




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 							__SCREAMING_SNAKE_CASE				:

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				value

					def __str__(							self   ):







						'''simple docstring'''




						return self.to_string()

					def       lowercase_				(							self   ):







						'''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   ):







						'''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 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase  ,	lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						return self._value





					def       lowercase_				(							self   ):







						'''simple docstring'''




						return self._value


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase  ,	lowerCamelCase    ):

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




						super().__init__(__lowercase   )

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

						lowerCamelCase_				:						str				  =				None
						lowerCamelCase_				:						Any				  =				None
						lowerCamelCase_				:						str				  =				None

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

					def       lowercase_				(							self					,			__lowercase=None					,			__lowercase=None   ):







						'''simple docstring'''




						from IPython.display import Image, display

						display(Image(self.to_string()   )   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




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

						if self._path is not None:
							lowerCamelCase_				:						int				  =				Image.open(self._path   )
							return self._raw





					def       lowercase_				(							self   ):







						'''simple docstring'''




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

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

							return self._path

						if self._tensor is not None:
							lowerCamelCase_				:						Optional[int]				  =				self._tensor.cpu().detach().numpy()

							# There is likely simpler than load into image into save
							lowerCamelCase_				:						Optional[int]				  =				Image.fromarray((array * 255).astype(np.uinta   )   )

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

							img.save(self._path   )

							return self._path


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def __init__(							self					,			__lowercase					,			__lowercase=16_000   ):







						'''simple docstring'''




						super().__init__(__lowercase   )

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

						lowerCamelCase_				:						Optional[int]				  =				None
						lowerCamelCase_				:						List[str]				  =				None

						lowerCamelCase_				:						Union[str, Any]				  =				samplerate

						if isinstance(__lowercase					,			(str, pathlib.Path)   ):
							lowerCamelCase_				:						Optional[int]				  =				value
						elif isinstance(__lowercase					,			torch.Tensor   ):
							lowerCamelCase_				:						Optional[int]				  =				value
						else:
							raise ValueError(f"Unsupported audio type: {type(__lowercase   )}"   )

					def       lowercase_				(							self					,			__lowercase=None					,			__lowercase=None   ):







						'''simple docstring'''




						from IPython.display import Audio, display

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

					def       lowercase_				(							self   ):







						'''simple docstring'''




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

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





					def       lowercase_				(							self   ):







						'''simple docstring'''




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

						if self._tensor is not None:
							lowerCamelCase_				:						List[Any]				  =				tempfile.mkdtemp()
							lowerCamelCase_				:						str				  =				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    snake_case   (				*A		,  **A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				[(arg.to_raw() if isinstance(A		,  A     ) else arg) for arg in args]
	lowerCamelCase_				:						int				  =				{k: (v.to_raw() if isinstance(A		,  A     ) else v) for k, v in kwargs.items()}
	return args, kwargs






def    snake_case   (				A		,  A=None     )				->				Tuple:




	'''simple docstring'''


	if isinstance(A		,  A     ):
		lowerCamelCase_				:						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:
					lowerCamelCase_				:						int				  =				AGENT_TYPE_MAPPING[output_types[i]](A     )
				else:
					lowerCamelCase_				:						str				  =				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     ):
						lowerCamelCase_				:						str				  =				_v(A     )
				if k not in decoded_outputs:
					lowerCamelCase_				:						str				  =				AgentType[v]

	elif isinstance(A		,  (list, tuple)     ):
		lowerCamelCase_				:						Optional[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
				lowerCamelCase_				:						List[Any]				  =				False
				for _k, _v in INSTANCE_TYPE_MAPPING.items():
					if isinstance(A		,  _k     ):
						decoded_outputs.append(_v(A     )     )
						lowerCamelCase_				:						Tuple				  =				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
			lowerCamelCase_				:						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






UpperCamelCase__						=		[
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]






def    snake_case   (				A		,  A		,  A		,  A     )				->				bool:




	'''simple docstring'''


	return 0 <= row < rows and 0 <= col < cols






def    snake_case   (				A		,  A		,  A		,  A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				len(A     )
	lowerCamelCase_				:						Any				  =				len(mat[0]     )
	if is_safe(A		,  A		,  A		,  A     ) and (row, col) not in seen and mat[row][col] == 1:
		seen.add((row, col)     )
		return (
		    1
		    + depth_first_search(row + 1		,  A		,  A		,  A     )
		    + depth_first_search(row - 1		,  A		,  A		,  A     )
		    + depth_first_search(A		,  col + 1		,  A		,  A     )
		    + depth_first_search(A		,  col - 1		,  A		,  A     )
		)
	else:
		return 0






def    snake_case   (				A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						set				  =				set()

	lowerCamelCase_				:						Optional[Any]				  =				0
	for row, line in enumerate(A     ):
		for col, item in enumerate(A     ):
			if item == 1 and (row, col) not in seen:
				# Maximizing the area
				lowerCamelCase_				:						Optional[Any]				  =				max(A		,  depth_first_search(A		,  A		,  A		,  A     )     )
	return max_area


if __name__ == "__main__":
							import doctest

							print(find_max_area(matrix))  # Output -> 6
							doctest.testmod()
