


'''simple docstring'''


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


lowerCamelCase__    	=     logging.get_logger(__name__)






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	List[str]          =     ["input_features", "is_longer"]




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Optional[Any]=64	,  lowerCamelCase__  :     int=4_80_00	,  lowerCamelCase__  :     str=4_80	,  lowerCamelCase__  :     str=10	,  lowerCamelCase__  :     List[str]=10_24	,  lowerCamelCase__  :     Any=0.0	,  lowerCamelCase__  :     int=False	,  lowerCamelCase__  :     float = 0	,  lowerCamelCase__  :     float = 1_40_00	,  lowerCamelCase__  :     int = None	,  lowerCamelCase__  :     str = "fusion"	,  lowerCamelCase__  :     str = "repeatpad"	,  **lowerCamelCase__  :     Optional[Any]	,  )   ->str:



					'''simple docstring'''







					super().__init__(
					    feature_size=lowerCamelCase__	,  sampling_rate=lowerCamelCase__	,  padding_value=lowerCamelCase__	,  return_attention_mask=lowerCamelCase__	,  **lowerCamelCase__	,  )
					_UpperCAmelCase		:       int								=      top_db
					_UpperCAmelCase		:       Any								=      truncation
					_UpperCAmelCase		:       Optional[Any]								=      padding
					_UpperCAmelCase		:       int								=      fft_window_size
					_UpperCAmelCase		:       Optional[int]								=      (fft_window_size >> 1) + 1
					_UpperCAmelCase		:       Optional[int]								=      hop_length
					_UpperCAmelCase		:       Union[str, Any]								=      max_length_s
					_UpperCAmelCase		:       Union[str, Any]								=      max_length_s * sampling_rate
					_UpperCAmelCase		:       List[str]								=      sampling_rate
					_UpperCAmelCase		:       Optional[int]								=      frequency_min
					_UpperCAmelCase		:       List[Any]								=      frequency_max
					_UpperCAmelCase		:       Union[str, Any]								=      mel_filter_bank(
					    num_frequency_bins=self.nb_frequency_bins	,  num_mel_filters=lowerCamelCase__	,  min_frequency=lowerCamelCase__	,  max_frequency=lowerCamelCase__	,  sampling_rate=lowerCamelCase__	,  norm=lowerCamelCase__	,  mel_scale="htk"	,  )
					_UpperCAmelCase		:       Tuple								=      mel_filter_bank(
					    num_frequency_bins=self.nb_frequency_bins	,  num_mel_filters=lowerCamelCase__	,  min_frequency=lowerCamelCase__	,  max_frequency=lowerCamelCase__	,  sampling_rate=lowerCamelCase__	,  norm="slaney"	,  mel_scale="slaney"	,  )




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->Dict[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      copy.deepcopy(self.__dict__ )
					_UpperCAmelCase		:       Optional[Any]								=      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 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     np.array	,  lowerCamelCase__  :     Optional[np.array] = None )   ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      spectrogram(
					    lowerCamelCase__	,  window_function(self.fft_window_size	,  "hann" )	,  frame_length=self.fft_window_size	,  hop_length=self.hop_length	,  power=2.0	,  mel_filters=lowerCamelCase__	,  log_mel="dB"	,  )
					return log_mel_spectrogram.T




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     str )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      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		:       int								=      [0]
					if len(ranges[2] ) == 0:
							# if the audio is too short, we just use the first chunk
							_UpperCAmelCase		:       List[Any]								=      [0]
					# randomly choose index for each part
					_UpperCAmelCase		:       List[Any]								=      np.random.choice(ranges[0] )
					_UpperCAmelCase		:       List[str]								=      np.random.choice(ranges[1] )
					_UpperCAmelCase		:       List[str]								=      np.random.choice(ranges[2] )

					_UpperCAmelCase		:       Dict								=      mel[idx_front : idx_front + chunk_frames, :]
					_UpperCAmelCase		:       int								=      mel[idx_middle : idx_middle + chunk_frames, :]
					_UpperCAmelCase		:       int								=      mel[idx_back : idx_back + chunk_frames, :]

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




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     np.array	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str )   ->np.array:



