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

import numpy as np

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


snake_case :		Any							      =  logging.get_logger(__name__)





class       _snake_case					(     lowerCAmelCase_				):
			UpperCamelCase__          =    ["input_features"]







			def __init__(				self						,   _a=80						,   _a=16_000						,   _a=160						,   _a=30						,   _a=400						,   _a=0.0						,   _a=False						,   **_a						,   ):
				super().__init__(
				    feature_size=lowerCamelCase__						,   sampling_rate=lowerCamelCase__						,   padding_value=lowerCamelCase__						,   return_attention_mask=lowerCamelCase__						,   **lowerCamelCase__						,   )
				__magic_name__		:		Dict	   =     n_fft
				__magic_name__		:		Dict	   =     hop_length
				__magic_name__		:		Union[str, Any]	   =     chunk_length
				__magic_name__		:		Optional[int]	   =     chunk_length * sampling_rate
				__magic_name__		:		Tuple	   =     self.n_samples // hop_length
				__magic_name__		:		Optional[Any]	   =     sampling_rate
				__magic_name__		:		List[str]	   =     mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2						,   num_mel_filters=lowerCamelCase__						,   min_frequency=0.0						,   max_frequency=80_00.0						,   sampling_rate=lowerCamelCase__						,   norm="slaney"						,   mel_scale="slaney"						,   )







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[int]	   =     spectrogram(
				    lowerCamelCase__						,   window_function(self.n_fft						,   "hann"		)						,   frame_length=self.n_fft						,   hop_length=self.hop_length						,   power=2.0						,   mel_filters=self.mel_filters						,   log_mel="log10"						,   )
				__magic_name__		:		Optional[int]	   =     log_spec[:, :-1]
				__magic_name__		:		Optional[int]	   =     np.maximum(lowerCamelCase__						,   log_spec.max() - 8.0		)
				__magic_name__		:		int	   =     (log_spec + 4.0) / 4.0
				return log_spec







			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 			SCREAMING_SNAKE_CASE    (				_a						,   _a						,   _a = 0.0		):


				if attention_mask is not None:
					__magic_name__		:		List[str]	   =     np.array(lowerCamelCase__						,   np.intaa		)
					__magic_name__		:		List[Any]	   =     []

					for vector, length in zip(lowerCamelCase__						,   attention_mask.sum(-1		)		):
						__magic_name__		:		int	   =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
						if length < normed_slice.shape[0]:
							__magic_name__		:		List[Any]	   =     padding_value

						normed_input_values.append(lowerCamelCase__		)
				else:
					__magic_name__		:		Tuple	   =     [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

				return normed_input_values







			def __call__(				self						,   _a						,   _a = True						,   _a = None						,   _a = None						,   _a = None						,   _a = "max_length"						,   _a = None						,   _a = None						,   _a = None						,   **_a						,   ):


				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."		)

				__magic_name__		:		List[str]	   =     isinstance(lowerCamelCase__						,   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}'''		)
				__magic_name__		:		Union[str, Any]	   =     is_batched_numpy or (
				    isinstance(lowerCamelCase__						,   (list, tuple)		) and (isinstance(raw_speech[0]						,   (np.ndarray, tuple, list)		))
				)

				if is_batched:
					__magic_name__		:		Tuple	   =     [np.asarray([speech]						,   dtype=np.floataa		).T for speech in raw_speech]
				elif not is_batched and not isinstance(lowerCamelCase__						,   np.ndarray		):
					__magic_name__		:		List[str]	   =     np.asarray(lowerCamelCase__						,   dtype=np.floataa		)
				elif isinstance(lowerCamelCase__						,   np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
					__magic_name__		:		int	   =     raw_speech.astype(np.floataa		)

				# always return batch
				if not is_batched:
					__magic_name__		:		str	   =     [np.asarray([raw_speech]		).T]

				__magic_name__		:		Union[str, Any]	   =     BatchFeature({"input_features": raw_speech}		)

				# convert into correct format for padding

				__magic_name__		:		int	   =     self.pad(
				    lowerCamelCase__						,   padding=lowerCamelCase__						,   max_length=max_length if max_length else self.n_samples						,   truncation=lowerCamelCase__						,   pad_to_multiple_of=lowerCamelCase__						,   return_attention_mask=return_attention_mask or do_normalize						,   )

				# zero-mean and unit-variance normalization
				if do_normalize:
					__magic_name__		:		Optional[Any]	   =     self.zero_mean_unit_var_norm(
					    padded_inputs["input_features"]						,   attention_mask=padded_inputs["attention_mask"]						,   padding_value=self.padding_value						,   )
					__magic_name__		:		List[str]	   =     np.stack(padded_inputs["input_features"]						,   axis=0		)

				# make sure list is in array format
				__magic_name__		:		Union[str, Any]	   =     padded_inputs.get("input_features"		).transpose(2						,   0						,   1		)

				__magic_name__		:		Any	   =     [self._np_extract_fbank_features(lowerCamelCase__		) for waveform in input_features[0]]

				if isinstance(input_features[0]						,   lowerCamelCase__		):
					__magic_name__		:		Optional[int]	   =     [np.asarray(lowerCamelCase__						,   dtype=np.floataa		) for feature in input_features]
				else:
					__magic_name__		:		int	   =     input_features

				if return_attention_mask:
					# rescale from sample (48000) to feature (3000)
					__magic_name__		:		Tuple	   =     padded_inputs["attention_mask"][:, :: self.hop_length]

				if return_tensors is not None:
					__magic_name__		:		List[str]	   =     padded_inputs.convert_to_tensors(lowerCamelCase__		)

				return padded_inputs







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     copy.deepcopy(self.__dict__		)
				__magic_name__		:		Optional[int]	   =     self.__class__.__name__
				if "mel_filters" in output:
					del output["mel_filters"]
				return output







from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     def __init__(							self						:    Union[str, Any]						,lowerCamelCase__						:    Callable						,lowerCamelCase__						:    Optional[Features] = None						,lowerCamelCase__						:    str = None						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    Optional[dict] = None						,lowerCamelCase__						:    Optional[int] = None						,**lowerCamelCase__						:    Optional[Any]						,)     ->		List[str]:




           '''simple docstring'''


           super().__init__(
               features=lowerCamelCase__						,cache_dir=lowerCamelCase__						,keep_in_memory=lowerCamelCase__						,streaming=lowerCamelCase__						,num_proc=lowerCamelCase__						,**lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						Generator(
               cache_dir=lowerCamelCase__						,features=lowerCamelCase__						,generator=lowerCamelCase__						,gen_kwargs=lowerCamelCase__						,**lowerCamelCase__						,)






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




           '''simple docstring'''


           if self.streaming:
                 SCREAMING_SNAKE_CASE		=						self.builder.as_streaming_dataset(split="""train"""       )
           # Build regular (map-style) dataset
           else:
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None

                 self.builder.download_and_prepare(
                     download_config=lowerCamelCase__						,download_mode=lowerCamelCase__						,verification_mode=lowerCamelCase__						,base_path=lowerCamelCase__						,num_proc=self.num_proc						,)
                 SCREAMING_SNAKE_CASE		=						self.builder.as_dataset(
                     split="""train"""						,verification_mode=lowerCamelCase__						,in_memory=self.keep_in_memory       )
           return dataset


