




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCamelCase_						:      Optional[int]       =						logging.get_logger(__name__)

class        _a    (						UpperCAmelCase_							):
     SCREAMING_SNAKE_CASE_							:   Any       =						["""input_features""", """is_longer"""]






     def __init__(			self ,_SCREAMING_SNAKE_CASE=64 ,_SCREAMING_SNAKE_CASE=48_000 ,_SCREAMING_SNAKE_CASE=480 ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = 14_000 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = "fusion" ,_SCREAMING_SNAKE_CASE = "repeatpad" ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(
              feature_size=__lowercase ,sampling_rate=__lowercase ,padding_value=__lowercase ,return_attention_mask=__lowercase ,**__lowercase ,)
          _snake_case							=				top_db
          _snake_case							=				truncation
          _snake_case							=				padding
          _snake_case							=				fft_window_size
          _snake_case							=				(fft_window_size >> 1) + 1
          _snake_case							=				hop_length
          _snake_case							=				max_length_s
          _snake_case							=				max_length_s * sampling_rate
          _snake_case							=				sampling_rate
          _snake_case							=				frequency_min
          _snake_case							=				frequency_max
          _snake_case							=				mel_filter_bank(
              num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=__lowercase ,min_frequency=__lowercase ,max_frequency=__lowercase ,sampling_rate=__lowercase ,norm=__lowercase ,mel_scale="htk" ,)
          _snake_case							=				mel_filter_bank(
              num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=__lowercase ,min_frequency=__lowercase ,max_frequency=__lowercase ,sampling_rate=__lowercase ,norm="slaney" ,mel_scale="slaney" ,)






     def     _lowercase     (			self   )							->      Dict[str, Any]:
          _snake_case							=				copy.deepcopy(self.__dict__   )
          _snake_case							=				self.__class__.__name__
          if "mel_filters" in output:
               del output["mel_filters"]
          if "mel_filters_slaney" in output:
               del output["mel_filters_slaney"]
          return output






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      np.ndarray:
          _snake_case							=				spectrogram(
              __lowercase ,window_function(self.fft_window_size ,"hann"   ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=__lowercase ,log_mel="dB" ,)
          return log_mel_spectrogram.T






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							=				np.array_split(list(range(0 ,total_frames - chunk_frames + 1   )   ) ,3   )
          if len(ranges[1]   ) == 0:
               # if the audio is too short, we just use the first chunk
               _snake_case							=				[0]
          if len(ranges[2]   ) == 0:
               # if the audio is too short, we just use the first chunk
               _snake_case							=				[0]
          # randomly choose index for each part
          _snake_case							=				np.random.choice(ranges[0]   )
          _snake_case							=				np.random.choice(ranges[1]   )
          _snake_case							=				np.random.choice(ranges[2]   )

          _snake_case							=				mel[idx_front : idx_front + chunk_frames, :]
          _snake_case							=				mel[idx_middle : idx_middle + chunk_frames, :]
          _snake_case							=				mel[idx_back : idx_back + chunk_frames, :]

          _snake_case							=				torch.tensor(mel[None, None, :]   )
          _snake_case							=				torch.nn.functional.interpolate(
              __lowercase ,size=[chunk_frames, 64] ,mode="bilinear" ,align_corners=__lowercase   )
          _snake_case							=				mel_shrink[0][0].numpy()
          _snake_case							=				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0   )
          return mel_fusion






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      np.array:
          if waveform.shape[0] > max_length:
               if truncation == "rand_trunc":
                    _snake_case							=				True
                    # random crop to max_length (for compatibility) -> this should be handled by self.pad
                    _snake_case							=				len(__lowercase   ) - max_length
                    _snake_case							=				np.random.randint(0 ,overflow + 1   )
                    _snake_case							=				waveform[idx : idx + max_length]
                    _snake_case							=				self._np_extract_fbank_features(__lowercase ,self.mel_filters_slaney   )[None, :]
               elif truncation == "fusion":
                    _snake_case							=				self._np_extract_fbank_features(__lowercase ,self.mel_filters   )
                    _snake_case							=				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                    _snake_case							=				mel.shape[0]
                    if chunk_frames == total_frames:
                         # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                         # In this case, we just use the whole audio.
                         _snake_case							=				np.stack([mel, mel, mel, mel] ,axis=0   )
                         _snake_case							=				False
                    else:
                         _snake_case							=				self._random_mel_fusion(__lowercase ,__lowercase ,__lowercase   )
                         _snake_case							=				True
               else:
                    raise NotImplementedError(f"""data_truncating {truncation} not implemented"""   )

          else:
               _snake_case							=				False
               # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
               if waveform.shape[0] < max_length:
                    if padding == "repeat":
                         _snake_case							=				int(max_length / len(__lowercase   )   )
                         _snake_case							=				np.stack(np.tile(__lowercase ,n_repeat + 1   )   )[:max_length]
                    if padding == "repeatpad":
                         _snake_case							=				int(max_length / len(__lowercase   )   )
                         _snake_case							=				np.stack(np.tile(__lowercase ,__lowercase   )   )
                    _snake_case							=				np.pad(__lowercase ,(0, max_length - waveform.shape[0]) ,mode="constant" ,constant_values=0   )

               if truncation == "fusion":
                    _snake_case							=				self._np_extract_fbank_features(__lowercase ,self.mel_filters   )
                    _snake_case							=				np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0   )
               else:
                    _snake_case							=				self._np_extract_fbank_features(__lowercase ,self.mel_filters_slaney   )[None, :]

          return input_mel, longer






     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      BatchFeature:
          _snake_case							=				truncation if truncation is not None else self.truncation
          _snake_case							=				padding if padding else self.padding

          if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                        f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                        f""" was sampled with {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."   )

