import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD





class   __a      (   nn.Module				):
 __lowercase			:							int
 __lowercase			:							int
 __lowercase			:							float       					=			0.0
 __lowercase			:							int       					=			1
 __lowercase			:							int       					=			1
 __lowercase			:							bool       					=			True
 __lowercase			:							bool       					=			False
 __lowercase			:							bool       					=			False
 __lowercase			:							bool       					=			False
 __lowercase			:							jnp.dtype       					=			jnp.floataa
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       str        =       []
        lowercase__:       Optional[int]        =       []

        for i in range(self.num_layers			):
               lowercase__:       Union[str, Any]        =       self.in_channels if i == 0 else self.out_channels

               lowercase__:       Union[str, Any]        =       FlaxResnetBlockaD(
                   in_channels=a__    ,			out_channels=self.out_channels    ,			dropout_prob=self.dropout    ,			dtype=self.dtype    ,			)
               resnets.append(a__			)

               lowercase__:       str        =       FlaxTransformeraDModel(
                   in_channels=self.out_channels    ,			n_heads=self.num_attention_heads    ,			d_head=self.out_channels // self.num_attention_heads    ,			depth=1    ,			use_linear_projection=self.use_linear_projection    ,			only_cross_attention=self.only_cross_attention    ,			use_memory_efficient_attention=self.use_memory_efficient_attention    ,			dtype=self.dtype    ,			)
               attentions.append(a__			)

        lowercase__:       List[str]        =       resnets
        lowercase__:       List[Any]        =       attentions

        if self.add_downsample:
               lowercase__:       Tuple        =       FlaxDownsampleaD(self.out_channels    ,			dtype=self.dtype			)
 def __call__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=True			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       str        =       ()

        for resnet, attn in zip(self.resnets    ,			self.attentions			):
               lowercase__:       Any        =       resnet(a__    ,			a__    ,			deterministic=a__			)
               lowercase__:       List[str]        =       attn(a__    ,			a__    ,			deterministic=a__			)
               output_states += (hidden_states,)

        if self.add_downsample:
               lowercase__:       str        =       self.downsamplers_a(a__			)
               output_states += (hidden_states,)

        return hidden_states, output_states





class   __a      (   nn.Module				):
 __lowercase			:							int
 __lowercase			:							int
 __lowercase			:							float       					=			0.0
 __lowercase			:							int       					=			1
 __lowercase			:							bool       					=			True
 __lowercase			:							jnp.dtype       					=			jnp.floataa
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       []

        for i in range(self.num_layers			):
               lowercase__:       Tuple        =       self.in_channels if i == 0 else self.out_channels

               lowercase__:       Any        =       FlaxResnetBlockaD(
                   in_channels=a__    ,			out_channels=self.out_channels    ,			dropout_prob=self.dropout    ,			dtype=self.dtype    ,			)
               resnets.append(a__			)
        lowercase__:       Dict        =       resnets

        if self.add_downsample:
               lowercase__:       List[str]        =       FlaxDownsampleaD(self.out_channels    ,			dtype=self.dtype			)
 def __call__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=True			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       ()

        for resnet in self.resnets:
               lowercase__:       str        =       resnet(a__    ,			a__    ,			deterministic=a__			)
               output_states += (hidden_states,)

        if self.add_downsample:
               lowercase__:       List[Any]        =       self.downsamplers_a(a__			)
               output_states += (hidden_states,)

        return hidden_states, output_states





class   __a      (   nn.Module				):
 __lowercase			:							int
 __lowercase			:							int
 __lowercase			:							int
 __lowercase			:							float       					=			0.0
 __lowercase			:							int       					=			1
 __lowercase			:							int       					=			1
 __lowercase			:							bool       					=			True
 __lowercase			:							bool       					=			False
 __lowercase			:							bool       					=			False
 __lowercase			:							bool       					=			False
 __lowercase			:							jnp.dtype       					=			jnp.floataa
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       []
        lowercase__:       Any        =       []

        for i in range(self.num_layers			):
               lowercase__:       Optional[int]        =       self.in_channels if (i == self.num_layers - 1) else self.out_channels
               lowercase__:       Optional[int]        =       self.prev_output_channel if i == 0 else self.out_channels

               lowercase__:       Optional[int]        =       FlaxResnetBlockaD(
                   in_channels=resnet_in_channels + res_skip_channels    ,			out_channels=self.out_channels    ,			dropout_prob=self.dropout    ,			dtype=self.dtype    ,			)
               resnets.append(a__			)

