




import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
	import librosa  # noqa: E402

	__A						:		Optional[int]          =						True
	__A						:		Optional[int]          =						""
except Exception as e:
	__A						:		Dict          =						False
	__A						:		Any          =						(
	    f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
	)


from PIL import Image  # noqa: E402

class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= "mel_config.json"




					@register_to_config
					def __init__(   self:		List[str],     _lowercase:		int = 256,     _lowercase:		int = 256,     _lowercase:		int = 22050,     _lowercase:		int = 2048,     _lowercase:		int = 512,     _lowercase:		int = 80,     _lowercase:		int = 32,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  hop_length
									__lowerCAmelCase					=  sample_rate
									__lowerCAmelCase					=  n_fft
									__lowerCAmelCase					=  top_db
									__lowerCAmelCase					=  n_iter
									self.set_resolution(_lowercase,     _lowercase)
									__lowerCAmelCase					=  None

									if not _librosa_can_be_imported:
													raise ValueError(_import_error)




					def     _lowercase						(   self:		Optional[int],     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  x_res
									__lowerCAmelCase					=  y_res
									__lowerCAmelCase					=  self.y_res
									__lowerCAmelCase					=  self.x_res * self.hop_length - 1




					def     _lowercase						(   self:		Optional[int],     _lowercase:		str = None,     _lowercase:		np.ndarray = None):


									'''simple docstring'''


									if audio_file is not None:
													__lowerCAmelCase	,				__lowerCAmelCase					=  librosa.load(_lowercase,     mono=_lowercase,     sr=self.sr)
									else:
													__lowerCAmelCase					=  raw_audio

									# Pad with silence if necessary.
									if len(self.audio) < self.x_res * self.hop_length:
													__lowerCAmelCase					=  np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio),))])




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return len(self.audio) // self.slice_size




					def     _lowercase						(   self:		Optional[int],     _lowercase:		int = 0):


									'''simple docstring'''


									return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									return self.sr




					def     _lowercase						(   self:		Dict,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  librosa.feature.melspectrogram(
									    y=self.get_audio_slice(_lowercase),     sr=self.sr,     n_fft=self.n_fft,     hop_length=self.hop_length,     n_mels=self.n_mels)
									__lowerCAmelCase					=  librosa.power_to_db(_lowercase,     ref=np.max,     top_db=self.top_db)
									__lowerCAmelCase					=  (((log_S + self.top_db) * 255 / self.top_db).clip(0,     255) + 0.5).astype(np.uinta)
									__lowerCAmelCase					=  Image.fromarray(_lowercase)
									return image







					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		Image.Image):


									'''simple docstring'''


									__lowerCAmelCase					=  np.frombuffer(image.tobytes(),     dtype="""uint8""").reshape((image.height, image.width))
									__lowerCAmelCase					=  bytedata.astype("""float""") * self.top_db / 255 - self.top_db
									__lowerCAmelCase					=  librosa.db_to_power(_lowercase)
									__lowerCAmelCase					=  librosa.feature.inverse.mel_to_audio(
									    _lowercase,     sr=self.sr,     n_fft=self.n_fft,     hop_length=self.hop_length,     n_iter=self.n_iter)
									return audio







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 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						def __init__(  self  , A  , A=7  , A=3  , A=18  , A=30  , A=4_00  , A=True  , A=None  , A=True  , A=None  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , )  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__		:str 		=    crop_size if crop_size is not None else {'height': 18, 'width': 18}

									UpperCAmelCase__		:Dict 		=    parent
									UpperCAmelCase__		:Optional[Any] 		=    batch_size
									UpperCAmelCase__		:Optional[Any] 		=    num_channels
									UpperCAmelCase__		:str 		=    image_size
									UpperCAmelCase__		:Optional[Any] 		=    min_resolution
									UpperCAmelCase__		:Optional[int] 		=    max_resolution
									UpperCAmelCase__		:Dict 		=    do_resize
									UpperCAmelCase__		:Any 		=    size
									UpperCAmelCase__		:Any 		=    do_center_crop
									UpperCAmelCase__		:Optional[int] 		=    crop_size
									UpperCAmelCase__		:Dict 		=    do_normalize
									UpperCAmelCase__		:List[Any] 		=    image_mean
									UpperCAmelCase__		:Tuple 		=    image_std


						def    A__       (  self	)  ->str:
									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 	UpperCamelCase__       (  UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Tuple												=			DeiTImageProcessor if is_vision_available() else None
						__a     :    Tuple												=			True


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:int 		=    DeiTImageProcessingTester(self	)


						@property
						def    A__       (  self	)  ->List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:Tuple 		=    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    A__       (  self	)  ->Tuple:
									UpperCAmelCase__		:Union[str, Any] 		=    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}	)

									UpperCAmelCase__		:List[Any] 		=    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    A__       (  self	)  ->List[str]:
									pass


						def    A__       (  self	)  ->List[str]:
									# Initialize image_processing
									UpperCAmelCase__		:int 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									UpperCAmelCase__		:Tuple 		=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=A	)
									for image in image_inputs:
												self.assertIsInstance(A  , Image.Image	)

									# Test not batched input
									UpperCAmelCase__		:int 		=    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
									UpperCAmelCase__		:List[str] 		=    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    A__       (  self	)  ->str:
									# Initialize image_processing
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		:Dict 		=    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
									UpperCAmelCase__		:List[str] 		=    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
									UpperCAmelCase__		:Optional[Any] 		=    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    A__       (  self	)  ->Tuple:
									# Initialize image_processing
									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		:str 		=    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
									UpperCAmelCase__		:List[str] 		=    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
									UpperCAmelCase__		:Optional[int] 		=    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'],
									    )  , )





