



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


lowerCAmelCase__	:  Optional[Any] 		=logging.get_logger(__name__)

if is_vision_available():
			import PIL.Image
			from PIL import Image
			from PIL.Image import Image as ImageType
else:
			lowerCAmelCase__	:  str 		=object

if is_torch_available():
			import torch

if is_soundfile_availble():
			import soundfile as sf

class      UpperCAmelCase_						:

						'''simple docstring'''




						def __init__(					self   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      value






						def __str__(					self	):







													'''simple docstring'''

													return self.to_string()






						def  _A			(					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  _A			(					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      UpperCAmelCase_						(     UpperCamelCase_					,					UpperCamelCase_ ):

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

													return self._value






						def  _A			(					self	):







													'''simple docstring'''

													return self._value







class      UpperCAmelCase_						(     UpperCamelCase_					,					UpperCamelCase_ ):

						'''simple docstring'''




						def __init__(					self   ,							_A	):







													'''simple docstring'''

													super().__init__(_A	)

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

													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      None

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






						def  _A			(					self   ,							_A=None   ,							_A=None	):







													'''simple docstring'''

													from IPython.display import Image, display

													display(Image(self.to_string()	)	)






						def  _A			(					self	):







													'''simple docstring'''

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

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






						def  _A			(					self	):







													'''simple docstring'''

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

													if self._raw is not None:
																				__SCREAMING_SNAKE_CASE								=      tempfile.mkdtemp()
																				__SCREAMING_SNAKE_CASE								=      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								=      self._tensor.cpu().detach().numpy()

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

																				__SCREAMING_SNAKE_CASE								=      tempfile.mkdtemp()
																				__SCREAMING_SNAKE_CASE								=      os.path.join(_A   ,							str(uuid.uuida()	) + '.png'	)

																				img.save(self._path	)

																				return self._path







class      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




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







													'''simple docstring'''

													super().__init__(_A	)

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

													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      None

													__SCREAMING_SNAKE_CASE								=      samplerate

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






						def  _A			(					self   ,							_A=None   ,							_A=None	):







													'''simple docstring'''

													from IPython.display import Audio, display

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






						def  _A			(					self	):







													'''simple docstring'''

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

													if self._path is not None:
																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      sf.read(self._path	)
																				__SCREAMING_SNAKE_CASE								=      torch.tensor(_A	)
																				return self._tensor






						def  _A			(					self	):







													'''simple docstring'''

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

													if self._tensor is not None:
																				__SCREAMING_SNAKE_CASE								=      tempfile.mkdtemp()
																				__SCREAMING_SNAKE_CASE								=      os.path.join(_A   ,							str(uuid.uuida()	) + '.wav'	)
																				sf.write(self._path   ,							self._tensor   ,							samplerate=self.samplerate	)
																				return self._path







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

if is_vision_available():
			lowerCAmelCase__	:  int 		=AgentImage






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






def 			__lowercase     (				a__							,     a__=None     )       ->				List[Any]:
							if isinstance(a__							,     a__     ):
														__SCREAMING_SNAKE_CASE								=      {}
														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								=      AGENT_TYPE_MAPPING[output_types[i]](a__     )
																												else:
																																			__SCREAMING_SNAKE_CASE								=      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								=      _v(a__     )
																												if k not in decoded_outputs:
																																			__SCREAMING_SNAKE_CASE								=      AgentType[v]

							elif isinstance(a__							,     (list, tuple)     ):
														__SCREAMING_SNAKE_CASE								=      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								=      False
																												for _k, _v in INSTANCE_TYPE_MAPPING.items():
																																			if isinstance(a__							,     _k     ):
																																										decoded_outputs.append(_v(a__     )     )
																																										__SCREAMING_SNAKE_CASE								=      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								=      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 itertools import combinations, product






def 			__lowercase     (				a__							,     a__							,     a__							,     a__     )       ->				bool:
							if xa == ya == 0 or xa == ya == 0:
														return False
							__SCREAMING_SNAKE_CASE								=      xa * xa + ya * ya
							__SCREAMING_SNAKE_CASE								=      xa * xa + ya * ya
							__SCREAMING_SNAKE_CASE								=      (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
							return (
							    a_square + b_square == c_square
							    or a_square + c_square == b_square
							    or b_square + c_square == a_square
							)






def 			__lowercase     (				a__ = 50     )       ->				int:
							return sum(
							    1
							    for pta, pta in combinations(product(range(limit + 1     )							,     repeat=2     )							,     2     )
							    if is_right(*a__							,     *a__     )     )


if __name__ == "__main__":
			print(F'''{solution() = }''')






