

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__	:       Any			      =      logging.get_logger(__name__)

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

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf







class     a__    :







		"""simple docstring"""




		def __init__(     self      :	List[str]			,			UpperCAmelCase__      :	Tuple						)   ->List[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE	: Tuple					       =				value


		def __str__(     self      :	Optional[Any]						)   ->Tuple:
								"""simple docstring"""







								return self.to_string()


		def 	_lowercase (     self      :	Tuple						)   ->Tuple:
								"""simple docstring"""







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




		def 	_lowercase (     self      :	Tuple						)   ->str:
								"""simple docstring"""







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







class     a__    (				UpperCAmelCase		,     UpperCAmelCase    ):







		"""simple docstring"""




		def 	_lowercase (     self      :	Optional[int]						)   ->int:
								"""simple docstring"""







								return self._value




		def 	_lowercase (     self      :	Dict						)   ->str:
								"""simple docstring"""







								return self._value







class     a__    (				UpperCAmelCase		,     UpperCAmelCase    ):







		"""simple docstring"""




		def __init__(     self      :	int			,			UpperCAmelCase__      :	Tuple						)   ->List[Any]:
								"""simple docstring"""







								super().__init__(UpperCAmelCase__						)

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

								SCREAMING_SNAKE_CASE	: List[str]					       =				None
								SCREAMING_SNAKE_CASE	: List[str]					       =				None
								SCREAMING_SNAKE_CASE	: Optional[Any]					       =				None

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


		def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	List[str]=None			,			UpperCAmelCase__      :	Dict=None						)   ->Optional[Any]:
								"""simple docstring"""







								from IPython.display import Image, display

								display(Image(self.to_string()						)						)


		def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[int]:
								"""simple docstring"""







								if self._raw is not None:
														return self._raw

								if self._path is not None:
														SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				Image.open(self._path						)
														return self._raw




		def 	_lowercase (     self      :	Optional[int]						)   ->Union[str, Any]:
								"""simple docstring"""







								if self._path is not None:
														return self._path

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

														return self._path

								if self._tensor is not None:
														SCREAMING_SNAKE_CASE	: Any					       =				self._tensor.cpu().detach().numpy()

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

														SCREAMING_SNAKE_CASE	: str					       =				tempfile.mkdtemp()
														SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				os.path.join(UpperCAmelCase__			,			str(uuid.uuida()						) + """.png"""						)

														img.save(self._path						)

														return self._path







class     a__    (				UpperCAmelCase    ):







		"""simple docstring"""




		def __init__(     self      :	str			,			UpperCAmelCase__      :	Optional[int]			,			UpperCAmelCase__      :	Dict=1_6_0_0_0						)   ->str:
								"""simple docstring"""







								super().__init__(UpperCAmelCase__						)

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

								SCREAMING_SNAKE_CASE	: Optional[int]					       =				None
								SCREAMING_SNAKE_CASE	: Optional[int]					       =				None

								SCREAMING_SNAKE_CASE	: List[Any]					       =				samplerate

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


		def 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	str=None			,			UpperCAmelCase__      :	Tuple=None						)   ->List[str]:
								"""simple docstring"""







								from IPython.display import Audio, display

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


		def 	_lowercase (     self      :	Optional[Any]						)   ->str:
								"""simple docstring"""







								if self._tensor is not None:
														return self._tensor

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




		def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[Any]:
								"""simple docstring"""







								if self._path is not None:
														return self._path

								if self._tensor is not None:
														SCREAMING_SNAKE_CASE	: Dict					       =				tempfile.mkdtemp()
														SCREAMING_SNAKE_CASE	: Tuple					       =				os.path.join(UpperCAmelCase__			,			str(uuid.uuida()						) + """.wav"""						)
														sf.write(self._path			,			self._tensor			,			samplerate=self.samplerate						)
														return self._path


UpperCAmelCase__	:       Optional[Any]			      =      {"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
UpperCAmelCase__	:       Dict			      =      {str: AgentText}

if is_vision_available():
		UpperCAmelCase__	:       Optional[int]			      =      AgentImage






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







def    __lowercase (       _A  ,      _A=None     )       ->  str:
						if isinstance(_A  ,      _A     ):
												SCREAMING_SNAKE_CASE	: 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:
																														SCREAMING_SNAKE_CASE	: Tuple					       =				AGENT_TYPE_MAPPING[output_types[i]](_A     )
																								else:
																														SCREAMING_SNAKE_CASE	: Optional[int]					       =				AgentType(_A     )

																		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(_A  ,      _k     ):
																																				SCREAMING_SNAKE_CASE	: Any					       =				_v(_A     )
																								if k not in decoded_outputs:
																														SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				AgentType[v]

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

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

						else:
												if output_types[0] in AGENT_TYPE_MAPPING:
																		# If the class has defined outputs, we can map directly according to the class definition
																		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				AGENT_TYPE_MAPPING[output_types[0]](_A     )

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

						return decoded_outputs







from __future__ import annotations

UpperCAmelCase__	:       Optional[int]			      =      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]







class     a__    :







  """simple docstring"""




  def __init__(     self      :	Optional[Any]			,			UpperCAmelCase__      :	int						)   ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				-1
        SCREAMING_SNAKE_CASE	: int					       =				-1
        SCREAMING_SNAKE_CASE	: list[int]					       =				[-1] * length
        SCREAMING_SNAKE_CASE	: Node | None					       =				None
        SCREAMING_SNAKE_CASE	: Node | None					       =				None




  def __repr__(     self      :	Dict						)   ->str:
        """simple docstring"""







        return f"Node(min_value={self.minn} max_value={self.maxx})"






def    __lowercase (       _A     )       ->  Node | None:
      SCREAMING_SNAKE_CASE	: Tuple					       =				Node(len(_A     )     )
      SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: int					       =				min(_A     ), max(_A     )
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      SCREAMING_SNAKE_CASE	: int					       =				(root.minn + root.maxx) // 2

      SCREAMING_SNAKE_CASE	: list[int]					       =				[]
      SCREAMING_SNAKE_CASE	: list[int]					       =				[]

      for index, num in enumerate(_A     ):
            if num <= pivot:
                  left_arr.append(_A     )
            else:
                  right_arr.append(_A     )
            SCREAMING_SNAKE_CASE	: int					       =				len(_A     )
      SCREAMING_SNAKE_CASE	: int					       =				build_tree(_A     )
      SCREAMING_SNAKE_CASE	: Tuple					       =				build_tree(_A     )
      return root






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  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
      SCREAMING_SNAKE_CASE	: List[str]					       =				(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  ,      _A  ,      node.map_left[index] - 1     )
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right  ,      _A  ,      index - node.map_left[index]     )






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  int:
      if start > end:
            return 0
      SCREAMING_SNAKE_CASE	: Any					       =				rank_till_index(_A  ,      _A  ,      _A     )
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				rank_till_index(_A  ,      _A  ,      start - 1     )
      return rank_till_end - rank_before_start






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  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]
      SCREAMING_SNAKE_CASE	: Tuple					       =				node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left  ,      _A  ,      (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 (       _A  ,      _A  ,      _A  ,      _A  ,      _A     )       ->  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
      SCREAMING_SNAKE_CASE	: Dict					       =				range_counting(
          node.left  ,      (node.map_left[start - 1] if start else 0)  ,      node.map_left[end] - 1  ,      _A  ,      _A  ,      )
      SCREAMING_SNAKE_CASE	: List[str]					       =				range_counting(
          node.right  ,      start - (node.map_left[start - 1] if start else 0)  ,      end - node.map_left[end]  ,      _A  ,      _A  ,      )
      return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()




