


"""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


snake_case__		:  str     					=					logging.get_logger(__name__)

if is_vision_available():
 import PIL.Image
 from PIL import Image
 from PIL.Image import Image as ImageType
else:
 snake_case__		:  Optional[int]     					=					object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf



class 		snake_case_:






     def __init__(						self	:       int       ,					UpperCamelCase_	:       Any					):
           lowerCAmelCase       :						Optional[Any]							= value






     def __str__(						self	:       Optional[Any]					):
           return self.to_string()






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






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



class 		snake_case_(							a__				,       a__      ):






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           return self._value






     def   lowerCamelCase__  (						self	:       int					):
           return self._value



class 		snake_case_(							a__				,       a__      ):






     def __init__(						self	:       Tuple       ,					UpperCamelCase_	:       Dict					):
           super().__init__(UpperCamelCase_					)

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

           lowerCAmelCase       :						List[str]							= None
           lowerCAmelCase       :						str							= None
           lowerCAmelCase       :						Tuple							= None

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






     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       Tuple=None       ,					UpperCamelCase_	:       Optional[Any]=None					):
           from IPython.display import Image, display

           display(Image(self.to_string()					)					)






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

           if self._path is not None:
                 lowerCAmelCase       :						List[str]							= Image.open(self._path					)
                 return self._raw






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

           if self._raw is not None:
                 lowerCAmelCase       :						Tuple							= tempfile.mkdtemp()
                 lowerCAmelCase       :						Optional[Any]							= os.path.join(UpperCamelCase_       ,					str(uuid.uuida()					) + '''.png'''					)
                 self._raw.save(self._path					)

                 return self._path

           if self._tensor is not None:
                 lowerCAmelCase       :						Union[str, Any]							= self._tensor.cpu().detach().numpy()

                 # There is likely simpler than load into image into save
                 lowerCAmelCase       :						Union[str, Any]							= Image.fromarray((array * 2_5_5).astype(np.uinta					)					)

                 lowerCAmelCase       :						Any							= tempfile.mkdtemp()
                 lowerCAmelCase       :						Optional[Any]							= os.path.join(UpperCamelCase_       ,					str(uuid.uuida()					) + '''.png'''					)

                 img.save(self._path					)

                 return self._path



class 		snake_case_(							a__      ):






     def __init__(						self	:       Dict       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Union[str, Any]=1_6_0_0_0					):
           super().__init__(UpperCamelCase_					)

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

           lowerCAmelCase       :						Any							= None
           lowerCAmelCase       :						str							= None

           lowerCAmelCase       :						str							= samplerate

           if isinstance(UpperCamelCase_       ,					(str, pathlib.Path)					):
                 lowerCAmelCase       :						Dict							= value
           elif isinstance(UpperCamelCase_       ,					torch.Tensor					):
                 lowerCAmelCase       :						Union[str, Any]							= value
           else:
                 raise ValueError(F'''Unsupported audio type: {type(UpperCamelCase_					)}'''					)






     def   lowerCamelCase__  (						self	:       Tuple       ,					UpperCamelCase_	:       str=None       ,					UpperCamelCase_	:       Dict=None					):
           from IPython.display import Audio, display

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






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

           if self._path is not None:
                 lowerCAmelCase,   lowerCAmelCase       :						List[str]							= sf.read(self._path					)
                 lowerCAmelCase       :						Tuple							= torch.tensor(UpperCamelCase_					)
                 return self._tensor






