



'''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


_UpperCamelCase							    =						logging.get_logger(__name__)

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

if is_torch_available():
   import torch

if is_soundfile_availble():
   import soundfile as sf






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Tuple      ,     _a				:     Optional[int]       ) -> Union[str, Any]:
    __lowerCamelCase	:	List[str]   	=			value




  def __str__(			self				:     Dict       ) -> Tuple:
    return self.to_string()




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




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> 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  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  def 						_lowercase					(			self				:     List[str]       ) -> Dict:
    return self._value




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Any:
    return self._value






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




  def __init__(			self				:     List[str]      ,     _a				:     Dict       ) -> int:
    super().__init__(_a       )

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

    __lowerCamelCase	:	List[Any]   	=			None
    __lowerCamelCase	:	str   	=			None
    __lowerCamelCase	:	List[str]   	=			None

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




  def 						_lowercase					(			self				:     Dict      ,     _a				:     Union[str, Any]=None      ,     _a				:     str=None       ) -> List[Any]:
    from IPython.display import Image, display

    display(Image(self.to_string()       )       )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Dict:
    if self._raw is not None:
      return self._raw

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




  def 						_lowercase					(			self				:     Dict       ) -> Optional[int]:
    if self._path is not None:
      return self._path

    if self._raw is not None:
      __lowerCamelCase	:	Dict   	=			tempfile.mkdtemp()
      __lowerCamelCase	:	List[str]   	=			os.path.join(_a      ,     str(uuid.uuida()       ) + '.png'       )
      self._raw.save(self._path       )

      return self._path

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

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

      __lowerCamelCase	:	Dict   	=			tempfile.mkdtemp()
      __lowerCamelCase	:	Union[str, Any]   	=			os.path.join(_a      ,     str(uuid.uuida()       ) + '.png'       )

      img.save(self._path       )

      return self._path






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




  def __init__(			self				:     Tuple      ,     _a				:     str      ,     _a				:     List[Any]=1_6000       ) -> int:
    super().__init__(_a       )

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

    __lowerCamelCase	:	Tuple   	=			None
    __lowerCamelCase	:	Optional[int]   	=			None

    __lowerCamelCase	:	Optional[Any]   	=			samplerate

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




  def 						_lowercase					(			self				:     str      ,     _a				:     Union[str, Any]=None      ,     _a				:     List[str]=None       ) -> List[str]:
    from IPython.display import Audio, display

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




  def 						_lowercase					(			self				:     Optional[int]       ) -> str:
    if self._tensor is not None:
      return self._tensor

    if self._path is not None:
      __lowerCamelCase	,__lowerCamelCase	:	int   	=			sf.read(self._path       )
      __lowerCamelCase	:	Any   	=			torch.tensor(_a       )
      return self._tensor




  def 						_lowercase					(			self				:     str       ) -> Tuple:
    if self._path is not None:
      return self._path

    if self._tensor is not None:
      __lowerCamelCase	:	Tuple   	=			tempfile.mkdtemp()
      __lowerCamelCase	:	Optional[Any]   	=			os.path.join(_a      ,     str(uuid.uuida()       ) + '.wav'       )
      sf.write(self._path      ,     self._tensor      ,     samplerate=self.samplerate       )
      return self._path




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

if is_vision_available():
   _UpperCamelCase							    =						AgentImage

def 	a_					(							*_lowerCAmelCase	,**_lowerCAmelCase	)				->				str:
  __lowerCamelCase	:	int   	=			[(arg.to_raw() if isinstance(_lowerCAmelCase	,_lowerCAmelCase	) else arg) for arg in args]
  __lowerCamelCase	:	Any   	=			{k: (v.to_raw() if isinstance(_lowerCAmelCase	,_lowerCAmelCase	) else v) for k, v in kwargs.items()}
  return args, kwargs


def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=None	)				->				List[str]:
  if isinstance(_lowerCAmelCase	,_lowerCAmelCase	):
    __lowerCamelCase	:	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:
          __lowerCamelCase	:	Dict   	=			AGENT_TYPE_MAPPING[output_types[i]](_lowerCAmelCase	)
        else:
          __lowerCamelCase	:	Dict   	=			AgentType(_lowerCAmelCase	)

      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(_lowerCAmelCase	,_k	):
            __lowerCamelCase	:	List[Any]   	=			_v(_lowerCAmelCase	)
        if k not in decoded_outputs:
          __lowerCamelCase	:	Tuple   	=			AgentType[v]

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

        if not found:
          decoded_outputs.append(AgentType(_lowerCAmelCase	)	)

  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	:	Tuple   	=			AGENT_TYPE_MAPPING[output_types[0]](_lowerCAmelCase	)

    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(_lowerCAmelCase	,_k	):
          return _v(_lowerCAmelCase	)
      return AgentType(_lowerCAmelCase	)

  return decoded_outputs











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

   _UpperCamelCase							    =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






