


"""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


__UpperCAmelCase    				=      logging.get_logger(__name__)

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

if is_torch_available():
   import torch

if is_soundfile_availble():
   import soundfile as sf

class  lowercase_  :







      def __init__(      self :							Union[str, Any]		,  _lowercase :							List[Any]    ):
        lowerCAmelCase__    :    int								=					value







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







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

class  lowercase_  ( a_							,							a_						):







      def 				_lowerCAmelCase       (      self :							Optional[Any]    ):
        return self._value







      def 				_lowerCAmelCase       (      self :							Dict    ):
        return self._value

class  lowercase_  ( a_							,							a_						):







      def __init__(      self :							int		,  _lowercase :							Optional[Any]    ):
        super().__init__(_lowercase    )

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

        lowerCAmelCase__    :    Union[str, Any]								=					None
        lowerCAmelCase__    :    Any								=					None
        lowerCAmelCase__    :    Any								=					None

        if isinstance(_lowercase		,  _lowercase    ):
          lowerCAmelCase__    :    Optional[int]								=					value
        elif isinstance(_lowercase		,  (str, pathlib.Path)    ):
          lowerCAmelCase__    :    List[str]								=					value
        elif isinstance(_lowercase		,  torch.Tensor    ):
          lowerCAmelCase__    :    Optional[Any]								=					value
        else:
          raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(_lowercase    )}"    )







      def 				_lowerCAmelCase       (      self :							Dict		,  _lowercase :							Optional[int]=None		,  _lowercase :							Optional[Any]=None    ):
        from IPython.display import Image, display

        display(Image(self.to_string()    )    )







      def 				_lowerCAmelCase       (      self :							Optional[int]    ):
        if self._raw is not None:
          return self._raw

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







      def 				_lowerCAmelCase       (      self :							Optional[Any]    ):
        if self._path is not None:
          return self._path

        if self._raw is not None:
          lowerCAmelCase__    :    Tuple								=					tempfile.mkdtemp()
          lowerCAmelCase__    :    Any								=					os.path.join(_lowercase		,  str(uuid.uuida()    ) + ".png"    )
          self._raw.save(self._path    )

          return self._path

        if self._tensor is not None:
          lowerCAmelCase__    :    str								=					self._tensor.cpu().detach().numpy()

          # There is likely simpler than load into image into save
          lowerCAmelCase__    :    Any								=					Image.fromarray((array * 2_5_5).astype(np.uinta    )    )

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

          img.save(self._path    )

          return self._path

class  lowercase_  ( a_						):







      def __init__(      self :							Any		,  _lowercase :							List[str]		,  _lowercase :							str=1_6_0_0_0    ):
        super().__init__(_lowercase    )

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

        lowerCAmelCase__    :    List[str]								=					None
        lowerCAmelCase__    :    Any								=					None

        lowerCAmelCase__    :    Tuple								=					samplerate

        if isinstance(_lowercase		,  (str, pathlib.Path)    ):
          lowerCAmelCase__    :    Dict								=					value
        elif isinstance(_lowercase		,  torch.Tensor    ):
          lowerCAmelCase__    :    int								=					value
        else:
          raise ValueError(f"Unsupported audio type: {type(_lowercase    )}"    )







      def 				_lowerCAmelCase       (      self :							Union[str, Any]		,  _lowercase :							List[str]=None		,  _lowercase :							Union[str, Any]=None    ):
        from IPython.display import Audio, display

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







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

        if self._path is not None:
          lowerCAmelCase__    :    Union[str, Any]								=					sf.read(self._path    )
          lowerCAmelCase__    :    str								=					torch.tensor(_lowercase    )
          return self._tensor







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

        if self._tensor is not None:
          lowerCAmelCase__    :    List[str]								=					tempfile.mkdtemp()
          lowerCAmelCase__    :    Any								=					os.path.join(_lowercase		,  str(uuid.uuida()    ) + ".wav"    )
          sf.write(self._path		,  self._tensor		,  samplerate=self.samplerate    )
          return self._path
__UpperCAmelCase    				=      {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
__UpperCAmelCase    				=      {str: AgentText}

if is_vision_available():
   __UpperCAmelCase    				=      AgentImage





def     lowercase__	(  *lowerCamelCase     :  Any				,	**lowerCamelCase     :  int				)					->     int:
  lowerCAmelCase__    :    List[str]								=					[(arg.to_raw() if isinstance(lowerCamelCase				,	lowerCamelCase				) else arg) for arg in args]
  lowerCAmelCase__    :    Optional[Any]								=					{k: (v.to_raw() if isinstance(lowerCamelCase				,	lowerCamelCase				) else v) for k, v in kwargs.items()}
  return args, kwargs







def     lowercase__	(  lowerCamelCase     :  Dict				,	lowerCamelCase     :  Dict=None				)					->     Tuple:
  if isinstance(lowerCamelCase				,	lowerCamelCase				):
    lowerCAmelCase__    :    Dict								=					{}
    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__    :    str								=					AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase				)
        else:
          lowerCAmelCase__    :    Union[str, Any]								=					AgentType(lowerCamelCase				)

