''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 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     create_inputs(self.tool.inputs   )
         __a					  =     self.tool(*lowerCamelCase   )

         if not isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     [outputs]

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

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






      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     create_inputs(self.tool.inputs   )

         __a					  =     []

         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
         __a					  =     self.tool(*lowerCamelCase   )

         if not isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     [outputs]

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







'''simple docstring'''



from math import isqrt



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      bool:

   """simple docstring"""
   return all(number % divisor != 0 for divisor in range(2,       isqrt(SCREAMING_SNAKE_CASE__     ) + 1     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int = 10**6     )	->      int:

   """simple docstring"""
   __a					  =     0
   __a					  =     1
   __a					  =     7
   while prime_candidate < max_prime:
      primes_count += is_prime(SCREAMING_SNAKE_CASE__     )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


if __name__ == "__main__":
 print(f"""{solution() = }""")