






import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViltImageProcessor
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Tuple=7  ,  lowerCAmelCase__					:	Union[str, Any]=3  ,  lowerCAmelCase__					:	List[Any]=1_8  ,  lowerCAmelCase__					:	str=3_0  ,  lowerCAmelCase__					:	str=4_0_0  ,  lowerCAmelCase__					:	Optional[int]=True  ,  lowerCAmelCase__					:	Tuple=None  ,  lowerCAmelCase__					:	List[Any]=2  ,  lowerCAmelCase__					:	Tuple=True  ,  lowerCAmelCase__					:	str=[0.5, 0.5, 0.5]  ,  lowerCAmelCase__					:	Optional[Any]=[0.5, 0.5, 0.5]  ,  )							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		size if size is not None else {'shortest_edge': 3_0}
      SCREAMING_SNAKE_CASE				    =		parent
      SCREAMING_SNAKE_CASE				    =		batch_size
      SCREAMING_SNAKE_CASE				    =		num_channels
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		min_resolution
      SCREAMING_SNAKE_CASE				    =		max_resolution
      SCREAMING_SNAKE_CASE				    =		do_resize
      SCREAMING_SNAKE_CASE				    =		size
      SCREAMING_SNAKE_CASE				    =		size_divisor
      SCREAMING_SNAKE_CASE				    =		do_normalize
      SCREAMING_SNAKE_CASE				    =		image_mean
      SCREAMING_SNAKE_CASE				    =		image_std





     def     __UpperCamelCase						(		self					:	Any)							->	str:

      """simple docstring"""
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }





     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	List[Any]=False)							->	List[str]:

      """simple docstring"""
      if not batched:
       SCREAMING_SNAKE_CASE				    =		self.size['shortest_edge']
       SCREAMING_SNAKE_CASE				    =		image_inputs[0]
       if isinstance(lowerCAmelCase__  ,  Image.Image):
        SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		image.size
       else:
        SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		image.shape[1], image.shape[2]
       SCREAMING_SNAKE_CASE				    =		size / min(lowerCAmelCase__  ,  lowerCAmelCase__)
       if h < w:
        SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		size, scale * w
       else:
        SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		scale * h, size

       SCREAMING_SNAKE_CASE				    =		int((1_3_3_3 / 8_0_0) * size)
       if max(lowerCAmelCase__  ,  lowerCAmelCase__) > max_size:
        SCREAMING_SNAKE_CASE				    =		max_size / max(lowerCAmelCase__  ,  lowerCAmelCase__)
        SCREAMING_SNAKE_CASE				    =		newh * scale
        SCREAMING_SNAKE_CASE				    =		neww * scale

       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		int(newh + 0.5), int(neww + 0.5)
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       SCREAMING_SNAKE_CASE				    =		[]
       for image in image_inputs:
        SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_expected_values([image])
        expected_values.append((expected_height, expected_width))
       SCREAMING_SNAKE_CASE				    =		max(lowerCAmelCase__  ,  key=lambda lowerCAmelCase__: item[0])[0]
       SCREAMING_SNAKE_CASE				    =		max(lowerCAmelCase__  ,  key=lambda lowerCAmelCase__: item[1])[1]

      return expected_height, expected_width

@require_torch
@require_vision
class 	a_(		lowercase__			,							unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				List[str]  					=ViltImageProcessor if is_vision_available() else None





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ViltImageProcessingTester(self)





     @property
     def     __UpperCamelCase						(		self					:	Dict)							->	Any:

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'image_mean'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'image_std'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_normalize'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_resize'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'size'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'size_divisor'))





     def     __UpperCamelCase						(		self					:	int)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size  ,  {'shortest_edge': 3_0})

      SCREAMING_SNAKE_CASE				    =		self.image_processing_class.from_dict(self.image_processor_dict  ,  size=4_2)
      self.assertEqual(image_processor.size  ,  {'shortest_edge': 4_2})





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Dict:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__)
      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processing(image_inputs[0]  ,  return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.image_processor_tester.get_expected_values(lowerCAmelCase__)
      self.assertEqual(
          encoded_images.shape  ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processing(lowerCAmelCase__  ,  return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.image_processor_tester.get_expected_values(lowerCAmelCase__  ,  batched=lowerCAmelCase__)
      self.assertEqual(
          encoded_images.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,  )