      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(lowerCamelCase				,	_k				):
            lowerCAmelCase__    :    Any								=					_v(lowerCamelCase				)
        if k not in decoded_outputs:
          lowerCAmelCase__    :    int								=					AgentType[v]

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

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

  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__    :    Optional[Any]								=					AGENT_TYPE_MAPPING[output_types[0]](lowerCamelCase				)

    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(lowerCamelCase				,	_k				):
          return _v(lowerCamelCase				)
      return AgentType(lowerCamelCase				)

  return decoded_outputs




"""simple docstring"""





from __future__ import annotations

__UpperCAmelCase    				=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]

class  lowercase_  :







      def __init__(      self :							int		,  _lowercase :							int    ):
        lowerCAmelCase__    :    int								=					-1
        lowerCAmelCase__    :    int								=					-1
        lowerCAmelCase__    :    list[int]								=					[-1] * length
        lowerCAmelCase__    :    Node | None								=					None
        lowerCAmelCase__    :    Node | None								=					None







      def __repr__(      self :							List[Any]    ):
        return f"Node(min_value={self.minn} max_value={self.maxx})"





def     lowercase__	(  lowerCamelCase     :  list[int]				)					->     Node | None:
  lowerCAmelCase__    :    Any								=					Node(len(lowerCamelCase				)				)
  lowerCAmelCase__				,		lowerCAmelCase__    :    Any								=					min(lowerCamelCase				), max(lowerCamelCase				)
  # Leaf node case where the node contains only one unique value
  if root.minn == root.maxx:
    return root
  lowerCAmelCase__    :    str								=					(root.minn + root.maxx) // 2

  lowerCAmelCase__    :    list[int]								=					[]
  lowerCAmelCase__    :    list[int]								=					[]

  for index, num in enumerate(lowerCamelCase				):
    if num <= pivot:
      left_arr.append(lowerCamelCase				)
    else:
      right_arr.append(lowerCamelCase				)
    lowerCAmelCase__    :    Optional[Any]								=					len(lowerCamelCase				)
  lowerCAmelCase__    :    List[str]								=					build_tree(lowerCamelCase				)
  lowerCAmelCase__    :    List[Any]								=					build_tree(lowerCamelCase				)
  return root





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if index < 0 or node is None:
    return 0
  # Leaf node cases
  if node.minn == node.maxx:
    return index + 1 if node.minn == num else 0
  lowerCAmelCase__    :    int								=					(node.minn + node.maxx) // 2
  if num <= pivot:
    # go the left subtree and map index to the left subtree
    return rank_till_index(node.left				,	lowerCamelCase				,	node.map_left[index] - 1				)
  else:
    # go to the right subtree and map index to the right subtree
    return rank_till_index(node.right				,	lowerCamelCase				,	index - node.map_left[index]				)





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if start > end:
    return 0
  lowerCAmelCase__    :    int								=					rank_till_index(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)
  lowerCAmelCase__    :    Optional[Any]								=					rank_till_index(lowerCamelCase				,	lowerCamelCase				,	start - 1				)
  return rank_till_end - rank_before_start





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if index > (end - start) or start > end or node is None:
    return -1
  # Leaf node case
  if node.minn == node.maxx:
    return node.minn
  # Number of elements in the left subtree in interval [start, end]
  lowerCAmelCase__    :    List[str]								=					node.map_left[end] - (
      node.map_left[start - 1] if start else 0
  )
  if num_elements_in_left_tree > index:
    return quantile(
        node.left				,	lowerCamelCase				,	(node.map_left[start - 1] if start else 0)				,	node.map_left[end] - 1				,	)
  else:
    return quantile(
        node.right				,	index - num_elements_in_left_tree				,	start - (node.map_left[start - 1] if start else 0)				,	end - node.map_left[end]				,	)





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if (
      start > end
      or node is None
      or start_num > end_num
      or node.minn > end_num
      or node.maxx < start_num
  ):
    return 0
  if start_num <= node.minn and node.maxx <= end_num:
    return end - start + 1
  lowerCAmelCase__    :    List[str]								=					range_counting(
      node.left				,	(node.map_left[start - 1] if start else 0)				,	node.map_left[end] - 1				,	lowerCamelCase				,	lowerCamelCase				,	)
  lowerCAmelCase__    :    List[str]								=					range_counting(
      node.right				,	start - (node.map_left[start - 1] if start else 0)				,	end - node.map_left[end]				,	lowerCamelCase				,	lowerCamelCase				,	)
  return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()
