





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       __lowerCAmelCase :






   def __init__(self      ,							__magic_name__					)  ->     List[Any]:


       '''simple docstring'''



       snake_case_		:					Dict			    =						value






   def __str__(self					)  ->     Any:


       '''simple docstring'''



       return self.to_string()






   def     lowerCamelCase						(self					)  ->     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     lowerCamelCase						(self					)  ->     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       __lowerCAmelCase (    __SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE  ):






   def     lowerCamelCase						(self					)  ->     int:


       '''simple docstring'''



       return self._value






   def     lowerCamelCase						(self					)  ->     int:


       '''simple docstring'''



       return self._value




class       __lowerCAmelCase (    __SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE  ):






   def __init__(self      ,							__magic_name__					)  ->     Optional[int]:


       '''simple docstring'''



       super().__init__(UpperCamelCase__					)

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

       snake_case_		:					Optional[int]			    =						None
       snake_case_		:					Any			    =						None
       snake_case_		:					Union[str, Any]			    =						None

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






   def     lowerCamelCase						(self      ,							__magic_name__=None      ,							__magic_name__=None					)  ->     List[Any]:


       '''simple docstring'''



       from IPython.display import Image, display

       display(Image(self.to_string()					)					)






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


       '''simple docstring'''



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

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






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


       '''simple docstring'''



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

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

           return self._path

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

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

           snake_case_		:					Union[str, Any]			    =						tempfile.mkdtemp()
           snake_case_		:					Optional[Any]			    =						os.path.join(UpperCamelCase__      ,							str(uuid.uuida()					) + '''.png'''					)

           img.save(self._path					)

           return self._path




class       __lowerCAmelCase (    __SCREAMING_SNAKE_CASE  ):






   def __init__(self      ,							__magic_name__      ,							__magic_name__=1_6000					)  ->     Any:


       '''simple docstring'''



       super().__init__(UpperCamelCase__					)

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

       snake_case_		:					Any			    =						None
       snake_case_		:					int			    =						None

       snake_case_		:					int			    =						samplerate

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






   def     lowerCamelCase						(self      ,							__magic_name__=None      ,							__magic_name__=None					)  ->     Tuple:


       '''simple docstring'''



       from IPython.display import Audio, display

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






   def     lowerCamelCase						(self					)  ->     Tuple:


       '''simple docstring'''



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

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






   def     lowerCamelCase						(self					)  ->     Any:


       '''simple docstring'''



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

       if self._tensor is not None:
           snake_case_		:					Any			    =						tempfile.mkdtemp()
           snake_case_		:					Optional[Any]			    =						os.path.join(UpperCamelCase__      ,							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   lowerCamelCase_		(       *_UpperCamelCase				,       **_UpperCamelCase				)	->		Dict:






    """simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase=None				)	->		int:






    """simple docstring"""


    if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
        snake_case_		:					Union[str, 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:
                    snake_case_		:					Any			    =						AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__				)
                else:
                    snake_case_		:					List[str]			    =						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				):
                        snake_case_		:					List[Any]			    =						_v(SCREAMING_SNAKE_CASE__				)
                if k not in decoded_outputs:
                    snake_case_		:					Dict			    =						AgentType[v]

    elif isinstance(SCREAMING_SNAKE_CASE__				,       (list, tuple)				):
        snake_case_		:					Any			    =						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
                snake_case_		:					Any			    =						False
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(SCREAMING_SNAKE_CASE__				,       _k				):
                        decoded_outputs.append(_v(SCREAMING_SNAKE_CASE__				)				)
                        snake_case_		:					List[str]			    =						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
            snake_case_		:					int			    =						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







class       __lowerCAmelCase :






			def __init__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						[]
							snake_case_		:					Any			    =						0
							snake_case_		:					Optional[int]			    =						0






			def __str__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					int			    =						'''<''' + str(self.entries					)[1:-1] + '''>'''
							return printed






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Dict:


							'''simple docstring'''



							self.entries.append(__magic_name__					)
							snake_case_		:					List[str]			    =						self.length + 1






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.length - 1
							snake_case_		:					Dict			    =						self.entries[self.front]
							# self.front-=1
							# self.entries = self.entries[self.front:]
							snake_case_		:					List[str]			    =						self.entries[1:]
							return dequeued






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



							for _ in range(__magic_name__					):
											self.put(self.get()					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							return self.entries[0]






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


							'''simple docstring'''



							return self.length
