
"""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_						    =					logging.get_logger(__name__)

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

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : List[Any]     ,   a_       : Dict )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  value




 def __str__( self       : str )->					List[Any]:



      """simple docstring"""

      return self.to_string()




 def   a      ( self       : Union[str, Any] )->					Dict:



      """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       : List[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 		UpperCAmelCase_	(lowerCamelCase_  , lowerCamelCase_						):
 """simple docstring"""




 def   a      ( self       : Optional[int] )->					Optional[int]:



      """simple docstring"""

      return self._value






 def   a      ( self       : List[str] )->					List[Any]:



      """simple docstring"""

      return self._value



class 		UpperCAmelCase_	(lowerCamelCase_  , lowerCamelCase_						):
 """simple docstring"""




 def __init__( self       : int     ,   a_       : List[Any] )->					str:



      """simple docstring"""

      super().__init__(a_ )

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

      UpperCAmelCase_						:    Tuple    					=  None
      UpperCAmelCase_						:    Optional[Any]    					=  None
      UpperCAmelCase_						:    List[str]    					=  None

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




 def   a      ( self       : Any     ,   a_       : List[str]=None     ,   a_       : str=None )->					int:



      """simple docstring"""

      from IPython.display import Image, display

      display(Image(self.to_string() ) )




 def   a      ( self       : str )->					str:



      """simple docstring"""

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

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






 def   a      ( self       : Any )->					List[str]:



      """simple docstring"""

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

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

           return self._path

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

           # There is likely simpler than load into image into save
           UpperCAmelCase_						:    Any    					=  Image.fromarray((array * 2_55).astype(np.uinta ) )

           UpperCAmelCase_						:    Dict    					=  tempfile.mkdtemp()
           UpperCAmelCase_						:    Dict    					=  os.path.join(a_     ,   str(uuid.uuida() ) + """.png""" )

           img.save(self._path )

           return self._path



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 def __init__( self       : Any     ,   a_       : Dict     ,   a_       : Optional[int]=1_60_00 )->					List[Any]:



      """simple docstring"""

      super().__init__(a_ )

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

      UpperCAmelCase_						:    str    					=  None
      UpperCAmelCase_						:    Any    					=  None

      UpperCAmelCase_						:    Dict    					=  samplerate

      if isinstance(a_     ,   (str, pathlib.Path) ):
           UpperCAmelCase_						:    Union[str, Any]    					=  value
      elif isinstance(a_     ,   torch.Tensor ):
           UpperCAmelCase_						:    Any    					=  value
      else:
           raise ValueError(f'''Unsupported audio type: {type(a_ )}''' )




 def   a      ( self       : Dict     ,   a_       : Optional[int]=None     ,   a_       : Dict=None )->					Optional[Any]:



      """simple docstring"""

      from IPython.display import Audio, display

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




 def   a      ( self       : Dict )->					List[Any]:



      """simple docstring"""

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

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






 def   a      ( self       : Dict )->					List[Any]:



      """simple docstring"""

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

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

lowercase_						    =					{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
lowercase_						    =					{str: AgentText}

if is_vision_available():
  lowercase_						    =					AgentImage




def 			A_			(					*lowercase						, **lowercase      )     ->							List[str]:


     """simple docstring"""







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




def 			A_			(					lowercase						, lowercase=None      )     ->							Optional[Any]:


     """simple docstring"""







     if isinstance(lowercase						, lowercase      ):
          UpperCAmelCase_						:    Optional[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:
                         UpperCAmelCase_						:    Union[str, Any]    					=  AGENT_TYPE_MAPPING[output_types[i]](lowercase      )
                    else:
                         UpperCAmelCase_						:    Optional[int]    					=  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_						:    Optional[Any]    					=  _v(lowercase      )
                    if k not in decoded_outputs:
                         UpperCAmelCase_						:    str    					=  AgentType[v]

     elif isinstance(lowercase						, (list, tuple)      ):
          UpperCAmelCase_						:    Optional[int]    					=  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_						:    Any    					=  False
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(lowercase						, _k      ):
                              decoded_outputs.append(_v(lowercase      )      )
                              UpperCAmelCase_						:    Dict    					=  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_						:    str    					=  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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_						    =					{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_						    =					[
           "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VisualBertForMultipleChoice",
           "VisualBertForPreTraining",
           "VisualBertForQuestionAnswering",
           "VisualBertForRegionToPhraseAlignment",
           "VisualBertForVisualReasoning",
           "VisualBertLayer",
           "VisualBertModel",
           "VisualBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_visual_bert import (
                  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VisualBertForMultipleChoice,
                  VisualBertForPreTraining,
                  VisualBertForQuestionAnswering,
                  VisualBertForRegionToPhraseAlignment,
                  VisualBertForVisualReasoning,
                  VisualBertLayer,
                  VisualBertModel,
                  VisualBertPreTrainedModel,
              )


else:
       import sys

       lowercase_						    =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

