






import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class      UpperCamelCase_							:




						def __init__(    self		,	lowercase		,	lowercase=sys.maxsize       )			->						int:
													_a  :    Dict			=						'''bilinear'''
													_a  :    List[str]			=						max_size
													_a  :    Union[str, Any]			=						short_edge_length







						def __call__(    self		,	lowercase       )			->						Union[str, Any]:
													_a  :    Union[str, Any]			=						[]
													for img in imgs:
																				_a		,					_a  :    Any			=						img.shape[:2]
																				# later: provide list and randomly choose index for resize
																				_a  :    int			=						np.random.randint(self.short_edge_length[0]		,	self.short_edge_length[1] + 1       )
																				if size == 0:
																											return img
																				_a  :    Tuple			=						size * 1.0 / min(lowercase		,	lowercase       )
																				if h < w:
																											_a		,					_a  :    Optional[Any]			=						size, scale * w
																				else:
																											_a		,					_a  :    Union[str, Any]			=						scale * h, size
																				if max(lowercase		,	lowercase       ) > self.max_size:
																											_a  :    Tuple			=						self.max_size * 1.0 / max(lowercase		,	lowercase       )
																											_a  :    Optional[Any]			=						newh * scale
																											_a  :    Union[str, Any]			=						neww * scale
																				_a  :    int			=						int(neww + 0.5       )
																				_a  :    List[str]			=						int(newh + 0.5       )

																				if img.dtype == np.uinta:
																											_a  :    Optional[int]			=						Image.fromarray(lowercase       )
																											_a  :    Union[str, Any]			=						pil_image.resize((neww, newh)		,	PILImageResampling.BILINEAR       )
																											_a  :    Dict			=						np.asarray(lowercase       )
																				else:
																											_a  :    Optional[int]			=						img.permute(2		,	0		,	1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
																											_a  :    Tuple			=						nn.functional.interpolate(
																											    lowercase		,	(newh, neww)		,	mode=self.interp_method		,	align_corners=lowercase       ).squeeze(0       )
																				img_augs.append(lowercase       )

													return img_augs

class      UpperCamelCase_							:




						def __init__(    self		,	lowercase       )			->						Union[str, Any]:
													_a  :    List[str]			=						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,	cfg.INPUT.MAX_SIZE_TEST       )
													_a  :    Dict			=						cfg.INPUT.FORMAT
													_a  :    Optional[int]			=						cfg.SIZE_DIVISIBILITY
													_a  :    int			=						cfg.PAD_VALUE
													_a  :    int			=						cfg.INPUT.MAX_SIZE_TEST
													_a  :    Optional[Any]			=						cfg.MODEL.DEVICE
													_a  :    Tuple			=						torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )		,	1		,	1       )
													_a  :    Any			=						torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )		,	1		,	1       )
													_a  :    Optional[Any]			=						lambda lowercase       : (x - self.pixel_mean) / self.pixel_std




						def      snake_case__(    self		,	lowercase       )			->						int:
													_a  :    Optional[int]			=						tuple(max(lowercase       ) for s in zip(*[img.shape for img in images]       )       )
													_a  :    str			=						[im.shape[-2:] for im in images]
													_a  :    Dict			=						[
													    nn.functional.pad(
													        lowercase		,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,	value=self.pad_value		,	)
													    for size, im in zip(lowercase		,	lowercase       )
													]

													return torch.stack(lowercase       ), torch.tensor(lowercase       )







						def __call__(    self		,	lowercase		,	lowercase=False       )			->						str:
													with torch.no_grad():
																				if not isinstance(lowercase		,	lowercase       ):
																											_a  :    Optional[Any]			=						[images]
																				if single_image:
																											assert len(lowercase       ) == 1
																				for i in range(len(lowercase       )       ):
																											if isinstance(images[i]		,	torch.Tensor       ):
																																		images.insert(lowercase		,	images.pop(lowercase       ).to(self.device       ).float()       )
																											elif not isinstance(images[i]		,	torch.Tensor       ):
																																		images.insert(
																																		    lowercase		,	torch.as_tensor(img_tensorize(images.pop(lowercase       )		,	input_format=self.input_format       )       )
																																		    .to(self.device       )
																																		    .float()		,	)
            # resize smallest edge
																				_a  :    Dict			=						torch.tensor([im.shape[:2] for im in images]       )
																				_a  :    int			=						self.aug(lowercase       )
																				# transpose images and convert to torch tensors
																				# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																				# now normalize before pad to avoid useless arithmetic
																				_a  :    Any			=						[self.normalizer(lowercase       ) for x in images]
																				# now pad them to do the following operations
																				_a		,					_a  :    str			=						self.pad(lowercase       )
																				# Normalize