               lowercase__:       str        =       FlaxTransformeraDModel(
                   in_channels=self.out_channels    ,			n_heads=self.num_attention_heads    ,			d_head=self.out_channels // self.num_attention_heads    ,			depth=1    ,			use_linear_projection=self.use_linear_projection    ,			only_cross_attention=self.only_cross_attention    ,			use_memory_efficient_attention=self.use_memory_efficient_attention    ,			dtype=self.dtype    ,			)
               attentions.append(a__			)

        lowercase__:       str        =       resnets
        lowercase__:       Optional[Any]        =       attentions

        if self.add_upsample:
               lowercase__:       Union[str, Any]        =       FlaxUpsampleaD(self.out_channels    ,			dtype=self.dtype			)
 def __call__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=True			) ->    Union[str, Any]:




        '''simple docstring'''
        for resnet, attn in zip(self.resnets    ,			self.attentions			):
               # pop res hidden states
               lowercase__:       Optional[int]        =       res_hidden_states_tuple[-1]
               lowercase__:       List[str]        =       res_hidden_states_tuple[:-1]
               lowercase__:       Dict        =       jnp.concatenate((hidden_states, res_hidden_states)    ,			axis=-1			)

               lowercase__:       Any        =       resnet(a__    ,			a__    ,			deterministic=a__			)
               lowercase__:       Tuple        =       attn(a__    ,			a__    ,			deterministic=a__			)

        if self.add_upsample:
               lowercase__:       List[Any]        =       self.upsamplers_a(a__			)

        return hidden_states





class   __a      (   nn.Module				):
 __lowercase			:							int
 __lowercase			:							int
 __lowercase			:							int
 __lowercase			:							float       					=			0.0
 __lowercase			:							int       					=			1
 __lowercase			:							bool       					=			True
 __lowercase			:							jnp.dtype       					=			jnp.floataa
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Tuple        =       []

        for i in range(self.num_layers			):
               lowercase__:       Dict        =       self.in_channels if (i == self.num_layers - 1) else self.out_channels
               lowercase__:       Optional[int]        =       self.prev_output_channel if i == 0 else self.out_channels

               lowercase__:       Tuple        =       FlaxResnetBlockaD(
                   in_channels=resnet_in_channels + res_skip_channels    ,			out_channels=self.out_channels    ,			dropout_prob=self.dropout    ,			dtype=self.dtype    ,			)
               resnets.append(a__			)

        lowercase__:       Tuple        =       resnets

        if self.add_upsample:
               lowercase__:       Union[str, Any]        =       FlaxUpsampleaD(self.out_channels    ,			dtype=self.dtype			)
 def __call__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=True			) ->    List[str]:




        '''simple docstring'''
        for resnet in self.resnets:
               # pop res hidden states
               lowercase__:       List[Any]        =       res_hidden_states_tuple[-1]
               lowercase__:       Any        =       res_hidden_states_tuple[:-1]
               lowercase__:       Union[str, Any]        =       jnp.concatenate((hidden_states, res_hidden_states)    ,			axis=-1			)

               lowercase__:       Tuple        =       resnet(a__    ,			a__    ,			deterministic=a__			)

        if self.add_upsample:
               lowercase__:       List[Any]        =       self.upsamplers_a(a__			)

        return hidden_states





class   __a      (   nn.Module				):
 __lowercase			:							int
 __lowercase			:							float       					=			0.0
 __lowercase			:							int       					=			1
 __lowercase			:							int       					=			1
 __lowercase			:							bool       					=			False
 __lowercase			:							bool       					=			False
 __lowercase			:							jnp.dtype       					=			jnp.floataa
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        # there is always at least one resnet
        lowercase__:       List[str]        =       [
            FlaxResnetBlockaD(
                in_channels=self.in_channels    ,			out_channels=self.in_channels    ,			dropout_prob=self.dropout    ,			dtype=self.dtype    ,			)
        ]

        lowercase__:       Optional[int]        =       []

        for _ in range(self.num_layers			):
               lowercase__:       List[Any]        =       FlaxTransformeraDModel(
                   in_channels=self.in_channels    ,			n_heads=self.num_attention_heads    ,			d_head=self.in_channels // self.num_attention_heads    ,			depth=1    ,			use_linear_projection=self.use_linear_projection    ,			use_memory_efficient_attention=self.use_memory_efficient_attention    ,			dtype=self.dtype    ,			)
               attentions.append(a__			)