     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Optional[int]:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__  ,  numpify=lowerCAmelCase__)
      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processing(image_inputs[0]  ,  return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.image_processor_tester.get_expected_values(lowerCAmelCase__)
      self.assertEqual(
          encoded_images.shape  ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processing(lowerCAmelCase__  ,  return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.image_processor_tester.get_expected_values(lowerCAmelCase__  ,  batched=lowerCAmelCase__)
      self.assertEqual(
          encoded_images.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,  )





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Dict:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__  ,  torchify=lowerCAmelCase__)
      for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processing(image_inputs[0]  ,  return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.image_processor_tester.get_expected_values(lowerCAmelCase__)
      self.assertEqual(
          encoded_images.shape  ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processing(lowerCAmelCase__  ,  return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.image_processor_tester.get_expected_values(lowerCAmelCase__  ,  batched=lowerCAmelCase__)
      self.assertEqual(
          encoded_images.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )  ,  )









'''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


_lowercase       :						Dict						=logging.get_logger(__name__)

if is_vision_available():
				import PIL.Image
				from PIL import Image
				from PIL.Image import Image as ImageType
else:
				_lowercase       :						List[Any]						=object

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   int      =    value



			def __str__(     self		:						int			)      ->      str:
							"""simple docstring"""





							return self.to_string()



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      List[str]:
							"""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 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      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 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def 				lowerCAmelCase (     self		:						str			)      ->      Tuple:
							"""simple docstring"""





							return self._value







			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Union[str, Any]:
							"""simple docstring"""





							return self._value






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Dict:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

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

							snake_case__      :   str      =    None
							snake_case__      :   List[Any]      =    None
							snake_case__      :   Tuple      =    None

							if isinstance(__UpperCamelCase    ,			__UpperCamelCase			):
											snake_case__      :   List[Any]      =    value
							elif isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   Union[str, Any]      =    value
							else:
											raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						Any=None			)      ->      List[Any]:
							"""simple docstring"""





							from IPython.display import Image, display

							display(Image(self.to_string()			)			)



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""simple docstring"""





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

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







			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





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

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

											return self._path

							if self._tensor is not None:
											snake_case__      :   List[str]      =    self._tensor.cpu().detach().numpy()

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

											snake_case__      :   str      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)

											img.save(self._path			)

											return self._path






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						str=16000			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

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

							snake_case__      :   List[str]      =    None
							snake_case__      :   Optional[int]      =    None

							snake_case__      :   Any      =    samplerate

							if isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   int      =    value
							else:
											raise ValueError(F'''Unsupported audio type: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      Any:
							"""simple docstring"""





							from IPython.display import Audio, display

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



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





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

							if self._path is not None:
											snake_case__  ,      snake_case__      :   Tuple      =    sf.read(self._path			)
											snake_case__      :   Tuple      =    torch.tensor(__UpperCamelCase			)
											return self._tensor







			def 				lowerCAmelCase (     self		:						Any			)      ->      Dict:
							"""simple docstring"""





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

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



_lowercase       :						Union[str, Any]						={"text": AgentText, "image": AgentImage, "audio": AgentAudio}
_lowercase       :						List[Any]						={str: AgentText}

if is_vision_available():
				_lowercase       :						Optional[Any]						=AgentImage


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Optional[int]   ,				**UpperCamelCase__				:List[Any]			)		->						Optional[Any]:
				snake_case__      :   List[str]      =    [(arg.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else arg) for arg in args]
				snake_case__      :   Any      =    {k: (v.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else v) for k, v in kwargs.items()}
				return args, kwargs



def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Tuple=None			)		->						Dict:
				if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								snake_case__      :   Any      =    {}
								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:
																				snake_case__      :   List[Any]      =    AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)
																else:
																				snake_case__      :   Optional[int]      =    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			):
																								snake_case__      :   str      =    _v(UpperCamelCase__			)
																if k not in decoded_outputs:
																				snake_case__      :   Any      =    AgentType[v]

				elif isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    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
																snake_case__      :   Any      =    False
																for _k, _v in INSTANCE_TYPE_MAPPING.items():
																				if isinstance(UpperCamelCase__   ,				_k			):
																								decoded_outputs.append(_v(UpperCamelCase__			)			)
																								snake_case__      :   str      =    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
												snake_case__      :   Tuple      =    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




