

import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowercase_									=    1000


class 					A		:







    """simple docstring"""





    def __init__( self					:			Any,lowercase_					:			str,lowercase_					:			Union[str, Any]=1.2,lowercase_					:			Any="g",lowercase_					:			Optional[int]=0.5,lowercase_					:			List[str]="-",lowercase_					:			List[str]="test_out.jpg",lowercase_					:			int=True,lowercase_					:			List[str]=False,lowercase_					:			Dict=None,lowercase_					:			str=None,lowercase_					:			Any=0.7,)->       List[Any]:





        '''simple docstring'''







        if isinstance(lowercase_,torch.Tensor						):
            A__        =  img.numpy().astype('np.uint8'						)
        if isinstance(lowercase_,lowercase_						):
            A__        =  img_tensorize(lowercase_						)
        assert isinstance(lowercase_,np.ndarray						)

        A__ ,		A__        =  img.shape[1], img.shape[0]
        A__        =  mplfigure.Figure(frameon=lowercase_						)
        A__        =  fig.get_dpi()
        A__        =  (width * scale + 1E-2) / dpi
        A__        =  (height * scale + 1E-2) / dpi
        fig.set_size_inches(lowercase_,lowercase_						)
        A__        =  fig.add_axes([0.0, 0.0, 1.0, 1.0]						)
        ax.axis('off'						)
        ax.set_xlim(0.0,lowercase_						)
        ax.set_ylim(lowercase_						)

        A__        =  saveas
        A__        =  rgb
        A__        =  pynb
        A__        =  img
        A__        =  edgecolor
        A__        =  0.5
        A__        =  linestyle
        A__        =  int(np.sqrt(min(lowercase_,lowercase_						)						) * scale // 3						)
        A__        =  width
        A__        =  height
        A__        =  scale
        A__        =  fig
        A__        =  ax
        A__        =  pad
        A__        =  idaobj
        A__        =  idaattr
        A__        =  FigureCanvasAgg(lowercase_						)


    def 		snake_case__	( self					:			Tuple,lowercase_					:			str,lowercase_					:			int=None						)->       Dict:





        '''simple docstring'''







        if color is None:
            A__        =  self.edgecolor
        ((A__) ,		(A__) ,		(A__) ,		(A__))        =  box
        A__        =  xa - xa
        A__        =  ya - ya
        self.ax.add_patch(
            mpl.patches.Rectangle(
                (xa, ya),lowercase_,lowercase_,fill=lowercase_,edgecolor=lowercase_,linewidth=self.font_size // 3,alpha=self.alpha,linestyle=self.linestyle,)						)


    def 		snake_case__	( self					:			Any,lowercase_					:			str,lowercase_					:			Tuple=None,lowercase_					:			int=None,lowercase_					:			Union[str, Any]=None,lowercase_					:			List[str]=None						)->       Optional[int]:





        '''simple docstring'''







        if len(boxes.shape						) > 2:
            A__        =  boxes[0]
        if len(obj_ids.shape						) > 1:
            A__        =  obj_ids[0]
        if len(obj_scores.shape						) > 1:
            A__        =  obj_scores[0]
        if len(attr_ids.shape						) > 1:
            A__        =  attr_ids[0]
        if len(attr_scores.shape						) > 1:
            A__        =  attr_scores[0]
        if isinstance(lowercase_,torch.Tensor						):
            A__        =  boxes.numpy()
        if isinstance(lowercase_,lowercase_						):
            A__        =  np.array(lowercase_						)
        assert isinstance(lowercase_,np.ndarray						)
        A__        =  np.prod(boxes[:, 2:] - boxes[:, :2],axis=1						)
        A__        =  np.argsort(-areas						).tolist()
        A__        =  boxes[sorted_idxs] if boxes is not None else None
        A__        =  obj_ids[sorted_idxs] if obj_ids is not None else None
        A__        =  obj_scores[sorted_idxs] if obj_scores is not None else None
        A__        =  attr_ids[sorted_idxs] if attr_ids is not None else None
        A__        =  attr_scores[sorted_idxs] if attr_scores is not None else None

        A__        =  [self._random_color(maximum=1						) for _ in range(len(lowercase_						)						)]
        A__        =  [assigned_colors[idx] for idx in sorted_idxs]
        if obj_ids is not None:
            A__        =  self._create_text_labels_attr(lowercase_,lowercase_,lowercase_,lowercase_						)
            for i in range(len(lowercase_						)						):
                A__        =  assigned_colors[i]
                self.add_box(boxes[i],lowercase_						)
                self.draw_labels(labels[i],boxes[i],lowercase_						)


    def 		snake_case__	( self					:			Any,lowercase_					:			int,lowercase_					:			Dict,lowercase_					:			int						)->       List[str]:





