


"""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


lowerCamelCase_     :     Tuple  		=						logging.get_logger(__name__)

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

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf






class    __A       :






 """simple docstring"""




 def __init__(	self				,					__A		)      ->  Union[str, Any]:
     a					=value


 def __str__(	self		)      ->  Any:
     return self.to_string()


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     logger.error(
         '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''		)
     return self._value





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  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       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




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





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     return self._value






class    __A       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 def __init__(	self				,					__A		)      ->  Optional[Any]:
     super().__init__(__A		)

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

     a					=None
     a					=None
     a					=None

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


 def    SCREAMING_SNAKE_CASE							(	self				,					__A=None				,					__A=None		)      ->  str:
     from IPython.display import Image, display

     display(Image(self.to_string()		)		)


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
     if self._raw is not None:
         return self._raw

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





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
     if self._path is not None:
         return self._path

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

         return self._path

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

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

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

         img.save(self._path		)

         return self._path






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 def __init__(	self				,					__A				,					__A=1_6000		)      ->  Optional[Any]:
     super().__init__(__A		)

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

     a					=None
     a					=None

     a					=samplerate

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


 def    SCREAMING_SNAKE_CASE							(	self				,					__A=None				,					__A=None		)      ->  Optional[Any]:
     from IPython.display import Audio, display

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


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
     if self._tensor is not None:
         return self._tensor

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





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
     if self._path is not None:
         return self._path

     if self._tensor is not None:
         a					=tempfile.mkdtemp()
         a					=os.path.join(__A				,					str(uuid.uuida()		) + '''.wav'''		)
         sf.write(self._path				,					self._tensor				,					samplerate=self.samplerate		)
         return self._path

lowerCamelCase_     :     Any  		=						{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
lowerCamelCase_     :     Optional[Any]  		=						{str: AgentText}

if is_vision_available():
       lowerCamelCase_     :     Dict  		=						AgentImage
def _A		(		*lowercase ,					**lowercase	):







    """simple docstring"""
    a					=[(arg.to_raw() if isinstance(lowercase ,					lowercase	) else arg) for arg in args]
    a					={k: (v.to_raw() if isinstance(lowercase ,					lowercase	) else v) for k, v in kwargs.items()}
    return args, kwargs





def _A		(		lowercase ,					lowercase=None	):







    """simple docstring"""
    if isinstance(lowercase ,					lowercase	):
        a					={}
        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:
                    a					=AGENT_TYPE_MAPPING[output_types[i]](lowercase	)
                else:
                    a					=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	):
                        a					=_v(lowercase	)
                if k not in decoded_outputs:
                    a					=AgentType[v]

    elif isinstance(lowercase ,					(list, tuple)	):
        a					=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
                a					=False
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(lowercase ,					_k	):
                        decoded_outputs.append(_v(lowercase	)	)
                        a					=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
            a					=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"""







import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


lowerCamelCase_     :     int    		=						logging.get_logger(__name__)






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"hybrid-clip"
 __lowerCAmelCase				      =						True


 def __init__(	self				,					__A=512				,					**__A		)      ->  Any:
     super().__init__(**__A		)

     if "text_config" not in kwargs:
         raise ValueError('''`text_config` can not be `None`.'''		)

     if "vision_config" not in kwargs:
         raise ValueError('''`vision_config` can not be `None`.'''		)

     a					=kwargs.pop('''text_config'''		)
     a					=kwargs.pop('''vision_config'''		)

     a					=text_config.pop('''model_type'''		)
     a					=vision_config.pop('''model_type'''		)

     from transformers import AutoConfig

     a					=AutoConfig.for_model(__A				,					**__A		)

     if vision_model_type == "clip":
         a					=AutoConfig.for_model(__A				,					**__A		).vision_config
     elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         a					=CLIPVisionConfig(**__A		)
     else:
         a					=AutoConfig.for_model(__A				,					**__A		)

     a					=projection_dim
     a					=1.0


 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					**__A		)      ->  Tuple:
     return cls(text_config=text_config.to_dict()				,					vision_config=vision_config.to_dict()				,					**__A		)





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
     a					=copy.deepcopy(self.__dict__		)
     a					=self.text_config.to_dict()
     a					=self.vision_config.to_dict()
     a					=self.__class__.model_type
     return output