          _snake_case							=				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}"""   )
          _snake_case							=				is_batched_numpy or (
              isinstance(__lowercase ,(list, tuple)   ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list)   ))
          )

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

          # always return batch
          if not is_batched:
               _snake_case							=				[np.asarray(__lowercase   )]

          # convert to mel spectrogram, truncate and pad if needed.
          _snake_case							=				[
              self._get_input_mel(__lowercase ,max_length if max_length else self.nb_max_samples ,__lowercase ,__lowercase   )
              for waveform in raw_speech
          ]

          _snake_case							=				[]
          _snake_case							=				[]
          for mel, longer in padded_inputs:
               input_mel.append(__lowercase   )
               is_longer.append(__lowercase   )

          if truncation == "fusion" and sum(__lowercase   ) == 0:
               # if no audio is longer than 10s, then randomly select one audio to be longer
               _snake_case							=				np.random.randint(0 ,len(__lowercase   )   )
               _snake_case							=				True

          if isinstance(input_mel[0] ,__lowercase   ):
               _snake_case							=				[np.asarray(__lowercase ,dtype=np.floataa   ) for feature in input_mel]

          # is_longer is a list of bool
          _snake_case							=				[[longer] for longer in is_longer]

          _snake_case							=				{'''input_features''': input_mel, '''is_longer''': is_longer}
          _snake_case							=				BatchFeature(__lowercase   )

          if return_tensors is not None:
               _snake_case							=				input_features.convert_to_tensors(__lowercase   )

          return input_features








'''simple docstring'''





from manim import *

class        _a    (						__lowerCAmelCase							):






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				Rectangle(height=0.5 ,width=0.5   )
          _snake_case							=				Rectangle(height=0.4_6 ,width=0.4_6   ).set_stroke(width=0   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				VGroup(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				Text("CPU" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          cpu.move_to([-2.5, -0.5, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(4   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				Text("GPU" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          gpu.move_to([-1, -1, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )

          _snake_case							=				Text("Model" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          model.move_to([3, -1.0, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[]
          for i, rect in enumerate(_SCREAMING_SNAKE_CASE   ):
               rect.set_stroke(_SCREAMING_SNAKE_CASE   )
               # target = fill.copy().set_fill(YELLOW, opacity=0.7)
               # target.move_to(rect)
               # self.add(target)

               _snake_case							=				Rectangle(height=0.4_6 / 4 ,width=0.4_6 / 3   ).set_stroke(width=0.0   ).set_fill(_SCREAMING_SNAKE_CASE ,opacity=0.7   )

               if i == 0:
                    cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   ) ,buff=0.0_2 ,direction=_SCREAMING_SNAKE_CASE   )
                    cpu_target.set_x(cpu_target.get_x() + 0.1   )
               elif i == 3:
                    cpu_target.next_to(cpu_targs[0] ,direction=_SCREAMING_SNAKE_CASE ,buff=0.0   )
               else:
                    cpu_target.next_to(cpu_targs[i - 1] ,direction=_SCREAMING_SNAKE_CASE ,buff=0.0   )
               self.add(_SCREAMING_SNAKE_CASE   )
               cpu_targs.append(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )

          _snake_case							=				Text("Loaded Checkpoint" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,aligned_edge=_SCREAMING_SNAKE_CASE ,buff=0.4   )
          checkpoint.move_to([3, 0.5, 0]   )

          _snake_case							=				Square(side_length=2.2   )
          key.move_to([-5, 2, 0]   )

          _snake_case							=				MarkupText(
              f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" ,font_size=18 ,)

          key_text.move_to([-5, 2.4, 0]   )

          self.add(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				MarkupText(
              f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" ,font_size=18 ,)

          blue_text.next_to(_SCREAMING_SNAKE_CASE ,DOWN * 2.4 ,aligned_edge=key_text.get_left()   )

          _snake_case							=				MarkupText(
              f"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" ,font_size=24 ,)
          step_a.move_to([2, 2, 0]   )
          self.play(Write(_SCREAMING_SNAKE_CASE   ) ,Write(_SCREAMING_SNAKE_CASE   )   )

          self.play(Write(_SCREAMING_SNAKE_CASE ,run_time=1   ) ,Create(_SCREAMING_SNAKE_CASE ,run_time=1   )   )

          _snake_case							=				[]
          _snake_case							=				[]
          for i, rect in enumerate(_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				fill.copy().set_fill(_SCREAMING_SNAKE_CASE ,opacity=0.7   )
               target.move_to(_SCREAMING_SNAKE_CASE   )
               first_animations.append(GrowFromCenter(_SCREAMING_SNAKE_CASE ,run_time=1   )   )

               _snake_case							=				target.copy()
               cpu_target.generate_target()
               if i < 5:
                    cpu_target.target.move_to(cpu_left_col_base[i + 1]   )
               else:
                    cpu_target.target.move_to(cpu_right_col_base[i - 5]   )
               second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE ,run_time=1.5   )   )

          self.play(*_SCREAMING_SNAKE_CASE   )
          self.play(*_SCREAMING_SNAKE_CASE   )
          self.wait()


