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


__snake_case         =     logging.get_logger(__name__)

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

if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf







class       __snake_case :
 def __init__(	self		, snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=value
 def __str__(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       return self.to_string()
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''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 						UpperCAmelCase__							(	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       __snake_case (  lowerCamelCase__       , lowerCamelCase__ ):
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       return self._value






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       return self._value







class       __snake_case (  lowerCamelCase__       , lowerCamelCase__ ):
 def __init__(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       super().__init__(snake_case__    )

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

       UpperCAmelCase     :			Tuple     	=None
       UpperCAmelCase     :			List[str]     	=None
       UpperCAmelCase     :			Dict     	=None

       if isinstance(snake_case__		, snake_case__    ):
             UpperCAmelCase     :			Any     	=value
       elif isinstance(snake_case__		, (str, pathlib.Path)    ):
             UpperCAmelCase     :			Optional[int]     	=value
       elif isinstance(snake_case__		, torch.Tensor    ):
             UpperCAmelCase     :			Optional[Any]     	=value
       else:
             raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(snake_case__    )}'''    )
 def 						UpperCAmelCase__							(	self		, snake_case__=None		, snake_case__=None    )						->				List[Any]:
       '''simple docstring'''





       from IPython.display import Image, display

       display(Image(self.to_string()    )    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





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

       if self._path is not None:
             UpperCAmelCase     :			List[Any]     	=Image.open(self._path    )
             return self._raw






 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





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

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

             return self._path

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

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

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

             img.save(self._path    )

             return self._path







class       __snake_case (  lowerCamelCase__ ):
 def __init__(	self		, snake_case__		, snake_case__=1_6000    )						->				Any:
       '''simple docstring'''





       super().__init__(snake_case__    )

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

       UpperCAmelCase     :			str     	=None
       UpperCAmelCase     :			Optional[Any]     	=None

       UpperCAmelCase     :			Optional[int]     	=samplerate

       if isinstance(snake_case__		, (str, pathlib.Path)    ):
             UpperCAmelCase     :			Any     	=value
       elif isinstance(snake_case__		, torch.Tensor    ):
             UpperCAmelCase     :			Optional[int]     	=value
       else:
             raise ValueError(f'''Unsupported audio type: {type(snake_case__    )}'''    )
 def 						UpperCAmelCase__							(	self		, snake_case__=None		, snake_case__=None    )						->				str:
       '''simple docstring'''





       from IPython.display import Audio, display

       display(Audio(self.to_string()		, rate=self.samplerate    )    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





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

       if self._path is not None:
             UpperCAmelCase     :			int     	=sf.read(self._path    )
             UpperCAmelCase     :			List[Any]     	=torch.tensor(snake_case__    )
             return self._tensor






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





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

       if self._tensor is not None:
             UpperCAmelCase     :			Optional[int]     	=tempfile.mkdtemp()
             UpperCAmelCase     :			Dict     	=os.path.join(snake_case__		, str(uuid.uuida()    ) + '''.wav'''    )
             sf.write(self._path		, self._tensor		, samplerate=self.samplerate    )
             return self._path



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

if is_vision_available():
     __snake_case         =     AgentImage





def 	lowerCAmelCase_			(					*__lowerCAmelCase		,       **__lowerCAmelCase    )->	int:






      '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase=None    )->	int:






      '''simple docstring'''





      if isinstance(__UpperCAmelCase		,       __UpperCAmelCase    ):
            UpperCAmelCase     :			Optional[int]     	={}
            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]](__UpperCAmelCase    )
                        else:
                              UpperCAmelCase     :			Dict     	=AgentType(__UpperCAmelCase    )

                  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(__UpperCAmelCase		,       _k    ):
                                    UpperCAmelCase     :			Optional[Any]     	=_v(__UpperCAmelCase    )
                        if k not in decoded_outputs:
                              UpperCAmelCase     :			Dict     	=AgentType[v]

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

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

      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     :			List[Any]     	=AGENT_TYPE_MAPPING[output_types[0]](__UpperCAmelCase    )

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

      return decoded_outputs

'''simple docstring'''
from typing import TYPE_CHECKING

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


_A		:      Optional[Any]						   ={
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:      Union[str, Any]						   =[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 _A		:      Tuple						   =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)