




'''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


lowerCAmelCase_      :  str				     =  ['text', 'image', 'audio']



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str]				):




     """simple docstring"""


     a_     :							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(UpperCamelCase__ ,    UpperCamelCase__				):
               inputs.append(create_inputs(UpperCamelCase__				)				)
          else:
               raise ValueError(F"Invalid type requested: {input_type}"				)

     return inputs



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List				):




     """simple docstring"""


     a_     :							Union[str, 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      SCREAMING_SNAKE_CASE    :






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

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

           a_     :							Union[str, Any]     						=  self.tool.inputs
           for _input in inputs:
                if isinstance(_input						,						lowercase__       ):
                     for __input in _input:
                          self.assertTrue(__input in authorized_types       )
                else:
                     self.assertTrue(_input in authorized_types       )

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






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  create_inputs(self.tool.inputs       )
           a_     :							List[Any]     						=  self.tool(*lowercase__       )

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

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






      def 							lowercase_						( self   :  Any       ):


           '''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 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							int     						=  create_inputs(self.tool.inputs       )
           a_     :							List[str]     						=  self.tool(*lowercase__       )

           if not isinstance(lowercase__						,						lowercase__       ):
                a_     :							Optional[int]     						=  [outputs]

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

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






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  create_inputs(self.tool.inputs       )

           a_     :							List[str]     						=  []

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

           if not isinstance(lowercase__						,						lowercase__       ):
                a_     :							Tuple     						=  [outputs]

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






'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
