
'''simple docstring'''
import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


a_     =  logging.get_logger(__name__)

if is_vision_available():
  import PIL.Image
  from PIL import Image
  from PIL.Image import Image as ImageType
else:
  a_     =  object

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf






class 				UpperCAmelCase_			:





 def __init__(      self	,    lowercase_):
     snake_case_	:      Optional[int]            =  value





 def __str__(      self):
     return self.to_string()





 def     snake_case__						(      self):
     logger.error(
         "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable")
     return self._value





 def     snake_case__						(      self):
     logger.error(
         "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable")
     return str(self._value)






class 				UpperCAmelCase_			(		snake_case__      ,  snake_case__   ):





 def     snake_case__						(      self):
     return self._value





 def     snake_case__						(      self):
     return self._value






class 				UpperCAmelCase_			(		snake_case__      ,  snake_case__   ):





 def __init__(      self	,    lowercase_):
     super().__init__(lowercase_)

     if not is_vision_available():
         raise ImportError("PIL must be installed in order to handle images.")

     snake_case_	:      Optional[Any]            =  None
     snake_case_	:      List[Any]            =  None
     snake_case_	:      Union[str, Any]            =  None

     if isinstance(lowercase_	,    lowercase_):
         snake_case_	:      Union[str, Any]            =  value
     elif isinstance(lowercase_	,    (str, pathlib.Path)):
         snake_case_	:      int            =  value
     elif isinstance(lowercase_	,    torch.Tensor):
         snake_case_	:      Any            =  value
     else:
         raise ValueError(F'Unsupported type for {self.__class__.__name__}: {type(lowercase_)}')





 def     snake_case__						(      self	,    lowercase_=None	,    lowercase_=None):
     from IPython.display import Image, display

     display(Image(self.to_string()))





 def     snake_case__						(      self):
     if self._raw is not None:
         return self._raw

     if self._path is not None:
         snake_case_	:      Optional[Any]            =  Image.open(self._path)
         return self._raw





 def     snake_case__						(      self):
     if self._path is not None:
         return self._path

     if self._raw is not None:
         snake_case_	:      Union[str, Any]            =  tempfile.mkdtemp()
         snake_case_	:      List[str]            =  os.path.join(lowercase_	,    str(uuid.uuida()) + ".png")
         self._raw.save(self._path)

         return self._path

     if self._tensor is not None:
         snake_case_	:      List[str]            =  self._tensor.cpu().detach().numpy()

         # There is likely simpler than load into image into save
         snake_case_	:      Union[str, Any]            =  Image.fromarray((array * 2_55).astype(np.uinta))

         snake_case_	:      List[str]            =  tempfile.mkdtemp()
         snake_case_	:      List[Any]            =  os.path.join(lowercase_	,    str(uuid.uuida()) + ".png")

         img.save(self._path)

         return self._path






class 				UpperCAmelCase_			(		snake_case__   ):





 def __init__(      self	,    lowercase_	,    lowercase_=1_60_00):
     super().__init__(lowercase_)

     if not is_soundfile_availble():
         raise ImportError("soundfile must be installed in order to handle audio.")

     snake_case_	:      Any            =  None
     snake_case_	:      Optional[Any]            =  None

     snake_case_	:      Any            =  samplerate

     if isinstance(lowercase_	,    (str, pathlib.Path)):
         snake_case_	:      Optional[int]            =  value
     elif isinstance(lowercase_	,    torch.Tensor):
         snake_case_	:      Dict            =  value
     else:
         raise ValueError(F'Unsupported audio type: {type(lowercase_)}')





 def     snake_case__						(      self	,    lowercase_=None	,    lowercase_=None):
     from IPython.display import Audio, display

     display(Audio(self.to_string()	,    rate=self.samplerate))





 def     snake_case__						(      self):
     if self._tensor is not None:
         return self._tensor

     if self._path is not None:
         snake_case_	:      Optional[int]            =  sf.read(self._path)
         snake_case_	:      List[Any]            =  torch.tensor(lowercase_)
         return self._tensor





