





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__							:      Any         =    get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class 	__lowerCAmelCase      (       _UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    int  		=					XLMRobertaTokenizer
   _UpperCamelCase			:    Union[str, Any]  		=					XLMRobertaTokenizerFast
   _UpperCamelCase			:    int  		=					True
   _UpperCamelCase			:    Dict  		=					True





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      super().setUp()

      # We have a SentencePiece fixture for testing
      a__						:    Optional[int]   		=	XLMRobertaTokenizer(snake_case       ,      keep_accents=snake_case   )
      tokenizer.save_pretrained(self.tmpdirname   )





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    int   		=	"<pad>"
      a__						:    Union[str, Any]   		=	1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case   )       ,      snake_case   )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case   )       ,      snake_case   )





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    Optional[Any]   		=	list(self.get_tokenizer().get_vocab().keys()   )

      self.assertEqual(vocab_keys[0]       ,      "<s>"   )
      self.assertEqual(vocab_keys[1]       ,      "<pad>"   )
      self.assertEqual(vocab_keys[-1]       ,      "<mask>"   )
      self.assertEqual(len(snake_case   )       ,      1_002   )





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      self.assertEqual(self.get_tokenizer().vocab_size       ,      1_002   )





   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	XLMRobertaTokenizer(snake_case       ,      keep_accents=snake_case   )

      a__						:    Tuple   		=	tokenizer.tokenize("This is a test"   )
      self.assertListEqual(snake_case       ,      ["▁This", "▁is", "▁a", "▁t", "est"]   )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(snake_case   )       ,      [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,      )

      a__						:    List[Any]   		=	tokenizer.tokenize("I was born in 92000, and this is falsé."   )
      self.assertListEqual(
          snake_case       ,      [
              SPIECE_UNDERLINE + "I",
              SPIECE_UNDERLINE + "was",
              SPIECE_UNDERLINE + "b",
              "or",
              "n",
              SPIECE_UNDERLINE + "in",
              SPIECE_UNDERLINE + "",
              "9",
              "2",
              "0",
              "0",
              "0",
              ",",
              SPIECE_UNDERLINE + "and",
              SPIECE_UNDERLINE + "this",
              SPIECE_UNDERLINE + "is",
              SPIECE_UNDERLINE + "f",
              "al",
              "s",
              "é",
              ".",
          ]       ,      )
      a__						:    Dict   		=	tokenizer.convert_tokens_to_ids(snake_case   )
      self.assertListEqual(
          snake_case       ,      [
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]       ,      )

      a__						:    Union[str, Any]   		=	tokenizer.convert_ids_to_tokens(snake_case   )
      self.assertListEqual(
          snake_case       ,      [
              SPIECE_UNDERLINE + "I",
              SPIECE_UNDERLINE + "was",
              SPIECE_UNDERLINE + "b",
              "or",
              "n",
              SPIECE_UNDERLINE + "in",
              SPIECE_UNDERLINE + "",
              "<unk>",
              "2",
              "0",
              "0",
              "0",
              ",",
              SPIECE_UNDERLINE + "and",
              SPIECE_UNDERLINE + "this",
              SPIECE_UNDERLINE + "is",
              SPIECE_UNDERLINE + "f",
              "al",
              "s",
              "<unk>",
              ".",
          ]       ,      )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      if not self.test_slow_tokenizer:
         # as we don't have a slow version, we can't compare the outputs between slow and fast versions
         return

      a__						:    List[str]   		=	(self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
            a__						:    Optional[Any]   		=	self.rust_tokenizer_class.from_pretrained(snake_case       ,      **snake_case   )
            a__						:    Any   		=	self.tokenizer_class.from_pretrained(snake_case       ,      **snake_case   )

            a__						:    Dict   		=	tempfile.mkdtemp()

            a__						:    Union[str, Any]   		=	tokenizer_r.save_pretrained(snake_case   )
            a__						:    str   		=	tokenizer_p.save_pretrained(snake_case   )

            # Checks it save with the same files + the tokenizer.json file for the fast one
            self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )
            a__						:    Union[str, Any]   		=	tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
            self.assertSequenceEqual(snake_case       ,      snake_case   )