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           if self._path is not None:
                 return self._path

           if self._tensor is not None:
                 lowerCAmelCase       :						Optional[Any]							= tempfile.mkdtemp()
                 lowerCAmelCase       :						Union[str, Any]							= os.path.join(UpperCamelCase_       ,					str(uuid.uuida()					) + '''.wav'''					)
                 sf.write(self._path       ,					self._tensor       ,					samplerate=self.samplerate					)
                 return self._path



snake_case__		:  int     					=					{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
snake_case__		:  Union[str, Any]     					=					{str: AgentText}

if is_vision_available():
 snake_case__		:  List[str]     					=					AgentImage


def   _snake_case    (    *_snake_case					:      int			, **_snake_case					:      Tuple      ):
      lowerCAmelCase       :						int							= [(arg.to_raw() if isinstance(_snake_case			, _snake_case      ) else arg) for arg in args]
      lowerCAmelCase       :						int							= {k: (v.to_raw() if isinstance(_snake_case			, _snake_case      ) else v) for k, v in kwargs.items()}
      return args, kwargs






def   _snake_case    (    _snake_case					:      List[Any]			, _snake_case					:      Optional[Any]=None      ):
      if isinstance(_snake_case			, _snake_case      ):
            lowerCAmelCase       :						int							= {}
            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:
                              lowerCAmelCase       :						Tuple							= AGENT_TYPE_MAPPING[output_types[i]](_snake_case      )
                        else:
                              lowerCAmelCase       :						Tuple							= AgentType(_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(_snake_case			, _k      ):
                                    lowerCAmelCase       :						Tuple							= _v(_snake_case      )
                        if k not in decoded_outputs:
                              lowerCAmelCase       :						List[str]							= AgentType[v]

      elif isinstance(_snake_case			, (list, tuple)      ):
            lowerCAmelCase       :						List[str]							= type(_snake_case      )()
            for i, v in enumerate(_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]](_snake_case      )      )
                        else:
                              decoded_outputs.append(AgentType(_snake_case      )      )
                  else:
                        # If the class does not have defined output, then we map according to the type
                        lowerCAmelCase       :						Optional[int]							= False
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(_snake_case			, _k      ):
                                    decoded_outputs.append(_v(_snake_case      )      )
                                    lowerCAmelCase       :						Any							= True

                        if not found:
                              decoded_outputs.append(AgentType(_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
                  lowerCAmelCase       :						int							= AGENT_TYPE_MAPPING[output_types[0]](_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(_snake_case			, _k      ):
                              return _v(_snake_case      )
                  return AgentType(_snake_case      )

      return decoded_outputs






"""simple docstring"""





import math as m


def   _snake_case    (    _snake_case					:      List[Any]			, _snake_case					:      Tuple			, _snake_case					:      Tuple=0.001      ):
      return (f(a + h      ) - f(a - h      )) / (2 * h)


def   _snake_case    (    _snake_case					:      int			, _snake_case					:      List[Any]=0			, _snake_case					:      Any=100			, _snake_case					:      Any=0.0001			, _snake_case					:      Optional[Any]=1E-6			, _snake_case					:      Any=False      ):
      lowerCAmelCase       :						Optional[Any]							= xa  # set the initial guess
      lowerCAmelCase       :						str							= [a]
      lowerCAmelCase       :						List[str]							= abs(f(_snake_case      )      )
      lowerCAmelCase       :						Dict							= lambda _snake_case      : calc_derivative(_snake_case			, _snake_case			, h=_snake_case      )  # noqa: E731  Derivative of f(x)
      for _ in range(_snake_case      ):
            if fa(_snake_case      ) == 0:
                  raise ValueError('''No converging solution found'''      )
            lowerCAmelCase       :						Tuple							= a - f(_snake_case      ) / fa(_snake_case      )  # Calculate the next estimate
            if logsteps:
                  steps.append(_snake_case      )
            if error < maxerror:
                  break
      else:
            raise ValueError('''Iteration limit reached, no converging solution found'''      )
      if logsteps:
            # If logstep is true, then log intermediate steps
            return a, error, steps
      return a, error


if __name__ == "__main__":
 from matplotlib import pyplot as plt

 snake_case__		:  Any     					=					lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
 snake_case__					,  snake_case__					,  snake_case__		:  List[str]     					=					newton_raphson(
     f, xa=10, maxiter=1_000, step=1e-6, logsteps=True
 )
 plt.plot([abs(f(x)) for x in steps])
 plt.xlabel('''step''')
 plt.ylabel('''error''')
 plt.show()
 print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")


