




'''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


_SCREAMING_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:
							_SCREAMING_SNAKE_CASE =	object

if is_torch_available():
							import torch

if is_soundfile_availble():
							import soundfile as sf
class      __UpperCAmelCase							:



					'''simple docstring'''





					def __init__(  self    :	str			,					_lowercase    :	Union[str, Any])						->       str:
								A_       		=   value



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



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



					def 						__snake_case	(  self    :	List[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      __UpperCAmelCase							(    lowerCAmelCase ,lowerCAmelCase							):



					'''simple docstring'''





					def 						__snake_case	(  self    :	int)						->       Tuple:
								return self._value



					def 						__snake_case	(  self    :	List[Any])						->       Any:
								return self._value





class      __UpperCAmelCase							(    lowerCAmelCase ,lowerCAmelCase							):



					'''simple docstring'''





					def __init__(  self    :	Any			,					_lowercase    :	List[str])						->       List[str]:
								super().__init__(_lowercase)

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

								A_       		=   None
								A_       		=   None
								A_       		=   None

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



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

								display(Image(self.to_string()))



					def 						__snake_case	(  self    :	str)						->       Dict:
								if self._raw is not None:
											return self._raw

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



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

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

											return self._path

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

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

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

											img.save(self._path)

											return self._path





class      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





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

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

								A_       		=   None
								A_       		=   None

								A_       		=   samplerate

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



					def 						__snake_case	(  self    :	Optional[int]			,					_lowercase    :	Dict=None			,					_lowercase    :	Dict=None)						->       Any:
								from IPython.display import Audio, display

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



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

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



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

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





_SCREAMING_SNAKE_CASE =	{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
_SCREAMING_SNAKE_CASE =	{str: AgentText}

if is_vision_available():
							_SCREAMING_SNAKE_CASE =	AgentImage





def  lowerCamelCase(       *SCREAMING_SNAKE_CASE_					,**SCREAMING_SNAKE_CASE_    )		->		Union[str, Any]:
			A_       		=   [(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    ) else arg) for arg in args]
			A_       		=   {k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    ) else v) for k, v in kwargs.items()}
			return args, kwargs






def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_=None    )		->		List[str]:
			if isinstance(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    ):
						A_       		=   {}
						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:
															A_       		=   AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE_    )
												else:
															A_       		=   AgentType(SCREAMING_SNAKE_CASE_    )

									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(SCREAMING_SNAKE_CASE_					,_k    ):
																		A_       		=   _v(SCREAMING_SNAKE_CASE_    )
												if k not in decoded_outputs:
															A_       		=   AgentType[v]

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

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

			else:
						if output_types[0] in AGENT_TYPE_MAPPING:
									# If the class has defined outputs, we can map directly according to the class definition
									A_       		=   AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_SNAKE_CASE_    )

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

			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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import CLIPImageProcessor
class      __UpperCAmelCase							(    unittest.TestCase							):



					'''simple docstring'''





					def __init__(  self    :	Dict			,					_lowercase    :	List[Any]			,					_lowercase    :	List[Any]=7			,					_lowercase    :	Dict=3			,					_lowercase    :	List[Any]=18			,					_lowercase    :	List[str]=30			,					_lowercase    :	List[str]=400			,					_lowercase    :	Any=True			,					_lowercase    :	Any=None			,					_lowercase    :	Any=True			,					_lowercase    :	Dict=None			,					_lowercase    :	Optional[int]=True			,					_lowercase    :	int=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,					_lowercase    :	Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,					_lowercase    :	Optional[Any]=True			,					)						->       List[str]:
								A_       		=   size if size is not None else {'shortest_edge': 20}
								A_       		=   crop_size if crop_size is not None else {'height': 18, 'width': 18}
								A_       		=   parent
								A_       		=   batch_size
								A_       		=   num_channels
								A_       		=   image_size
								A_       		=   min_resolution
								A_       		=   max_resolution
								A_       		=   do_resize
								A_       		=   size
								A_       		=   do_center_crop
								A_       		=   crop_size
								A_       		=   do_normalize
								A_       		=   image_mean
								A_       		=   image_std
								A_       		=   do_convert_rgb



					def 						__snake_case	(  self    :	Optional[Any])						->       List[Any]:
								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,
								    "do_convert_rgb": self.do_convert_rgb,
								}



