

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							:
			'''simple docstring'''







			def __init__(     self							:	Dict , a							:	Any		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    value
			def __str__(     self							:	str		)							->    List[Any]:

				"""simple docstring"""

				return self.to_string()
			def 							__UpperCamelCase	(     self							:	Dict		)							->    List[str]:

				"""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 							__UpperCamelCase	(     self							:	Any		)							->    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 _UpperCamelCase							(							__A							,		__A				):
			'''simple docstring'''







			def 							__UpperCamelCase	(     self							:	Tuple		)							->    Tuple:

				"""simple docstring"""

				return self._value






			def 							__UpperCamelCase	(     self							:	str		)							->    Any:

				"""simple docstring"""

				return self._value






class _UpperCamelCase							(							__A							,		__A				):
			'''simple docstring'''







			def __init__(     self							:	Union[str, Any] , a							:	List[Any]		)							->    str:

				"""simple docstring"""

				super().__init__(a		)

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

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

				if isinstance(a , a		):
					SCREAMING_SNAKE_CASE				: int			       =    value
				elif isinstance(a , (str, pathlib.Path)		):
					SCREAMING_SNAKE_CASE				: int			       =    value
				elif isinstance(a , torch.Tensor		):
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    value
				else:
					raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(a		)}"		)
			def 							__UpperCamelCase	(     self							:	str , a							:	List[str]=None , a							:	str=None		)							->    Tuple:

				"""simple docstring"""

				from IPython.display import Image, display

				display(Image(self.to_string()		)		)
			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Union[str, Any]:

				"""simple docstring"""

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

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






			def 							__UpperCamelCase	(     self							:	Any		)							->    int:

				"""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				: Any			       =    os.path.join(a , str(uuid.uuida()		) + ".png"		)
					self._raw.save(self._path		)

					return self._path

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

					# There is likely simpler than load into image into save
					SCREAMING_SNAKE_CASE				: str			       =    Image.fromarray((array * 255).astype(np.uinta		)		)

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

					img.save(self._path		)

					return self._path






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			def __init__(     self							:	int , a							:	List[str] , a							:	str=1_6000		)							->    Dict:

				"""simple docstring"""

				super().__init__(a		)

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

				SCREAMING_SNAKE_CASE				: str			       =    None
				SCREAMING_SNAKE_CASE				: int			       =    None

				SCREAMING_SNAKE_CASE				: Tuple			       =    samplerate

				if isinstance(a , (str, pathlib.Path)		):
					SCREAMING_SNAKE_CASE				: Any			       =    value
				elif isinstance(a , torch.Tensor		):
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    value
				else:
					raise ValueError(F"Unsupported audio type: {type(a		)}"		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	Optional[int]=None , a							:	Optional[Any]=None		)							->    int:

				"""simple docstring"""

				from IPython.display import Audio, display

				display(Audio(self.to_string() , rate=self.samplerate		)		)
			def 							__UpperCamelCase	(     self							:	List[Any]		)							->    List[str]:

				"""simple docstring"""

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

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






			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Tuple:

				"""simple docstring"""

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

				if self._tensor is not None:
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    tempfile.mkdtemp()
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    os.path.join(a , 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 	lowerCamelCase__	(   *_a							,     **_a):
	SCREAMING_SNAKE_CASE				: str			       =    [(arg.to_raw() if isinstance(_a							,     _a) else arg) for arg in args]
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {k: (v.to_raw() if isinstance(_a							,     _a) else v) for k, v in kwargs.items()}
	return args, kwargs


def 	lowerCamelCase__	(   _a							,     _a=None):
	if isinstance(_a							,     _a):
		SCREAMING_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:
					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				: str			       =    _v(_a)
				if k not in decoded_outputs:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    AgentType[v]

	elif isinstance(_a							,     (list, tuple)):
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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				: Optional[int]			       =    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				: Tuple			       =    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

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	if len(_a) == 0:
		raise ValueError("find_min() arg is an empty sequence")
	if (
	    left >= len(_a)
	    or left < -len(_a)
	    or right >= len(_a)
	    or right < -len(_a)
	):
		raise IndexError("list index out of range")
	if left == right:
		return nums[left]
	SCREAMING_SNAKE_CASE				: List[str]			       =    (left + right) >> 1  # the middle
	SCREAMING_SNAKE_CASE				: Tuple			       =    find_min(_a							,     _a							,     _a)  # find min in range[left, mid]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    find_min(_a							,     mid + 1							,     _a)  # find min in range[mid + 1, right]

	return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)