					'''simple docstring'''







					if waveform.shape[0] > max_length:
							if truncation == "rand_trunc":
									_UpperCAmelCase		:       Optional[int]								=      True
									# random crop to max_length (for compatibility) -> this should be handled by self.pad
									_UpperCAmelCase		:       Optional[Any]								=      len(lowerCamelCase__ ) - max_length
									_UpperCAmelCase		:       List[Any]								=      np.random.randint(0	,  overflow + 1 )
									_UpperCAmelCase		:       str								=      waveform[idx : idx + max_length]
									_UpperCAmelCase		:       Dict								=      self._np_extract_fbank_features(lowerCamelCase__	,  self.mel_filters_slaney )[None, :]
							elif truncation == "fusion":
									_UpperCAmelCase		:       List[str]								=      self._np_extract_fbank_features(lowerCamelCase__	,  self.mel_filters )
									_UpperCAmelCase		:       List[Any]								=      max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
									_UpperCAmelCase		:       str								=      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		:       Optional[Any]								=      np.stack([mel, mel, mel, mel]	,  axis=0 )
											_UpperCAmelCase		:       List[str]								=      False
									else:
											_UpperCAmelCase		:       Optional[Any]								=      self._random_mel_fusion(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
											_UpperCAmelCase		:       Union[str, Any]								=      True
							else:
									raise NotImplementedError(F"""data_truncating {truncation} not implemented""" )

					else:
							_UpperCAmelCase		:       Union[str, Any]								=      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		:       Optional[int]								=      int(max_length / len(lowerCamelCase__ ) )
											_UpperCAmelCase		:       int								=      np.stack(np.tile(lowerCamelCase__	,  n_repeat + 1 ) )[:max_length]
									if padding == "repeatpad":
											_UpperCAmelCase		:       Optional[Any]								=      int(max_length / len(lowerCamelCase__ ) )
											_UpperCAmelCase		:       str								=      np.stack(np.tile(lowerCamelCase__	,  lowerCamelCase__ ) )
									_UpperCAmelCase		:       Any								=      np.pad(lowerCamelCase__	,  (0, max_length - waveform.shape[0])	,  mode="constant"	,  constant_values=0 )

							if truncation == "fusion":
									_UpperCAmelCase		:       List[Any]								=      self._np_extract_fbank_features(lowerCamelCase__	,  self.mel_filters )
									_UpperCAmelCase		:       Tuple								=      np.stack([input_mel, input_mel, input_mel, input_mel]	,  axis=0 )
							else:
									_UpperCAmelCase		:       List[str]								=      self._np_extract_fbank_features(lowerCamelCase__	,  self.mel_filters_slaney )[None, :]

					return input_mel, longer






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



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      truncation if truncation is not None else self.truncation
					_UpperCAmelCase		:       Any								=      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		:       Tuple								=      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}""" )
					_UpperCAmelCase		:       int								=      is_batched_numpy or (
					    isinstance(lowerCamelCase__	,  (list, tuple) ) and (isinstance(raw_speech[0]	,  (np.ndarray, tuple, list) ))
					)

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

					# always return batch
					if not is_batched:
							_UpperCAmelCase		:       int								=      [np.asarray(lowerCamelCase__ )]

					# convert to mel spectrogram, truncate and pad if needed.
					_UpperCAmelCase		:       List[str]								=      [
					    self._get_input_mel(lowerCamelCase__	,  max_length if max_length else self.nb_max_samples	,  lowerCamelCase__	,  lowerCamelCase__ )
					    for waveform in raw_speech
					]

					_UpperCAmelCase		:       Optional[int]								=      []
					_UpperCAmelCase		:       Tuple								=      []
					for mel, longer in padded_inputs:
							input_mel.append(lowerCamelCase__ )
							is_longer.append(lowerCamelCase__ )

					if truncation == "fusion" and sum(lowerCamelCase__ ) == 0:
							# if no audio is longer than 10s, then randomly select one audio to be longer
							_UpperCAmelCase		:       List[Any]								=      np.random.randint(0	,  len(lowerCamelCase__ ) )
							_UpperCAmelCase		:       List[str]								=      True

					if isinstance(input_mel[0]	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Dict								=      [np.asarray(lowerCamelCase__	,  dtype=np.floataa ) for feature in input_mel]

					# is_longer is a list of bool
					_UpperCAmelCase		:       Any								=      [[longer] for longer in is_longer]

					_UpperCAmelCase		:       str								=      {"input_features": input_mel, "is_longer": is_longer}
					_UpperCAmelCase		:       Dict								=      BatchFeature(lowerCamelCase__ )

					if return_tensors is not None:
							_UpperCAmelCase		:       Optional[int]								=      input_features.convert_to_tensors(lowerCamelCase__ )

					return input_features







'''simple docstring'''


