




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_						:					int			   =       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_						:					Dict			   =       object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(      self :       Union[str, Any]						,				A :       Optional[int]    )			->			Tuple:






         """simple docstring"""
         a__      =       value

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






         """simple docstring"""
         return self.to_string()

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			List[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 				__SCREAMING_SNAKE_CASE		(      self :       List[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 		__UpperCAmelCase(		lowercase__       ,	lowercase__ ):





     """simple docstring"""



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






         """simple docstring"""
         return self._value






     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Optional[Any]:






         """simple docstring"""
         return self._value







class 		__UpperCAmelCase(		lowercase__       ,	lowercase__ ):





     """simple docstring"""



     def __init__(      self :       Optional[Any]						,				A :       List[Any]    )			->			Any:






         """simple docstring"""
         super().__init__(A    )

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

         a__      =       None
         a__      =       None
         a__      =       None

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       List[str]=None						,				A :       Tuple=None    )			->			Optional[Any]:






         """simple docstring"""
         from IPython.display import Image, display

         display(Image(self.to_string()    )    )

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






         """simple docstring"""
         if self._raw is not None:
             return self._raw

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






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






         """simple docstring"""
         if self._path is not None:
             return self._path

         if self._raw is not None:
             a__      =       tempfile.mkdtemp()
             a__      =       os.path.join(A						,				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 * 2_55).astype(np.uinta    )    )

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

             img.save(self._path    )

             return self._path







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       Optional[Any]						,				A :       Union[str, Any]						,				A :       List[Any]=1_60_00    )			->			List[Any]:






         """simple docstring"""
         super().__init__(A    )

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

         a__      =       None
         a__      =       None

         a__      =       samplerate

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       List[str]=None						,				A :       List[str]=None    )			->			str:






         """simple docstring"""
         from IPython.display import Audio, display

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple:






         """simple docstring"""
         if self._tensor is not None:
             return self._tensor

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






     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			List[str]:






         """simple docstring"""
         if self._path is not None:
             return self._path

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






SCREAMING_SNAKE_CASE_						:					Dict			   =       {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
SCREAMING_SNAKE_CASE_						:					int			   =       {str: AgentText}

if is_vision_available():
 SCREAMING_SNAKE_CASE_						:					Tuple			   =       AgentImage
def        __lowerCAmelCase (*UpperCAmelCase_   ,    **UpperCAmelCase_		):
    a__      =       [(arg.to_raw() if isinstance(UpperCAmelCase_   ,    UpperCAmelCase_		) else arg) for arg in args]
    a__      =       {k: (v.to_raw() if isinstance(UpperCAmelCase_   ,    UpperCAmelCase_		) else v) for k, v in kwargs.items()}
    return args, kwargs







def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_=None		):
    if isinstance(UpperCAmelCase_   ,    UpperCAmelCase_		):
        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]](UpperCAmelCase_		)
                else:
                    a__      =       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		):
                        a__      =       _v(UpperCAmelCase_		)
                if k not in decoded_outputs:
                    a__      =       AgentType[v]

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












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 SwinaSRImageProcessor
 from transformers.image_transforms import get_image_size







class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     def __init__(      self :       Optional[int]						,				A :       Dict						,				A :       Union[str, Any]=7						,				A :       List[str]=3						,				A :       Optional[int]=18						,				A :       Optional[int]=30						,				A :       Optional[Any]=4_00						,				A :       Optional[Any]=True						,				A :       Tuple=1 / 2_55						,				A :       Tuple=True						,				A :       Optional[int]=8						,				)			->			Any:






         """simple docstring"""
         a__      =       parent
         a__      =       batch_size
         a__      =       num_channels
         a__      =       image_size
         a__      =       min_resolution
         a__      =       max_resolution
         a__      =       do_rescale
         a__      =       rescale_factor
         a__      =       do_pad
         a__      =       pad_size

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






         """simple docstring"""
         return {
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
             "pad_size": self.pad_size,
         }






     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       int=False						,				A :       Any=False						,				A :       List[Any]=False    )			->			Optional[Any]:






         """simple docstring"""
         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(
                         2_55						,				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(2_55						,				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(A						,				0						,				-1    )    ) for x in image_inputs]

         if torchify:
             a__      =       [torch.from_numpy(A    ) for x in image_inputs]

         return image_inputs







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





     """simple docstring"""



     __UpperCAmelCase	: Dict						=    SwinaSRImageProcessor if is_vision_available() else None

     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Dict:






         """simple docstring"""
         a__      =       SwinaSRImageProcessingTester(self    )

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			List[Any]:






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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			int:






         """simple docstring"""
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(A						,				"""do_rescale"""    )    )
         self.assertTrue(hasattr(A						,				"""rescale_factor"""    )    )
         self.assertTrue(hasattr(A						,				"""do_pad"""    )    )
         self.assertTrue(hasattr(A						,				"""pad_size"""    )    )

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






         """simple docstring"""
         pass

     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       Optional[Any]    )			->			List[str]:






         """simple docstring"""
         a__ ,		a__      =       get_image_size(A    )
         a__      =       self.image_processor_tester.pad_size

         a__      =       (old_height // size + 1) * size - old_height
         a__      =       (old_width // size + 1) * size - old_width
         return old_height + pad_height, old_width + pad_width

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






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         a__      =       self.image_processor_tester.prepare_inputs(equal_resolution=A    )
         for image in image_inputs:
             self.assertIsInstance(A						,				Image.Image    )

         # Test not batched input
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(np.array(image_inputs[0]    )    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)

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






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         a__      =       self.image_processor_tester.prepare_inputs(equal_resolution=A						,				numpify=A    )
         for image in image_inputs:
             self.assertIsInstance(A						,				np.ndarray    )

         # Test not batched input
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(image_inputs[0]    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)






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






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         a__      =       self.image_processor_tester.prepare_inputs(equal_resolution=A						,				torchify=A    )
         for image in image_inputs:
             self.assertIsInstance(A						,				torch.Tensor    )

         # Test not batched input
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(image_inputs[0]    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)






