



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 				a_    :

						def __init__(       self      :Optional[int]  ,		_lowercase      :Tuple)						->   Tuple:
												UpperCAmelCase_					=	value

						def __str__(       self      :List[Any])						->   Union[str, Any]:
												return self.to_string()

						def       __a			(       self      :Dict)						->   Union[str, Any]:
												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      :List[Any])						->   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_    (						lowercase_   ,					lowercase_							):

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




						def       __a			(       self      :Tuple)						->   List[str]:
												return self._value







class 				a_    (						lowercase_   ,					lowercase_							):

						def __init__(       self      :Tuple  ,		_lowercase      :Any)						->   str:
												super().__init__(snake_case__)

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

												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None

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

						def       __a			(       self      :str  ,		_lowercase      :Dict=None  ,		_lowercase      :Optional[Any]=None)						->   str:
												from IPython.display import Image, display

												display(Image(self.to_string()))

						def       __a			(       self      :Any)						->   str:



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

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




						def       __a			(       self      :Optional[Any])						->   Tuple:



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

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

																		return self._path

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

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

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

																		img.save(self._path)

																		return self._path







class 				a_    (						lowercase_							):

						def __init__(       self      :List[str]  ,		_lowercase      :Union[str, Any]  ,		_lowercase      :Optional[Any]=16000)						->   Tuple:
												super().__init__(snake_case__)

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

												UpperCAmelCase_					=	None
												UpperCAmelCase_					=	None

												UpperCAmelCase_					=	samplerate

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

						def       __a			(       self      :List[Any]  ,		_lowercase      :int=None  ,		_lowercase      :Any=None)						->   Optional[int]:
												from IPython.display import Audio, display

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

						def       __a			(       self      :Optional[Any])						->   Optional[int]:



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

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




						def       __a			(       self      :List[str])						->   str:



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

												if self._tensor is not None:
																		UpperCAmelCase_					=	tempfile.mkdtemp()
																		UpperCAmelCase_					=	os.path.join(snake_case__  ,		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 							A     (						*__UpperCAmelCase				,   **__UpperCAmelCase      )			->			Union[str, Any]:



						'''simple docstring'''






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







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=None      )			->			List[str]:



						'''simple docstring'''






						if isinstance(_lowercase				,   _lowercase      ):
												UpperCAmelCase_					=	{}
												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:
																														UpperCAmelCase_					=	AGENT_TYPE_MAPPING[output_types[i]](_lowercase      )
																								else:
																														UpperCAmelCase_					=	AgentType(_lowercase      )

																		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(_lowercase				,   _k      ):
																																				UpperCAmelCase_					=	_v(_lowercase      )
																								if k not in decoded_outputs:
																														UpperCAmelCase_					=	AgentType[v]

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

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

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

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

						return decoded_outputs













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCamelCase					    =	{}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50Tokenizer''']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase					    =	['''MBart50TokenizerFast''']


if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa import MBartaaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
   import sys

   lowerCamelCase					    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
