

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		      = logging.get_logger(__name__)

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

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf




class A						:






      def __init__(      self		:  Tuple    ,     lowercase_		:  Any				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=value






      def __str__(      self		:  List[str]				)      ->		Tuple:
             """simple docstring"""




             return self.to_string()






      def  lowerCamelCase (      self		:  List[Any]				)      ->		int:
             """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  lowerCamelCase (      self		:  str				)      ->		str:
             """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 A						(     UpperCamelCase_					,							UpperCamelCase_  ):






      def  lowerCamelCase (      self		:  str				)      ->		Tuple:
             """simple docstring"""




             return self._value






      def  lowerCamelCase (      self		:  Tuple				)      ->		int:
             """simple docstring"""




             return self._value




class A						(     UpperCamelCase_					,							UpperCamelCase_  ):






      def __init__(      self		:  Dict    ,     lowercase_		:  Optional[int]				)      ->		List[str]:
             """simple docstring"""




             super().__init__(lowercase_				)

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

             _lowerCamelCase :	int						=None
             _lowerCamelCase :	Dict						=None
             _lowerCamelCase :	Any						=None

             if isinstance(lowercase_    ,     lowercase_				):
                    _lowerCamelCase :	str						=value
             elif isinstance(lowercase_    ,     (str, pathlib.Path)				):
                    _lowerCamelCase :	List[str]						=value
             elif isinstance(lowercase_    ,     torch.Tensor				):
                    _lowerCamelCase :	int						=value
             else:
                    raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(lowercase_				)}'''				)






      def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  int=None    ,     lowercase_		:  int=None				)      ->		List[Any]:
             """simple docstring"""




             from IPython.display import Image, display

             display(Image(self.to_string()				)				)






      def  lowerCamelCase (      self		:  List[str]				)      ->		Dict:
             """simple docstring"""




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

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






      def  lowerCamelCase (      self		:  str				)      ->		int:
             """simple docstring"""




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

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

                    return self._path

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

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

                    _lowerCamelCase :	List[Any]						=tempfile.mkdtemp()
                    _lowerCamelCase :	str						=os.path.join(lowercase_    ,     str(uuid.uuida()				) + '.png'				)

                    img.save(self._path				)

                    return self._path




class A						(     UpperCamelCase_  ):






      def __init__(      self		:  Dict    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Optional[int]=1_6000				)      ->		List[Any]:
             """simple docstring"""




             super().__init__(lowercase_				)

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

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

             _lowerCamelCase :	List[Any]						=samplerate

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






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  List[Any]=None    ,     lowercase_		:  Optional[int]=None				)      ->		List[str]:
             """simple docstring"""




             from IPython.display import Audio, display

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






      def  lowerCamelCase (      self		:  Tuple				)      ->		Optional[Any]:
             """simple docstring"""




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

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






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Any:
             """simple docstring"""




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

             if self._tensor is not None:
                    _lowerCamelCase :	Dict						=tempfile.mkdtemp()
                    _lowerCamelCase :	Union[str, Any]						=os.path.join(lowercase_    ,     str(uuid.uuida()				) + '.wav'				)
                    sf.write(self._path    ,     self._tensor    ,     samplerate=self.samplerate				)
                    return self._path




lowerCamelCase		      = {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
lowerCamelCase		      = {str: AgentText}

if is_vision_available():
 lowerCamelCase		      = AgentImage




def 			a_   (					*SCREAMING_SNAKE_CASE__      :   Tuple	,    **SCREAMING_SNAKE_CASE__      :   Tuple  ):
       '''simple docstring'''





       _lowerCamelCase :	List[Any]						=[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) else arg) for arg in args]
       _lowerCamelCase :	Tuple						={k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) else v) for k, v in kwargs.items()}
       return args, kwargs




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   List[Any]=None  ):
       '''simple docstring'''





       if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              _lowerCamelCase :	Optional[int]						={}
              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 :	Tuple						=AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__  )
                            else:
                                   _lowerCamelCase :	Tuple						=AgentType(SCREAMING_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(SCREAMING_SNAKE_CASE__	,    _k  ):
                                          _lowerCamelCase :	Any						=_v(SCREAMING_SNAKE_CASE__  )
                            if k not in decoded_outputs:
                                   _lowerCamelCase :	Optional[int]						=AgentType[v]

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

                            if not found:
                                   decoded_outputs.append(AgentType(SCREAMING_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 :	List[str]						=AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_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(SCREAMING_SNAKE_CASE__	,    _k  ):
                                   return _v(SCREAMING_SNAKE_CASE__  )
                     return AgentType(SCREAMING_SNAKE_CASE__  )

       return decoded_outputs






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Optional[Any]   ='AutoFeatureExtractor'
      UpperCamelCase__							:						List[str]   ='Speech2Text2Tokenizer'






      def __init__(      self		:  str    ,     lowercase_		:  Dict    ,     lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             super().__init__(lowercase_    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=self.feature_extractor
             _lowerCamelCase :	Tuple						=False






      def __call__(      self		:  Tuple    ,     *lowercase_		:  str    ,     **lowercase_		:  List[Any]				)      ->		List[str]:
             """simple docstring"""




             if self._in_target_context_manager:
                    return self.current_processor(*lowercase_    ,     **lowercase_				)

             if "raw_speech" in kwargs:
                    warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'				)
                    _lowerCamelCase :	List[str]						=kwargs.pop('raw_speech'				)
             else:
                    _lowerCamelCase :	str						=kwargs.pop('audio'    ,     lowercase_				)
             _lowerCamelCase :	Optional[Any]						=kwargs.pop('sampling_rate'    ,     lowercase_				)
             _lowerCamelCase :	List[str]						=kwargs.pop('text'    ,     lowercase_				)
             if len(lowercase_				) > 0:
                    _lowerCamelCase :	Dict						=args[0]
                    _lowerCamelCase :	Optional[Any]						=args[1:]

             if audio is None and text is None:
                    raise ValueError('You need to specify either an `audio` or `text` input to process.'				)

             if audio is not None:
                    _lowerCamelCase :	List[Any]						=self.feature_extractor(lowercase_    ,     *lowercase_    ,     sampling_rate=lowercase_    ,     **lowercase_				)
             if text is not None:
                    _lowerCamelCase :	Tuple						=self.tokenizer(lowercase_    ,     **lowercase_				)

             if text is None:
                    return inputs
             elif audio is None:
                    return encodings
             else:
                    _lowerCamelCase :	Dict						=encodings['input_ids']
                    return inputs






      def  lowerCamelCase (      self		:  str    ,     *lowercase_		:  List[Any]    ,     **lowercase_		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




             return self.tokenizer.batch_decode(*lowercase_    ,     **lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]    ,     *lowercase_		:  Dict    ,     **lowercase_		:  Optional[int]				)      ->		str:
             """simple docstring"""




             return self.tokenizer.decode(*lowercase_    ,     **lowercase_				)






      @contextmanager
      def  lowerCamelCase (      self		:  Optional[Any]				)      ->		Tuple:
             """simple docstring"""




             warnings.warn(
                 '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
                 'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
                 'your audio inputs, or in a separate call.'				)
             _lowerCamelCase :	Optional[Any]						=True
             _lowerCamelCase :	List[Any]						=self.tokenizer
             yield
             _lowerCamelCase :	Optional[int]						=self.feature_extractor
             _lowerCamelCase :	List[str]						=False