               lowercase__:       List[str]        =       FlaxResnetBlockaD(
                   in_channels=self.in_channels    ,			out_channels=self.in_channels    ,			dropout_prob=self.dropout    ,			dtype=self.dtype    ,			)
               resnets.append(a__			)

        lowercase__:       Union[str, Any]        =       resnets
        lowercase__:       int        =       attentions
 def __call__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=True			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Any        =       self.resnets[0](a__    ,			a__			)
        for attn, resnet in zip(self.attentions    ,			self.resnets[1:]			):
               lowercase__:       int        =       attn(a__    ,			a__    ,			deterministic=a__			)
               lowercase__:       List[str]        =       resnet(a__    ,			a__    ,			deterministic=a__			)

        return hidden_states






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

import numpy as np

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


__lowerCAmelCase			=							logging.get_logger(__name__)





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Tuple       					=			['input_values', 'attention_mask']
 def __init__(       self    ,			lowerCAmelCase__ = 1    ,			lowerCAmelCase__ = 16_000    ,			lowerCAmelCase__ = 0.0    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = 80    ,			lowerCAmelCase__ = 16    ,			lowerCAmelCase__ = 64    ,			lowerCAmelCase__ = "hann_window"    ,			lowerCAmelCase__ = 1.0    ,			lowerCAmelCase__ = 80    ,			lowerCAmelCase__ = 7_600    ,			lowerCAmelCase__ = 1E-10    ,			lowerCAmelCase__ = 2    ,			lowerCAmelCase__ = True    ,			**lowerCAmelCase__    ,			) ->    List[Any]:




        '''simple docstring'''
        super().__init__(feature_size=lowerCAmelCase__    ,			sampling_rate=lowerCAmelCase__    ,			padding_value=lowerCAmelCase__    ,			**lowerCAmelCase__			)
        lowercase__:       Dict        =       do_normalize
        lowercase__:       Optional[Any]        =       return_attention_mask

        lowercase__:       str        =       num_mel_bins
        lowercase__:       Dict        =       hop_length
        lowercase__:       Dict        =       win_length
        lowercase__:       Optional[int]        =       win_function
        lowercase__:       Any        =       frame_signal_scale
        lowercase__:       Tuple        =       fmin
        lowercase__:       Tuple        =       fmax
        lowercase__:       Dict        =       mel_floor
        lowercase__:       int        =       reduction_factor

        lowercase__:       List[Any]        =       win_length * sampling_rate // 1_000
        lowercase__:       Optional[Any]        =       hop_length * sampling_rate // 1_000
        lowercase__:       Optional[int]        =       optimal_fft_length(self.sample_size			)
        lowercase__:       Optional[Any]        =       (self.n_fft // 2) + 1

        lowercase__:       str        =       window_function(window_length=self.sample_size    ,			name=self.win_function    ,			periodic=lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       mel_filter_bank(
            num_frequency_bins=self.n_freqs    ,			num_mel_filters=self.num_mel_bins    ,			min_frequency=self.fmin    ,			max_frequency=self.fmax    ,			sampling_rate=self.sampling_rate    ,			norm='slaney'    ,			mel_scale='slaney'    ,			)

        if frame_signal_scale != 1.0:
               warnings.warn(
                   'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'    ,			lowerCAmelCase__    ,			)
        if reduction_factor != 2.0:
               warnings.warn(
                   'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'    ,			lowerCAmelCase__    ,			)
 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 		SCREAMING_SNAKE_CASE__       (       lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = 0.0			) ->    List[np.ndarray]:




        '''simple docstring'''
        if attention_mask is not None:
               lowercase__:       List[str]        =       np.array(lowerCAmelCase__    ,			np.intaa			)
               lowercase__:       Tuple        =       []

               for vector, length in zip(lowerCAmelCase__    ,			attention_mask.sum(-1			)			):
                      lowercase__:       int        =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7			)
                      if length < normed_slice.shape[0]:
                             lowercase__:       Tuple        =       padding_value

                      normed_input_values.append(lowerCAmelCase__			)
        else:
               lowercase__:       Union[str, Any]        =       [(x - x.mean()) / np.sqrt(x.var() + 1E-7			) for x in input_values]

        return normed_input_values
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			) ->    np.ndarray:




