'''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


_lowercase		:					Optional[int]													=		logging.get_logger(__name__)

if is_vision_available():
						import PIL.Image
						from PIL import Image
						from PIL.Image import Image as ImageType
else:
						_lowercase		:					Tuple													=		object

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf





class        __magic_name__		:







			def __init__(      self							:    str							,     lowercase_							:    Dict       ):
										lowercase_   :     List[str]									=			value







			def __str__(      self							:    int       ):
										return self.to_string()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										logger.error(
										    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""       )
										return self._value







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[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        __magic_name__		(       _UpperCAmelCase,	_UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										return self._value







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										return self._value





class        __magic_name__		(       _UpperCAmelCase,	_UpperCAmelCase):







			def __init__(      self							:    Any							,     lowercase_							:    List[str]       ):
										super().__init__(lowercase_       )

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

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

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    Optional[int]=None							,     lowercase_							:    Optional[int]=None       ):
										from IPython.display import Image, display

										display(Image(self.to_string()       )       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										if self._raw is not None:
																	return self._raw

										if self._path is not None:
																	lowercase_   :     List[str]									=			Image.open(self._path       )
																	return self._raw







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										if self._path is not None:
																	return self._path

										if self._raw is not None:
																	lowercase_   :     Union[str, Any]									=			tempfile.mkdtemp()
																	lowercase_   :     str									=			os.path.join(lowercase_							,     str(uuid.uuida()       ) + """.png"""       )
																	self._raw.save(self._path       )

																	return self._path

										if self._tensor is not None:
																	lowercase_   :     Union[str, Any]									=			self._tensor.cpu().detach().numpy()

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

																	lowercase_   :     Optional[int]									=			tempfile.mkdtemp()
																	lowercase_   :     Any									=			os.path.join(lowercase_							,     str(uuid.uuida()       ) + """.png"""       )

																	img.save(self._path       )

																	return self._path





class        __magic_name__		(       _UpperCAmelCase):







			def __init__(      self							:    List[Any]							,     lowercase_							:    Union[str, Any]							,     lowercase_							:    Tuple=16000       ):
										super().__init__(lowercase_       )

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

										lowercase_   :     str									=			None
										lowercase_   :     Optional[Any]									=			None

										lowercase_   :     Tuple									=			samplerate

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    List[str]=None							,     lowercase_							:    Tuple=None       ):
										from IPython.display import Audio, display

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										if self._tensor is not None:
																	return self._tensor

										if self._path is not None:
																	lowercase_		,		lowercase_   :     Optional[Any]									=			sf.read(self._path       )
																	lowercase_   :     Optional[Any]									=			torch.tensor(lowercase_       )
																	return self._tensor







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										if self._path is not None:
																	return self._path

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




_lowercase		:					int													=		{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
_lowercase		:					Tuple													=		{str: AgentText}

if is_vision_available():
						_lowercase		:					List[str]													=		AgentImage




def 				lowerCamelCase      ( *UpperCAmelCase__			:   Tuple , **UpperCAmelCase__			:   Any					)     -> Dict:
							lowercase_   :     Tuple									=			[(arg.to_raw() if isinstance(UpperCAmelCase__ , UpperCAmelCase__					) else arg) for arg in args]
							lowercase_   :     Dict									=			{k: (v.to_raw() if isinstance(UpperCAmelCase__ , UpperCAmelCase__					) else v) for k, v in kwargs.items()}
							return args, kwargs




def 				lowerCamelCase      ( UpperCAmelCase__			:   int , UpperCAmelCase__			:   List[Any]=None					)     -> List[str]:
							if isinstance(UpperCAmelCase__ , UpperCAmelCase__					):
														lowercase_   :     Tuple									=			{}
														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:
																																			lowercase_   :     Dict									=			AGENT_TYPE_MAPPING[output_types[i]](UpperCAmelCase__					)
																												else:
																																			lowercase_   :     Optional[Any]									=			AgentType(UpperCAmelCase__					)

																					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(UpperCAmelCase__ , _k					):
																																										lowercase_   :     Dict									=			_v(UpperCAmelCase__					)
																												if k not in decoded_outputs:
																																			lowercase_   :     Optional[Any]									=			AgentType[v]

							elif isinstance(UpperCAmelCase__ , (list, tuple)					):
														lowercase_   :     int									=			type(UpperCAmelCase__					)()
														for i, v in enumerate(UpperCAmelCase__					):
																					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]](UpperCAmelCase__					)					)
																												else:
																																			decoded_outputs.append(AgentType(UpperCAmelCase__					)					)
																					else:
																												# If the class does not have defined output, then we map according to the type
																												lowercase_   :     str									=			False
																												for _k, _v in INSTANCE_TYPE_MAPPING.items():
																																			if isinstance(UpperCAmelCase__ , _k					):
																																										decoded_outputs.append(_v(UpperCAmelCase__					)					)
																																										lowercase_   :     List[Any]									=			True

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

							else:
														if output_types[0] in AGENT_TYPE_MAPPING:
																					# If the class has defined outputs, we can map directly according to the class definition
																					lowercase_   :     str									=			AGENT_TYPE_MAPPING[output_types[0]](UpperCAmelCase__					)

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

							return decoded_outputs






'''simple docstring'''




import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:					int													=		[(1,)]




def 				lowerCamelCase      ( )     -> Dict:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow:
														assert True
							else:
														assert False, "should have been skipped"




def 				lowerCamelCase      ( )     -> List[str]:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow and torch_device == "cuda":
														assert True
							else:
														assert False, "should have been skipped"





@require_torch
class        __magic_name__		(       unittest.TestCase):







			@slow
			@require_torch_gpu
			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										check_slow_torch_cuda()







			@require_torch_gpu
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										check_slow_torch_cuda()







			@parameterized.expand(lowercase_       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    Dict=None       ):
										check_slow()




@slow
@require_torch_gpu
def 				lowerCamelCase      ( )     -> Union[str, Any]:
							check_slow_torch_cuda()




@require_torch_gpu
@slow
def 				lowerCamelCase      ( )     -> Any:
							check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param""" , [1]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str]					)     -> List[Any]:
							check_slow()




@pytest.mark.parametrize("""param""" , [1]					)
@slow
def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any]					)     -> Any:
							check_slow()