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						)






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def __init__(		self  :     int	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str=13	,  lowerCamelCase__  :     Dict=7	,  lowerCamelCase__  :     str=True	,  lowerCamelCase__  :     List[str]=True	,  lowerCamelCase__  :     Dict=True	,  lowerCamelCase__  :     int=True	,  lowerCamelCase__  :     Tuple=99	,  lowerCamelCase__  :     Optional[int]=32	,  lowerCamelCase__  :     str=5	,  lowerCamelCase__  :     List[Any]=4	,  lowerCamelCase__  :     Any=37	,  lowerCamelCase__  :     List[Any]="gelu"	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     Tuple=0.1	,  lowerCamelCase__  :     Optional[int]=5_12	,  lowerCamelCase__  :     Any=16	,  lowerCamelCase__  :     Optional[Any]=2	,  lowerCamelCase__  :     Optional[Any]=0.0_2	,  lowerCamelCase__  :     Optional[int]=4	,  )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      parent
					_UpperCAmelCase		:       Optional[int]								=      batch_size
					_UpperCAmelCase		:       List[Any]								=      seq_length
					_UpperCAmelCase		:       Dict								=      is_training
					_UpperCAmelCase		:       int								=      use_attention_mask
					_UpperCAmelCase		:       List[Any]								=      use_token_type_ids
					_UpperCAmelCase		:       int								=      use_labels
					_UpperCAmelCase		:       str								=      vocab_size
					_UpperCAmelCase		:       Tuple								=      hidden_size
					_UpperCAmelCase		:       Dict								=      num_hidden_layers
					_UpperCAmelCase		:       List[Any]								=      num_attention_heads
					_UpperCAmelCase		:       Tuple								=      intermediate_size
					_UpperCAmelCase		:       List[Any]								=      hidden_act
					_UpperCAmelCase		:       Union[str, Any]								=      hidden_dropout_prob
					_UpperCAmelCase		:       List[str]								=      attention_probs_dropout_prob
					_UpperCAmelCase		:       Optional[int]								=      max_position_embeddings
					_UpperCAmelCase		:       Tuple								=      type_vocab_size
					_UpperCAmelCase		:       int								=      type_sequence_label_size
					_UpperCAmelCase		:       List[str]								=      initializer_range
					_UpperCAmelCase		:       Union[str, Any]								=      num_choices




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

					_UpperCAmelCase		:       Any								=      None
					if self.use_attention_mask:
							_UpperCAmelCase		:       Optional[Any]								=      random_attention_mask([self.batch_size, self.seq_length] )

					_UpperCAmelCase		:       int								=      None
					if self.use_token_type_ids:
							_UpperCAmelCase		:       List[Any]								=      ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size )

					_UpperCAmelCase		:       Tuple								=      RobertaPreLayerNormConfig(
					    vocab_size=self.vocab_size	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  max_position_embeddings=self.max_position_embeddings	,  type_vocab_size=self.type_vocab_size	,  is_decoder=lowerCamelCase__	,  initializer_range=self.initializer_range	,  )

					return config, input_ids, token_type_ids, attention_mask




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       int								=      config_and_inputs
					_UpperCAmelCase		:       Optional[Any]								=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
					return config, inputs_dict






			def 							lowerCAmelCase__    (		self  :     int )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      config_and_inputs

					_UpperCAmelCase		:       List[Any]								=      True
					_UpperCAmelCase		:       List[str]								=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					_UpperCAmelCase		:       int								=      ids_tensor([self.batch_size, self.seq_length]	,  vocab_size=2 )

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Tuple          =     True

			lowerCAmelCase :	Tuple          =     (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      FlaxRobertaPreLayerNormModelTester(self )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->int:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase		:       Any								=      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      model(np.ones((1, 1) ) )
							self.assertIsNotNone(lowerCamelCase__ )






@require_flax
class 	lowerCAmelCase__			(			unittest.TestCase	):




			@slow
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       str								=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Tuple								=      model(lowerCamelCase__ )[0]
					_UpperCAmelCase		:       int								=      [1, 11, 5_02_65]
					self.assertEqual(list(output.shape )	,  lowerCamelCase__ )
					# compare the actual values for a slice.
					_UpperCAmelCase		:       int								=      np.array(
					    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       List[Any]								=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Optional[Any]								=      model(lowerCamelCase__ )[0]
					# compare the actual values for a slice.
					_UpperCAmelCase		:       str								=      np.array(
					    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )



