




"""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


__A :		Union[str, Any]      							=    logging.get_logger(__name__)

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

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf
class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Optional[int]          =							value







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







   def       a_							(							self ):
       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 ):
       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		( _UpperCAmelCase	,  _UpperCAmelCase				):







   def       a_							(							self ):
       return self._value







   def       a_							(							self ):
       return self._value







class 							lowerCamelCase		( _UpperCAmelCase	,  _UpperCAmelCase				):







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       super().__init__(SCREAMING_SNAKE_CASE_ )

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

       UpperCamelCase				:   Optional[Any]          =							None
       UpperCamelCase				:   Dict          =							None
       UpperCamelCase				:   str          =							None

       if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
           UpperCamelCase				:   Union[str, Any]          =							value
       elif isinstance(SCREAMING_SNAKE_CASE_    ,      (str, pathlib.Path) ):
           UpperCamelCase				:   Union[str, Any]          =							value
       elif isinstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor ):
           UpperCamelCase				:   Any          =							value
       else:
           raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_ )}' )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None ):
       from IPython.display import Image, display

       display(Image(self.to_string() ) )







   def       a_							(							self ):
       if self._raw is not None:
           return self._raw

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







   def       a_							(							self ):
       if self._path is not None:
           return self._path

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

           return self._path

       if self._tensor is not None:
           UpperCamelCase				:   Optional[int]          =							self._tensor.cpu().detach().numpy()

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

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

           img.save(self._path )

           return self._path







class 							lowerCamelCase		( _UpperCAmelCase				):







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=1_6000 ):
       super().__init__(SCREAMING_SNAKE_CASE_ )

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

       UpperCamelCase				:   Tuple          =							None
       UpperCamelCase				:   Union[str, Any]          =							None

       UpperCamelCase				:   Union[str, Any]          =							samplerate

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







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None ):
       from IPython.display import Audio, display

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







   def       a_							(							self ):
       if self._tensor is not None:
           return self._tensor

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







   def       a_							(							self ):
       if self._path is not None:
           return self._path

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







__A :		int      							=    {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
__A :		List[Any]      							=    {str: AgentText}

if is_vision_available():
  __A :		Optional[Any]      							=    AgentImage
def 					A_			(		*snake_case_     :       List[str]						,**snake_case_     :       Any    ):







    '''simple docstring'''
    UpperCamelCase				:   Tuple          =							[(arg.to_raw() if isinstance(snake_case_						,snake_case_    ) else arg) for arg in args]
    UpperCamelCase				:   Union[str, Any]          =							{k: (v.to_raw() if isinstance(snake_case_						,snake_case_    ) else v) for k, v in kwargs.items()}
    return args, kwargs


def 					A_			(		snake_case_     :       Optional[int]						,snake_case_     :       Tuple=None    ):







    '''simple docstring'''
    if isinstance(snake_case_						,snake_case_    ):
        UpperCamelCase				:   str          =							{}
        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				:   int          =							AGENT_TYPE_MAPPING[output_types[i]](snake_case_    )
                else:
                    UpperCamelCase				:   str          =							AgentType(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(snake_case_						,_k    ):
                        UpperCamelCase				:   Union[str, Any]          =							_v(snake_case_    )
                if k not in decoded_outputs:
                    UpperCamelCase				:   Optional[int]          =							AgentType[v]

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

                if not found:
                    decoded_outputs.append(AgentType(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
            UpperCamelCase				:   Any          =							AGENT_TYPE_MAPPING[output_types[0]](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(snake_case_						,_k    ):
                    return _v(snake_case_    )
            return AgentType(snake_case_    )

    return decoded_outputs










"""simple docstring"""




from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
  from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline