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

								if equal_resolution:
											A_       		=   []
											for i in range(self.batch_size):
														image_inputs.append(
														    np.random.randint(
														        255			,					size=(self.num_channels, self.max_resolution, self.max_resolution)			,					dtype=np.uinta))
								else:
											A_       		=   []
											for i in range(self.batch_size):
														A_		,   A_       		=   np.random.choice(np.arange(self.min_resolution			,					self.max_resolution)			,					2)
														image_inputs.append(np.random.randint(255			,					size=(self.num_channels, width, height)			,					dtype=np.uinta))

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

								if torchify:
											A_       		=   [torch.from_numpy(_lowercase) for x in image_inputs]

								return image_inputs





@require_torch
@require_vision
class      __UpperCAmelCase							(    lowerCAmelCase ,unittest.TestCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		CLIPImageProcessor if is_vision_available() else None



					def 						__snake_case	(  self    :	Optional[Any])						->       List[str]:
								A_       		=   CLIPImageProcessingTester(self)



					@property
					def 						__snake_case	(  self    :	Dict)						->       Optional[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						__snake_case	(  self    :	Optional[Any])						->       str:
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase			,					'do_resize'))
								self.assertTrue(hasattr(_lowercase			,					'size'))
								self.assertTrue(hasattr(_lowercase			,					'do_center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'do_normalize'))
								self.assertTrue(hasattr(_lowercase			,					'image_mean'))
								self.assertTrue(hasattr(_lowercase			,					'image_std'))
								self.assertTrue(hasattr(_lowercase			,					'do_convert_rgb'))



					def 						__snake_case	(  self    :	Union[str, Any])						->       Optional[int]:
								A_       		=   self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size			,					{'shortest_edge': 20})
								self.assertEqual(image_processor.crop_size			,					{'height': 18, 'width': 18})

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



					def 						__snake_case	(  self    :	str)						->       List[str]:
								pass



					def 						__snake_case	(  self    :	List[str])						->       Dict:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					Image.Image)

								# Test not batched input
								A_       		=   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
								A_       		=   image_processing(_lowercase			,					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 						__snake_case	(  self    :	Union[str, Any])						->       Dict:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase			,					numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					np.ndarray)

								# Test not batched input
								A_       		=   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
								A_       		=   image_processing(_lowercase			,					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 						__snake_case	(  self    :	Optional[Any])						->       Tuple:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase			,					torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					torch.Tensor)

								# Test not batched input
								A_       		=   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
								A_       		=   image_processing(_lowercase			,					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'],
								    )			,					)





@require_torch
@require_vision
class      __UpperCAmelCase							(    lowerCAmelCase ,unittest.TestCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		CLIPImageProcessor if is_vision_available() else None



					def 						__snake_case	(  self    :	int)						->       List[Any]:
								A_       		=   CLIPImageProcessingTester(self			,					num_channels=4)
								A_       		=   3



					@property
					def 						__snake_case	(  self    :	str)						->       Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						__snake_case	(  self    :	Any)						->       Dict:
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase			,					'do_resize'))
								self.assertTrue(hasattr(_lowercase			,					'size'))
								self.assertTrue(hasattr(_lowercase			,					'do_center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'do_normalize'))
								self.assertTrue(hasattr(_lowercase			,					'image_mean'))
								self.assertTrue(hasattr(_lowercase			,					'image_std'))
								self.assertTrue(hasattr(_lowercase			,					'do_convert_rgb'))



					def 						__snake_case	(  self    :	Any)						->       Optional[int]:
								pass



					def 						__snake_case	(  self    :	str)						->       List[Any]:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					Image.Image)

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

								# Test batched
								A_       		=   image_processing(_lowercase			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)




