
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     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Any     					=    ["input_features", "is_longer"]



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



		def __UpperCamelCase			(			self	)  -> Dict[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					copy.deepcopy(self.__dict__	)
				UpperCamelCase          =					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 __UpperCamelCase			(			self    ,				A_    ,				A_ = None	)  -> np.ndarray:
				"""simple docstring"""
				UpperCamelCase          =					spectrogram(
				    A_    ,				window_function(self.fft_window_size    ,				'hann'	)    ,				frame_length=self.fft_window_size    ,				hop_length=self.hop_length    ,				power=2.0    ,				mel_filters=A_    ,				log_mel='dB'    ,				)
				return log_mel_spectrogram.T



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					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
						UpperCamelCase          =					[0]
				if len(ranges[2]	) == 0:
						# if the audio is too short, we just use the first chunk
						UpperCamelCase          =					[0]
				# randomly choose index for each part
				UpperCamelCase          =					np.random.choice(ranges[0]	)
				UpperCamelCase          =					np.random.choice(ranges[1]	)
				UpperCamelCase          =					np.random.choice(ranges[2]	)

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

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> np.array:
				"""simple docstring"""
				if waveform.shape[0] > max_length:
						if truncation == "rand_trunc":
								UpperCamelCase          =					True
								# random crop to max_length (for compatibility) -> this should be handled by self.pad
								UpperCamelCase          =					len(A_	) - max_length
								UpperCamelCase          =					np.random.randint(0    ,				overflow + 1	)
								UpperCamelCase          =					waveform[idx : idx + max_length]
								UpperCamelCase          =					self._np_extract_fbank_features(A_    ,				self.mel_filters_slaney	)[None, :]
						elif truncation == "fusion":
								UpperCamelCase          =					self._np_extract_fbank_features(A_    ,				self.mel_filters	)
								UpperCamelCase          =					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
								UpperCamelCase          =					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.
										UpperCamelCase          =					np.stack([mel, mel, mel, mel]    ,				axis=0	)
										UpperCamelCase          =					False
								else:
										UpperCamelCase          =					self._random_mel_fusion(A_    ,				A_    ,				A_	)
										UpperCamelCase          =					True
						else:
								raise NotImplementedError(F'''data_truncating {truncation} not implemented'''	)

				else:
						UpperCamelCase          =					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":
										UpperCamelCase          =					int(max_length / len(A_	)	)
										UpperCamelCase          =					np.stack(np.tile(A_    ,				n_repeat + 1	)	)[:max_length]
								if padding == "repeatpad":
										UpperCamelCase          =					int(max_length / len(A_	)	)
										UpperCamelCase          =					np.stack(np.tile(A_    ,				A_	)	)
								UpperCamelCase          =					np.pad(A_    ,				(0, max_length - waveform.shape[0])    ,				mode='constant'    ,				constant_values=0	)

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

				return input_mel, longer



		def __call__(			self    ,				A_    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				**A_    ,				)  -> BatchFeature:
				"""simple docstring"""
				UpperCamelCase          =					truncation if truncation is not None else self.truncation
				UpperCamelCase          =					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.'	)

				UpperCamelCase          =					isinstance(A_    ,				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}'''	)
				UpperCamelCase          =					is_batched_numpy or (
				    isinstance(A_    ,				(list, tuple)	) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)	))
				)

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

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

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

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

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

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

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

				UpperCamelCase          =					{'input_features': input_mel, 'is_longer': is_longer}
				UpperCamelCase          =					BatchFeature(A_	)

				if return_tensors is not None:
						UpperCamelCase          =					input_features.convert_to_tensors(A_	)

				return input_features


import logging
import os

from .state import PartialState


class     lowercase (		logging.LoggerAdapter       ):



		@staticmethod
		def __UpperCamelCase			(			A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					PartialState()
				return not main_process_only or (main_process_only and state.is_main_process)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				*A_    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				if PartialState._shared_state == {}:
						raise RuntimeError(
						    'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'	)
				UpperCamelCase          =					kwargs.pop('main_process_only'    ,				A_	)
				UpperCamelCase          =					kwargs.pop('in_order'    ,				A_	)

				if self.isEnabledFor(A_	):
						if self._should_log(A_	):
								UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
								self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)

						elif in_order:
								UpperCamelCase          =					PartialState()
								for i in range(state.num_processes	):
										if i == state.process_index:
												UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
												self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)
										state.wait_for_everyone()






def 				A	( lowercase   ,	lowercase = None			)       ->  Dict:
		'''simple docstring'''

		if log_level is None:
				UpperCamelCase          =					os.environ.get('ACCELERATE_LOG_LEVEL'   ,	lowercase			)
		UpperCamelCase          =					logging.getLogger(lowercase			)
		if log_level is not None:
				logger.setLevel(log_level.upper()			)
				logger.root.setLevel(log_level.upper()			)
		return MultiProcessAdapter(lowercase   ,	{}			)