																				if self.size_divisibility > 0:
																											raise NotImplementedError()
																				# pad
																				_a  :    str			=						torch.true_divide(lowercase		,	lowercase       )
																				if single_image:
																											return images[0], sizes[0], scales_yx[0]
																				else:
																											return images, sizes, scales_yx






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		int:







							"""simple docstring"""





							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Tuple:







							"""simple docstring"""





							assert torch.isfinite(UpperCAmelCase     ).all(), "Box tensor contains infinite or NaN!"
							_a		,					_a  :    List[Any]			=						box_size
							tensor[:, 0].clamp_(min=0    ,	max=UpperCAmelCase     )
							tensor[:, 1].clamp_(min=0    ,	max=UpperCAmelCase     )
							tensor[:, 2].clamp_(min=0    ,	max=UpperCAmelCase     )
							tensor[:, 3].clamp_(min=0    ,	max=UpperCAmelCase     )







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

						__lowerCamelCase						= True
						__lowerCamelCase						= ''
except Exception as e:
						__lowerCamelCase						= False
						__lowerCamelCase						= (
						    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      UpperCamelCase_							(				UpperCamelCase ,       UpperCamelCase       ):
						lowercase									=  '''mel_config.json'''




						@register_to_config
						def __init__(    self		,	lowercase = 256		,	lowercase = 256		,	lowercase = 22_050		,	lowercase = 2_048		,	lowercase = 512		,	lowercase = 80		,	lowercase = 32		,	)			->						Optional[Any]:
													_a  :    Tuple			=						hop_length
													_a  :    Dict			=						sample_rate
													_a  :    Dict			=						n_fft
													_a  :    List[str]			=						top_db
													_a  :    Optional[int]			=						n_iter
													self.set_resolution(lowercase		,	lowercase       )
													_a  :    Optional[int]			=						None

													if not _librosa_can_be_imported:
																				raise ValueError(_import_error       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Any:
													_a  :    Any			=						x_res
													_a  :    Optional[Any]			=						y_res
													_a  :    List[Any]			=						self.y_res
													_a  :    Tuple			=						self.x_res * self.hop_length - 1




						def      snake_case__(    self		,	lowercase = None		,	lowercase = None       )			->						Union[str, Any]:
													if audio_file is not None:
																				_a		,					_a  :    str			=						librosa.load(lowercase		,	mono=lowercase		,	sr=self.sr       )
													else:
																				_a  :    Any			=						raw_audio

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




						def      snake_case__(    self       )			->						int:
													return len(self.audio       ) // self.slice_size




						def      snake_case__(    self		,	lowercase = 0       )			->						np.ndarray:
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




						def      snake_case__(    self       )			->						int:
													return self.sr




						def      snake_case__(    self		,	lowercase       )			->						Image.Image:
													_a  :    int			=						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       )
													_a  :    List[str]			=						librosa.power_to_db(lowercase		,	ref=np.max		,	top_db=self.top_db       )
													_a  :    List[str]			=						(((log_S + self.top_db) * 255 / self.top_db).clip(0		,	255       ) + 0.5).astype(np.uinta       )
													_a  :    Any			=						Image.fromarray(lowercase       )
													return image







						def      snake_case__(    self		,	lowercase       )			->						np.ndarray:
													_a  :    Any			=						np.frombuffer(image.tobytes()		,	dtype='''uint8'''       ).reshape((image.height, image.width)       )
													_a  :    Any			=						bytedata.astype('''float'''       ) * self.top_db / 255 - self.top_db
													_a  :    Dict			=						librosa.db_to_power(lowercase       )
													_a  :    List[str]			=						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