 def     snake_case__						(      self):
     if self._path is not None:
         return self._path

     if self._tensor is not None:
         snake_case_	:      Tuple            =  tempfile.mkdtemp()
         snake_case_	:      Union[str, Any]            =  os.path.join(lowercase_	,    str(uuid.uuida()) + ".wav")
         sf.write(self._path	,    self._tensor	,    samplerate=self.samplerate)
         return self._path


a_     =  {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
a_     =  {str: AgentText}

if is_vision_available():
  a_     =  AgentImage

def   UpperCamelCase_       (		*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      List[Any]            =  [(arg.to_raw() if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE    ) else arg) for arg in args]
    snake_case_	:      str            =  {k: (v.to_raw() if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE    ) else v) for k, v in kwargs.items()}
    return args, kwargs


def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE=None    ):
    """simple docstring"""






    if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE    ):
        snake_case_	:      List[Any]            =  {}
        for i, (k, v) in enumerate(outputs.items()    ):
            if output_types is not None:
                # If the class has defined outputs, we can map directly according to the class definition
                if output_types[i] in AGENT_TYPE_MAPPING:
                    snake_case_	:      List[str]            =  AGENT_TYPE_MAPPING[output_types[i]](__SCREAMING_SNAKE_CASE    )
                else:
                    snake_case_	:      List[Any]            =  AgentType(__SCREAMING_SNAKE_CASE    )

            else:
                # If the class does not have defined output, then we map according to the type
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(__SCREAMING_SNAKE_CASE       ,						_k    ):
                        snake_case_	:      List[Any]            =  _v(__SCREAMING_SNAKE_CASE    )
                if k not in decoded_outputs:
                    snake_case_	:      List[Any]            =  AgentType[v]

    elif isinstance(__SCREAMING_SNAKE_CASE       ,						(list, tuple)    ):
        snake_case_	:      Dict            =  type(__SCREAMING_SNAKE_CASE    )()
        for i, v in enumerate(__SCREAMING_SNAKE_CASE    ):
            if output_types is not None:
                # If the class has defined outputs, we can map directly according to the class definition
                if output_types[i] in AGENT_TYPE_MAPPING:
                    decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__SCREAMING_SNAKE_CASE    )    )
                else:
                    decoded_outputs.append(AgentType(__SCREAMING_SNAKE_CASE    )    )
            else:
                # If the class does not have defined output, then we map according to the type
                snake_case_	:      Any            =  False
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(__SCREAMING_SNAKE_CASE       ,						_k    ):
                        decoded_outputs.append(_v(__SCREAMING_SNAKE_CASE    )    )
                        snake_case_	:      Tuple            =  True

                if not found:
                    decoded_outputs.append(AgentType(__SCREAMING_SNAKE_CASE    )    )

    else:
        if output_types[0] in AGENT_TYPE_MAPPING:
            # If the class has defined outputs, we can map directly according to the class definition
            snake_case_	:      str            =  AGENT_TYPE_MAPPING[output_types[0]](__SCREAMING_SNAKE_CASE    )

        else:
            # If the class does not have defined output, then we map according to the type
            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                if isinstance(__SCREAMING_SNAKE_CASE       ,						_k    ):
                    return _v(__SCREAMING_SNAKE_CASE    )
            return AgentType(__SCREAMING_SNAKE_CASE    )

    return decoded_outputs






'''simple docstring'''
from math import exp

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    return 8 * x - 2 * exp(-x    )

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  lower_bound
    snake_case_	:      int            =  upper_bound
    for _ in range(0,						__SCREAMING_SNAKE_CASE    ):
        snake_case_     ,	snake_case_	:      List[str]            =  xa, xa - (f(__SCREAMING_SNAKE_CASE    ) * (xa - xa)) / (f(__SCREAMING_SNAKE_CASE    ) - f(__SCREAMING_SNAKE_CASE    ))
    return xa


if __name__ == "__main__":
  print(f"""Example: {secant_method(1, 3, 2)}""")




