


"""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


snake_case__		:  Any     					=					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__		:  str     					=					object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf



class 		snake_case_:






     def __init__(						self	:       int       ,					UpperCamelCase_	:       Any					):
           lowerCAmelCase       :						List[Any]							= value






     def __str__(						self	:       Optional[int]					):
           return self.to_string()






     def   lowerCamelCase__  (						self	:       Tuple					):
           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	:       Union[str, 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 		snake_case_(							a__				,       a__      ):






     def   lowerCamelCase__  (						self	:       Any					):
           return self._value






     def   lowerCamelCase__  (						self	:       Any					):
           return self._value



class 		snake_case_(							a__				,       a__      ):






     def __init__(						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Tuple					):
           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   lowerCamelCase__  (						self	:       Any       ,					UpperCamelCase_	:       Dict=None       ,					UpperCamelCase_	:       Union[str, Any]=None					):
           from IPython.display import Image, display

           display(Image(self.to_string()					)					)






     def   lowerCamelCase__  (						self	:       int					):




           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   lowerCamelCase__  (						self	:       Optional[Any]					):




           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 * 2_5_5).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 		snake_case_(							a__      ):






     def __init__(						self	:       Dict       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       List[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       :						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   lowerCamelCase__  (						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[Any]=None       ,					UpperCamelCase_	:       Any=None					):
           from IPython.display import Audio, display

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






     def   lowerCamelCase__  (						self	:       Any					):




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

           if self._path is not None:
                 lowerCAmelCase       :						str							= sf.read(self._path					)
                 lowerCAmelCase       :						List[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[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



snake_case__		:  List[str]     					=					{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
snake_case__		:  Union[str, Any]     					=					{str: AgentText}

if is_vision_available():
 snake_case__		:  str     					=					AgentImage


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






def   _snake_case    (    _snake_case					:      str			, _snake_case					:      Union[str, Any]=None      ):


      if isinstance(_snake_case			, _snake_case      ):
            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]](_snake_case      )
                        else:
                              lowerCAmelCase       :						str							= AgentType(_snake_case      )

                  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(_snake_case			, _k      ):
                                    lowerCAmelCase       :						str							= _v(_snake_case      )
                        if k not in decoded_outputs:
                              lowerCAmelCase       :						str							= AgentType[v]

      elif isinstance(_snake_case			, (list, tuple)      ):
            lowerCAmelCase       :						Optional[int]							= type(_snake_case      )()
            for i, v in enumerate(_snake_case      ):
                  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]](_snake_case      )      )
                        else:
                              decoded_outputs.append(AgentType(_snake_case      )      )
                  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(_snake_case			, _k      ):
                                    decoded_outputs.append(_v(_snake_case      )      )
                                    lowerCAmelCase       :						Tuple							= True

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

      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]](_snake_case      )

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

      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()
