





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image


def 			lowerCamelCase_		(					lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Tuple=False			,							lowerCAmelCase:		int=False			,							lowerCAmelCase:		Union[str, Any]=False )->						Tuple:
  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  _snake_case      : List[str]														=		[]
  for i in range(image_processor_tester.batch_size ):
    if equal_resolution:
      _snake_case      : Optional[int]														=		image_processor_tester.max_resolution
    else:
      # To avoid getting image width/height 0
      _snake_case      : int														=		image_processor_tester.min_resolution
      if getattr(lowerCAmelCase			,							'size_divisor'			,							lowerCAmelCase ):
        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
        _snake_case      : str														=		max(image_processor_tester.size_divisor			,							lowerCAmelCase )
      _snake_case							,    _snake_case      : int														=		np.random.choice(np.arange(lowerCAmelCase			,							image_processor_tester.max_resolution )			,							2 )
    image_inputs.append(
        np.random.randint(2_55			,							size=(image_processor_tester.num_channels, width, height)			,							dtype=np.uinta ) )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    _snake_case      : Dict														=		[Image.fromarray(np.moveaxis(lowerCAmelCase			,							0			,							-1 ) ) for image in image_inputs]

  if torchify:
    _snake_case      : Optional[int]														=		[torch.from_numpy(lowerCAmelCase ) for image in image_inputs]

  return image_inputs


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		int=10			,							lowerCAmelCase:		Union[str, Any]=10			,							lowerCAmelCase:		int=False			,							lowerCAmelCase:		Optional[int]=False )->						int:
  _snake_case      : int														=		[]
  for i in range(image_processor_tester.num_frames ):
    video.append(np.random.randint(2_55			,							size=(image_processor_tester.num_channels, width, height)			,							dtype=np.uinta ) )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    _snake_case      : List[str]														=		[Image.fromarray(np.moveaxis(lowerCAmelCase			,							0			,							-1 ) ) for frame in video]

  if torchify:
    _snake_case      : Any														=		[torch.from_numpy(lowerCAmelCase ) for frame in video]

  return video


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		Union[str, Any]=False			,							lowerCAmelCase:		int=False			,							lowerCAmelCase:		Union[str, Any]=False )->						Dict:
  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  _snake_case      : Any														=		[]
  for i in range(image_processor_tester.batch_size ):
    if equal_resolution:
      _snake_case      : List[str]														=		image_processor_tester.max_resolution
    else:
      _snake_case							,    _snake_case      : Union[str, Any]														=		np.random.choice(
          np.arange(image_processor_tester.min_resolution			,							image_processor_tester.max_resolution )			,							2 )
      _snake_case      : Tuple														=		prepare_video(
          image_processor_tester=lowerCAmelCase			,							width=lowerCAmelCase			,							height=lowerCAmelCase			,							numpify=lowerCAmelCase			,							torchify=lowerCAmelCase			,							)
    video_inputs.append(lowerCAmelCase )

  return video_inputs







class 				_lowerCAmelCase			:


  '''simple docstring'''
  a_							:				List[Any]					    =None







  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		self.image_processing_class(**self.image_processor_dict							)
    _snake_case      : List[str]														=		json.loads(image_processor.to_json_string()							)
    for key, value in self.image_processor_dict.items():
      self.assertEqual(obj[key]					, UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    _snake_case      : List[str]														=		self.image_processing_class(**self.image_processor_dict							)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _snake_case      : Optional[int]														=		os.path.join(UpperCamelCase					, 'image_processor.json'							)
      image_processor_first.to_json_file(UpperCamelCase							)
      _snake_case      : List[str]														=		self.image_processing_class.from_json_file(UpperCamelCase							)

    self.assertEqual(image_processor_second.to_dict()					, image_processor_first.to_dict()							)







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Any														=		self.image_processing_class(**self.image_processor_dict							)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _snake_case      : List[Any]														=		image_processor_first.save_pretrained(UpperCamelCase							)[0]
      check_json_file_has_correct_format(UpperCamelCase							)
      _snake_case      : List[Any]														=		self.image_processing_class.from_pretrained(UpperCamelCase							)

    self.assertEqual(image_processor_second.to_dict()					, image_processor_first.to_dict()							)







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    _snake_case      : Dict														=		self.image_processing_class()
    self.assertIsNotNone(UpperCamelCase							)







  @require_torch
  @require_vision
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    if self.test_cast_dtype is not None:
      # Initialize image_processor
      _snake_case      : Dict														=		self.image_processing_class(**self.image_processor_dict							)

      # create random PyTorch tensors
      _snake_case      : Optional[int]														=		prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCamelCase					, torchify=UpperCamelCase							)

