



"""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


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  A_     :



						"""simple docstring"""






						def __init__(   self					:Union[str, Any]  ,    lowercase_					:Optional[Any]   )							->     Union[str, Any]:
												UpperCAmelCase			 =					value






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






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Tuple:
												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					:List[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  A_     (	_A      ,  _A							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Any   )							->     Dict:
												return self._value






						def       UpperCAmelCase__	(   self					:str   )							->     int:
												return self._value







class  A_     (	_A      ,  _A							):



						"""simple docstring"""






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

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

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None

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






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:Any=None  ,    lowercase_					:Optional[Any]=None   )							->     Union[str, Any]:
												from IPython.display import Image, display

												display(Image(self.to_string()   )   )






						def       UpperCAmelCase__	(   self					:int   )							->     List[str]:





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

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






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





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

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

																		return self._path

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

																		# There is likely simpler than load into image into save
																		UpperCAmelCase			 =					Image.fromarray((array * 2_55).astype(np.uinta   )   )

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

																		img.save(self._path   )

																		return self._path







class  A_     (	_A							):



						"""simple docstring"""






						def __init__(   self					:Dict  ,    lowercase_					:Optional[int]  ,    lowercase_					:Optional[Any]=1_60_00   )							->     Optional[Any]:
												super().__init__(__A   )

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

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None

												UpperCAmelCase			 =					samplerate

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






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:List[Any]=None  ,    lowercase_					:Tuple=None   )							->     Optional[Any]:
												from IPython.display import Audio, display

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






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Tuple:





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

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






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





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

												if self._tensor is not None:
																		UpperCAmelCase			 =					tempfile.mkdtemp()
																		UpperCAmelCase			 =					os.path.join(__A  ,    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 (  *lowercase_						,      **lowercase_		):
						UpperCAmelCase			 =					[(arg.to_raw() if isinstance(UpperCAmelCase_						,      UpperCAmelCase_		) else arg) for arg in args]
						UpperCAmelCase			 =					{k: (v.to_raw() if isinstance(UpperCAmelCase_						,      UpperCAmelCase_		) else v) for k, v in kwargs.items()}
						return args, kwargs






def 					_lowerCAmelCase (  lowercase_						,      lowercase_=None		):
						if isinstance(UpperCAmelCase_						,      UpperCAmelCase_		):
												UpperCAmelCase			 =					{}
												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			 =					AGENT_TYPE_MAPPING[output_types[i]](UpperCAmelCase_		)
																								else:
																														UpperCAmelCase			 =					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			 =					_v(UpperCAmelCase_		)
																								if k not in decoded_outputs:
																														UpperCAmelCase			 =					AgentType[v]

						elif isinstance(UpperCAmelCase_						,      (list, tuple)		):
												UpperCAmelCase			 =					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			 =					False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(UpperCAmelCase_						,      _k		):
																																				decoded_outputs.append(_v(UpperCAmelCase_		)		)
																																				UpperCAmelCase			 =					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			 =					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'''







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 DeiTImageProcessor

class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

	def __init__(  self		:  List[str]   ,		__A		:  Any   ,		__A		:  Tuple=7   ,		__A		:  Dict=3   ,		__A		:  List[str]=18   ,		__A		:  Optional[int]=30   ,		__A		:  int=400   ,		__A		:  Union[str, Any]=True   ,		__A		:  Tuple=None   ,		__A		:  int=True   ,		__A		:  int=None   ,		__A		:  List[str]=True   ,		__A		:  Union[str, Any]=[0.5, 0.5, 0.5]   ,		__A		:  List[Any]=[0.5, 0.5, 0.5]   ,		)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   size if size is not None else {"""height""": 20, """width""": 20}
								lowerCAmelCase__  =   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   batch_size
								lowerCAmelCase__  =   num_channels
								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   min_resolution
								lowerCAmelCase__  =   max_resolution
								lowerCAmelCase__  =   do_resize
								lowerCAmelCase__  =   size
								lowerCAmelCase__  =   do_center_crop
								lowerCAmelCase__  =   crop_size
								lowerCAmelCase__  =   do_normalize
								lowerCAmelCase__  =   image_mean
								lowerCAmelCase__  =   image_std




	def      lowercase__							(  self		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}





@require_torch
@require_vision
class      lowerCamelCase__     (     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	DeiTImageProcessor if is_vision_available() else None
	A__		=	True




	def      lowercase__							(  self		:  List[Any]					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   DeiTImageProcessingTester(self					)




	@property
	def      lowercase__							(  self		:  Any					)     ->			int:




								'''simple docstring'''





								return self.image_processor_tester.prepare_image_processor_dict()




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




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(__A   ,		"""do_resize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""size"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""center_crop"""					)					)
								self.assertTrue(hasattr(__A   ,		"""do_normalize"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_mean"""					)					)
								self.assertTrue(hasattr(__A   ,		"""image_std"""					)					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size   ,		{"""height""": 20, """width""": 20}					)
								self.assertEqual(image_processor.crop_size   ,		{"""height""": 18, """width""": 18}					)

								lowerCAmelCase__  =   self.image_processing_class.from_dict(self.image_processor_dict   ,		size=42   ,		crop_size=84					)
								self.assertEqual(image_processor.size   ,		{"""height""": 42, """width""": 42}					)
								self.assertEqual(image_processor.crop_size   ,		{"""height""": 84, """width""": 84}					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			List[str]:




								'''simple docstring'''





								pass




	def      lowercase__							(  self		:  Dict					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		Image.Image					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)




	def      lowercase__							(  self		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		numpify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		np.ndarray					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)




	def      lowercase__							(  self		:  List[str]					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								lowerCAmelCase__  =   prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__A   ,		torchify=__A					)
								for image in image_inputs:
															self.assertIsInstance(__A   ,		torch.Tensor					)

								# Test not batched input
								lowerCAmelCase__  =   image_processing(image_inputs[0]   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)

								# Test batched
								lowerCAmelCase__  =   image_processing(__A   ,		return_tensors="""pt"""					).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,		(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,		)





