






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_   :	Tuple							      =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[Any]							      =object

if is_torch_available():
					import torch

if is_soundfile_availble():
					import soundfile as sf





class        a__      :






		def __init__(      self							, lowercase__						)				->				Optional[Any]:
				__A    =							value






		def __str__(      self						)				->				Tuple:
				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						)				->				str:
				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__      (	lowerCAmelCase__	,       lowerCAmelCase__		):






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				return self._value






		def 					_lowerCamelCase					(      self						)				->				str:
				return self._value





class        a__      (	lowerCAmelCase__	,       lowerCAmelCase__		):






		def __init__(      self							, lowercase__						)				->				List[str]:
				super().__init__(lowercase__						)

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

				__A    =							None
				__A    =							None
				__A    =							None

				if isinstance(lowercase__							, lowercase__						):
						__A    =							value
				elif isinstance(lowercase__							, (str, pathlib.Path)						):
						__A    =							value
				elif isinstance(lowercase__							, torch.Tensor						):
						__A    =							value
				else:
						raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(lowercase__						)}"""						)






		def 					_lowerCamelCase					(      self							, lowercase__=None							, lowercase__=None						)				->				List[str]:
				from IPython.display import Image, display

				display(Image(self.to_string()						)						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				if self._raw is not None:
						return self._raw

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

						return self._path

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

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

						__A    =							tempfile.mkdtemp()
						__A    =							os.path.join(lowercase__							, str(uuid.uuida()						) + ".png"						)

						img.save(self._path						)

						return self._path





class        a__      (	lowerCAmelCase__		):






		def __init__(      self							, lowercase__							, lowercase__=16000						)				->				Tuple:
				super().__init__(lowercase__						)

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

				__A    =							None
				__A    =							None

				__A    =							samplerate

				if isinstance(lowercase__							, (str, pathlib.Path)						):
						__A    =							value
				elif isinstance(lowercase__							, torch.Tensor						):
						__A    =							value
				else:
						raise ValueError(F"""Unsupported audio type: {type(lowercase__						)}"""						)






		def 					_lowerCamelCase					(      self							, lowercase__=None							, lowercase__=None						)				->				List[str]:
				from IPython.display import Audio, display

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






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				if self._tensor is not None:
						return self._tensor

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



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

if is_vision_available():
					snake_case_   :	str							      =AgentImage



def      UpperCAmelCase						( *lowerCAmelCase__					,     **lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							[(arg.to_raw() if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	) else arg) for arg in args]
		__A    =							{k: (v.to_raw() if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	) else v) for k, v in kwargs.items()}
		return args, kwargs





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__=None	):


		'''simple docstring'''

		if isinstance(lowerCAmelCase__					,     lowerCAmelCase__	):
				__A    =							{}
				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:
										__A    =							AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase__	)
								else:
										__A    =							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	):
												__A    =							_v(lowerCAmelCase__	)
								if k not in decoded_outputs:
										__A    =							AgentType[v]

		elif isinstance(lowerCAmelCase__					,     (list, tuple)	):
				__A    =							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
								__A    =							False
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(lowerCAmelCase__					,     _k	):
												decoded_outputs.append(_v(lowerCAmelCase__	)	)
												__A    =							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
						__A    =							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'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		assert b != 0, "This cannot accept modulo that is == 0"
		if n == 0:
				return 1

		if n % 2 == 1:
				return (bin_exp_mod(SCREAMING_SNAKE_CASE ,		n - 1 ,		SCREAMING_SNAKE_CASE     ) * a) % b

		SCREAMING_SNAKE_CASE_						:List[str]										=      bin_exp_mod(SCREAMING_SNAKE_CASE ,		n / 2 ,		SCREAMING_SNAKE_CASE     )
		return (r * r) % b


if __name__ == "__main__":
							try:
														SCREAMING_SNAKE_CASE__     : Any				=   int(input("""Enter Base : """).strip())
														SCREAMING_SNAKE_CASE__     : str				=   int(input("""Enter Power : """).strip())
														SCREAMING_SNAKE_CASE__     : List[Any]				=   int(input("""Enter Modulo : """).strip())
							except ValueError:
														print("""Invalid literal for integer""")

							print(bin_exp_mod(BASE, POWER, MODULO))