      _snake_case      : Dict														=		image_processor(UpperCamelCase					, return_tensors='pt'							)
      # for layoutLM compatiblity
      self.assertEqual(encoding.pixel_values.device					, torch.device('cpu'							)							)
      self.assertEqual(encoding.pixel_values.dtype					, torch.floataa							)

      _snake_case      : Dict														=		image_processor(UpperCamelCase					, return_tensors='pt'							).to(torch.floataa							)
      self.assertEqual(encoding.pixel_values.device					, torch.device('cpu'							)							)
      self.assertEqual(encoding.pixel_values.dtype					, torch.floataa							)

      _snake_case      : str														=		image_processor(UpperCamelCase					, return_tensors='pt'							).to('cpu'					, torch.bfloataa							)
      self.assertEqual(encoding.pixel_values.device					, torch.device('cpu'							)							)
      self.assertEqual(encoding.pixel_values.dtype					, torch.bfloataa							)

      with self.assertRaises(UpperCamelCase							):
        _snake_case      : Any														=		image_processor(UpperCamelCase					, return_tensors='pt'							).to(torch.bfloataa					, 'cpu'							)

      # Try with text + image feature
      _snake_case      : Tuple														=		image_processor(UpperCamelCase					, return_tensors='pt'							)
      encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]							)}							)
      _snake_case      : str														=		encoding.to(torch.floataa							)

      self.assertEqual(encoding.pixel_values.device					, torch.device('cpu'							)							)
      self.assertEqual(encoding.pixel_values.dtype					, torch.floataa							)
      self.assertEqual(encoding.input_ids.dtype					, torch.long							)










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   ,	SCREAMING_SNAKE_CASE__     :     List[str]   )      ->       Union[str, Any]:
         lowerCAmelCase__		= value


    def __str__(       self     :     int   )      ->       List[Any]:
         return self.to_string()


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




    """simple docstring"""

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


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






class  __lowerCamelCase       (    UpperCamelCase__	,				UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       Optional[int]:
         super().__init__(SCREAMING_SNAKE_CASE__   )

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

         lowerCAmelCase__		= None
         lowerCAmelCase__		= None
         lowerCAmelCase__		= None

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


    def    a      (       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Any=None   ,	SCREAMING_SNAKE_CASE__     :     str=None   )      ->       Union[str, Any]:
         from IPython.display import Image, display

         display(Image(self.to_string()   )   )


    def    a      (       self     :     List[Any]   )      ->       int:
         if self._raw is not None:
              return self._raw

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


    def    a      (       self     :     Optional[int]   )      ->       Union[str, Any]:
         if self._path is not None:
              return self._path

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

              return self._path

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

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

              lowerCAmelCase__		= tempfile.mkdtemp()
              lowerCAmelCase__		= os.path.join(SCREAMING_SNAKE_CASE__   ,	str(uuid.uuida()   ) + ".png"   )

              img.save(self._path   )

              return self._path






class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     int=16_000   )      ->       Union[str, Any]:
         super().__init__(SCREAMING_SNAKE_CASE__   )

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

         lowerCAmelCase__		= None
         lowerCAmelCase__		= None

         lowerCAmelCase__		= samplerate

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


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

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


    def    a      (       self     :     Optional[Any]   )      ->       List[Any]:
         if self._tensor is not None:
              return self._tensor

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


    def    a      (       self     :     Dict   )      ->       List[str]:
         if self._path is not None:
              return self._path

         if self._tensor is not None:
              lowerCAmelCase__		= tempfile.mkdtemp()
              lowerCAmelCase__		= os.path.join(SCREAMING_SNAKE_CASE__   ,	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_     :					List[str]   ,				**lowerCAmelCase_     :					Optional[int]			):
     """simple docstring"""

     lowerCAmelCase__		= [(arg.to_raw() if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			) else arg) for arg in args]
     lowerCAmelCase__		= {k: (v.to_raw() if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			) else v) for k, v in kwargs.items()}
     return args, kwargs






def 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					Optional[int]=None			):
     """simple docstring"""

     if isinstance(lowerCAmelCase_   ,				lowerCAmelCase_			):
          lowerCAmelCase__		= {}
          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__		= AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase_			)
                    else:
                         lowerCAmelCase__		= 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__		= _v(lowerCAmelCase_			)
                    if k not in decoded_outputs:
                         lowerCAmelCase__		= AgentType[v]

     elif isinstance(lowerCAmelCase_   ,				(list, tuple)			):
          lowerCAmelCase__		= 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__		= False
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(lowerCAmelCase_   ,				_k			):
                              decoded_outputs.append(_v(lowerCAmelCase_			)			)
                              lowerCAmelCase__		= 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__		= 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