        '''simple docstring'''







        A__ ,		A__ ,		A__ ,		A__        =  box
        A__        =  (xa, ya)
        A__        =  (ya - ya) * (xa - xa)
        A__        =  _SMALL_OBJ * self.scale
        if instance_area < small or ya - ya < 4_0 * self.scale:
            if ya >= self.height - 5:
                A__        =  (xa, ya)
            else:
                A__        =  (xa, ya)

        A__        =  (ya - ya) / np.sqrt(self.height * self.width						)
        A__        =  self._change_color_brightness(lowercase_,brightness_factor=0.7						)
        A__        =  np.clip((height_ratio - 0.02) / 0.08 + 1,1.2,2						)
        font_size *= 0.75 * self.font_size

        self.draw_text(
            text=lowercase_,position=lowercase_,color=lowercase_,)


    def 		snake_case__	( self					:			List[Any],lowercase_					:			str,lowercase_					:			Optional[int],lowercase_					:			Union[str, Any]="g",lowercase_					:			Union[str, Any]="left",)->       List[str]:





        '''simple docstring'''







        A__        =  0
        A__        =  self.font_size
        A__        =  np.maximum(list(mplc.to_rgb(lowercase_						)						),0.2						)
        A__        =  max(0.8,np.max(lowercase_						)						)
        A__        =  {
            'facecolor': 'black',
            'alpha': self.alpha,
            'pad': self.pad,
            'edgecolor': 'none',
        }
        A__ ,		A__        =  position
        self.ax.text(
            lowercase_,lowercase_,lowercase_,size=font_size * self.scale,family='sans-serif',bbox=lowercase_,verticalalignment='top',horizontalalignment=lowercase_,color=lowercase_,zorder=1_0,rotation=lowercase_,)


    def 		snake_case__	( self					:			int,lowercase_					:			Optional[Any]=None						)->       str:





        '''simple docstring'''







        if saveas is None:
            A__        =  self.saveas
        if saveas.lower().endswith('.jpg'						) or saveas.lower().endswith('.png'						):
            cva.imwrite(
                lowercase_,self._get_buffer()[:, :, ::-1],)
        else:
            self.fig.savefig(lowercase_						)


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			Any,lowercase_					:			Dict,lowercase_					:			List[str],lowercase_					:			Optional[int]						)->       List[str]:





        '''simple docstring'''







        A__        =  [self.idaobj[i] for i in classes]
        A__        =  [self.idaattr[i] for i in attr_classes]
        A__        =  [
            F'{label} {score:.2f} {attr} {attr_score:.2f}'
            for label, score, attr, attr_score in zip(lowercase_,lowercase_,lowercase_,lowercase_						)
        ]
        return labels


    def 		snake_case__	( self					:			Any,lowercase_					:			Optional[Any],lowercase_					:			int						)->       str:





        '''simple docstring'''







        A__        =  [self.idaobj[i] for i in classes]
        if scores is not None:
            if labels is None:
                A__        =  ['{:.0f}%'.format(s * 1_0_0						) for s in scores]
            else:
                A__        =  ['{} {:.0f}%'.format(lowercase_,s * 1_0_0						) for li, s in zip(lowercase_,lowercase_						)]
        return labels


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			str=2_5_5						)->       Any:





        '''simple docstring'''







        A__        =  np.random.randint(0,len(_COLORS						)						)
        A__        =  _COLORS[idx] * maximum
        if not self.rgb:
            A__        =  ret[::-1]
        return ret


    def 		snake_case__	( self					:			str						)->       Any:





        '''simple docstring'''







        if not self.pynb:
            A__ ,		A__        =  self.canvas.print_to_buffer()
            if (width, height) != (self.width, self.height):
                A__        =  cva.resize(self.img,(width, height)						)
            else:
                A__        =  self.img
        else:
            A__        =  io.BytesIO()  # works for cairo backend
            self.canvas.print_rgba(lowercase_						)
            A__ ,		A__        =  self.width, self.height
            A__        =  buf.getvalue()
            A__        =  self.img