        '''simple docstring'''
        lowercase__:       List[str]        =       spectrogram(
            lowerCAmelCase__    ,			window=self.window    ,			frame_length=self.sample_size    ,			hop_length=self.sample_stride    ,			fft_length=self.n_fft    ,			mel_filters=self.mel_filters    ,			mel_floor=self.mel_floor    ,			log_mel='log10'    ,			)
        return log_mel_spec.T
 def __call__(       self    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    BatchFeature:




        '''simple docstring'''
        if audio is None and audio_target is None:
               raise ValueError('You must provide either `audio` or `audio_target` values.'			)

        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 audio 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.'			)

        if audio is not None:
               lowercase__:       Dict        =       self._process_audio(
                   lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
        else:
               lowercase__:       str        =       None

        if audio_target is not None:
               lowercase__:       List[str]        =       self._process_audio(
                   lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)

               if inputs is None:
                      return inputs_target
               else:
                      lowercase__:       int        =       inputs_target['input_values']
                      lowercase__:       List[str]        =       inputs_target.get('attention_mask'			)
                      if decoder_attention_mask is not None:
                             lowercase__:       Optional[int]        =       decoder_attention_mask

        return inputs
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    BatchFeature:




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

        if is_batched:
               lowercase__:       Dict        =       [np.asarray(lowerCAmelCase__    ,			dtype=np.floataa			) for speech in speech]
        elif not is_batched and not isinstance(lowerCAmelCase__    ,			np.ndarray			):
               lowercase__:       Optional[Any]        =       np.asarray(lowerCAmelCase__    ,			dtype=np.floataa			)
        elif isinstance(lowerCAmelCase__    ,			np.ndarray			) and speech.dtype is np.dtype(np.floataa			):
               lowercase__:       Optional[Any]        =       speech.astype(np.floataa			)

        # always return batch
        if not is_batched:
               lowercase__:       Optional[int]        =       [speech]

        # needed to make pad() work on spectrogram inputs
        lowercase__:       str        =       self.feature_size

        # convert into correct format for padding
        if is_target:
               lowercase__:       int        =       [self._extract_mel_features(lowerCAmelCase__			) for waveform in speech]
               lowercase__:       Dict        =       BatchFeature({'input_values': features}			)
               lowercase__:       Union[str, Any]        =       self.num_mel_bins
        else:
               lowercase__:       Union[str, Any]        =       BatchFeature({'input_values': speech}			)

        lowercase__:       Dict        =       self.pad(
            lowerCAmelCase__    ,			padding=lowerCAmelCase__    ,			max_length=lowerCAmelCase__    ,			truncation=lowerCAmelCase__    ,			pad_to_multiple_of=lowerCAmelCase__    ,			return_attention_mask=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)

        lowercase__:       List[str]        =       feature_size_hack

        # convert input values to correct format
        lowercase__:       Union[str, Any]        =       padded_inputs['input_values']
        if not isinstance(input_values[0]    ,			np.ndarray			):
               lowercase__:       List[Any]        =       [np.asarray(lowerCAmelCase__    ,			dtype=np.floataa			) for array in input_values]
        elif (
            not isinstance(lowerCAmelCase__    ,			np.ndarray			)
            and isinstance(input_values[0]    ,			np.ndarray			)
            and input_values[0].dtype is np.dtype(np.floataa			)
        ):
               lowercase__:       Dict        =       [array.astype(np.floataa			) for array in input_values]
        elif isinstance(lowerCAmelCase__    ,			np.ndarray			) and input_values.dtype is np.dtype(np.floataa			):
               lowercase__:       Tuple        =       input_values.astype(np.floataa			)

        # convert attention_mask to correct format
        lowercase__:       Tuple        =       padded_inputs.get('attention_mask'			)
        if attention_mask is not None:
               lowercase__:       str        =       [np.asarray(lowerCAmelCase__    ,			dtype=np.intaa			) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if not is_target and self.do_normalize:
               lowercase__:       Tuple        =       (
                   attention_mask
                   if self._get_padding_strategies(lowerCAmelCase__    ,			max_length=lowerCAmelCase__			) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               lowercase__:       str        =       self.zero_mean_unit_var_norm(
                   padded_inputs['input_values']    ,			attention_mask=lowerCAmelCase__    ,			padding_value=self.padding_value			)

        if return_tensors is not None:
               lowercase__:       Union[str, Any]        =       padded_inputs.convert_to_tensors(lowerCAmelCase__			)

        return padded_inputs
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       super().to_dict()

        # Don't serialize these as they are derived from the other properties.
        lowercase__:       str        =       ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
        for name in names:
               if name in output:
                      del output[name]

        return output