        A__        =  np.frombuffer(lowercase_,dtype='uint8'						)
        A__        =  buffer.reshape(lowercase_,lowercase_,4						)
        A__ ,		A__        =  np.split(lowercase_,[3],axis=2						)

        try:
            import numexpr as ne  # fuse them with numexpr

            A__        =  ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'						)
        except ImportError:
            A__        =  alpha.astype('float32'						) / 255.0
            A__        =  img * (1 - alpha) + rgb * alpha

        return visualized_image.astype('uint8'						)


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





        '''simple docstring'''







        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
        A__        =  mplc.to_rgb(lowercase_						)
        A__        =  colorsys.rgb_to_hls(*mplc.to_rgb(lowercase_						)						)
        A__        =  polygon_color[1] + (brightness_factor * polygon_color[1])
        A__        =  0.0 if modified_lightness < 0.0 else modified_lightness
        A__        =  1.0 if modified_lightness > 1.0 else modified_lightness
        A__        =  colorsys.hls_to_rgb(polygon_color[0],lowercase_,polygon_color[2]						)
        return modified_color




lowercase_									=    (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				['input_values', 'attention_mask']


    def __init__( self					:			Tuple,lowercase_					:			Optional[Any]=1,lowercase_					:			Union[str, Any]=1_6_0_0_0,lowercase_					:			str=1_2_8,lowercase_					:			Optional[Any]=1_0_2_4,lowercase_					:			Union[str, Any]=0.0,lowercase_					:			Any=True,lowercase_					:			List[Any]=-4.2_677_393,lowercase_					:			Union[str, Any]=4.5_689_974,lowercase_					:			Dict=False,**lowercase_					:			Dict,)->       int:





        '''simple docstring'''







        super().__init__(feature_size=lowercase_,sampling_rate=lowercase_,padding_value=lowercase_,**lowercase_						)
        A__        =  num_mel_bins
        A__        =  max_length
        A__        =  do_normalize
        A__        =  mean
        A__        =  std
        A__        =  return_attention_mask


    def 		snake_case__	( self					:			Any,lowercase_					:			np.ndarray,lowercase_					:			int,)->       np.ndarray:





        '''simple docstring'''







        A__        =  torch.from_numpy(lowercase_						).unsqueeze(0						)
        A__        =  ta_kaldi.fbank(
            lowercase_,htk_compat=lowercase_,sample_frequency=self.sampling_rate,use_energy=lowercase_,window_type='hanning',num_mel_bins=self.num_mel_bins,dither=0.0,frame_shift=1_0,)

        A__        =  fbank.shape[0]
        A__        =  max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
            A__        =  torch.nn.ZeroPadad((0, 0, 0, difference)						)
            A__        =  pad_module(lowercase_						)
        elif difference < 0:
            A__        =  fbank[0:max_length, :]

        A__        =  fbank.numpy()

        return fbank


    def 		snake_case__	( self					:			int,lowercase_					:			np.ndarray						)->       np.ndarray:





        '''simple docstring'''







        return (input_values - (self.mean)) / (self.std * 2)


    def __call__( self					:			str,lowercase_					:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],lowercase_					:			Optional[int] = None,lowercase_					:			Optional[Union[str, TensorType]] = None,**lowercase_					:			str,)->       BatchFeature:





        '''simple docstring'''







        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                    F' {self.sampling_rate} and not {sampling_rate}.'						)
        else:
            logger.warning(
                'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                'Failing to do so can result in silent errors that might be hard to debug.'						)

        A__        =  isinstance(lowercase_,np.ndarray						) and len(raw_speech.shape						) > 1
        if is_batched_numpy and len(raw_speech.shape						) > 2:
            raise ValueError(F'Only mono-channel audio is supported for input to {self}'						)
        A__        =  is_batched_numpy or (
            isinstance(lowercase_,(list, tuple)						) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)						))
        )

        if is_batched:
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase_,np.ndarray						):
            A__        =  np.asarray(lowercase_,dtype=np.floataa						)
        elif isinstance(lowercase_,np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
            A__        =  raw_speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
            A__        =  [raw_speech]

        # extract fbank features and pad/truncate to max_length
        A__        =  [self._extract_fbank_features(lowercase_,max_length=self.max_length						) for waveform in raw_speech]

        # convert into BatchFeature
        A__        =  BatchFeature({'input_values': features}						)

        # make sure list is in array format
        A__        =  padded_inputs.get('input_values'						)
        if isinstance(input_values[0],lowercase_						):
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for feature in input_values]

        # normalization
        if self.do_normalize:
            A__        =  [self.normalize(lowercase_						) for feature in input_values]

        if return_tensors is not None:
            A__        =  padded_inputs.convert_to_tensors(lowercase_						)

        return padded_inputs