            # Checks everything loads correctly in the same way
            a__						:    Optional[Any]   		=	tokenizer_r.from_pretrained(snake_case   )
            a__						:    str   		=	tokenizer_p.from_pretrained(snake_case   )

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(snake_case       ,      snake_case   )   )
               # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
               # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

            shutil.rmtree(snake_case   )

            # Save tokenizer rust, legacy_format=True
            a__						:    str   		=	tempfile.mkdtemp()

            a__						:    str   		=	tokenizer_r.save_pretrained(snake_case       ,      legacy_format=snake_case   )
            a__						:    List[str]   		=	tokenizer_p.save_pretrained(snake_case   )

            # Checks it save with the same files
            self.assertSequenceEqual(snake_case       ,      snake_case   )

            # Checks everything loads correctly in the same way
            a__						:    Dict   		=	tokenizer_r.from_pretrained(snake_case   )
            a__						:    Tuple   		=	tokenizer_p.from_pretrained(snake_case   )

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(snake_case       ,      snake_case   )   )

            shutil.rmtree(snake_case   )

            # Save tokenizer rust, legacy_format=False
            a__						:    List[str]   		=	tempfile.mkdtemp()

            a__						:    Tuple   		=	tokenizer_r.save_pretrained(snake_case       ,      legacy_format=snake_case   )
            a__						:    Tuple   		=	tokenizer_p.save_pretrained(snake_case   )

            # Checks it saved the tokenizer.json file
            self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )

            # Checks everything loads correctly in the same way
            a__						:    str   		=	tokenizer_r.from_pretrained(snake_case   )
            a__						:    Optional[Any]   		=	tokenizer_p.from_pretrained(snake_case   )

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
               self.assertTrue(hasattr(snake_case       ,      snake_case   )   )

            shutil.rmtree(snake_case   )





   @cached_property
   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"   )





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(snake_case       ,      f.name   )
         a__						:    Optional[Any]   		=	XLMRobertaTokenizer(f.name       ,      keep_accents=snake_case   )
         a__						:    List[Any]   		=	pickle.dumps(snake_case   )
      pickle.loads(snake_case   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      if not self.test_rust_tokenizer:
         return

      a__						:    Dict   		=	self.get_tokenizer()
      a__						:    Dict   		=	self.get_rust_tokenizer()

      a__						:    Union[str, Any]   		=	"I was born in 92000, and this is falsé."

      a__						:    List[Any]   		=	tokenizer.tokenize(snake_case   )
      a__						:    List[str]   		=	rust_tokenizer.tokenize(snake_case   )
      self.assertListEqual(snake_case       ,      snake_case   )

      a__						:    List[str]   		=	tokenizer.encode(snake_case       ,      add_special_tokens=snake_case   )
      a__						:    Optional[int]   		=	rust_tokenizer.encode(snake_case       ,      add_special_tokens=snake_case   )
      self.assertListEqual(snake_case       ,      snake_case   )

      a__						:    Optional[Any]   		=	self.get_rust_tokenizer()
      a__						:    str   		=	tokenizer.encode(snake_case   )
      a__						:    Tuple   		=	rust_tokenizer.encode(snake_case   )
      self.assertListEqual(snake_case       ,      snake_case   )





   @slow
   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      a__						:    List[str]   		=	"Hello World!"
      a__						:    List[Any]   		=	[0, 35_378, 6_661, 38, 2]
      # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
      # xlmr.eval()
      # xlmr.encode(symbols)

      self.assertListEqual(snake_case       ,      self.big_tokenizer.encode(snake_case   )   )





   @slow
   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Optional[Any]   		=	(
          "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
          " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
      )
      a__						:    str   		=	[
          0,
          3_293,
          83,
          10,
          4_552,
          4_989,
          7_986,
          678,
          10,
          5_915,
          111,
          179_459,
          124_850,
          4,
          6_044,
          237,
          12,
          6,
          5,
          6,
          4,
          6_780,
          705,
          15,
          1_388,
          44,
          378,
          10_114,
          711,
          152,
          20,
          6,
          5,
          22_376,
          642,
          1_221,
          15_190,
          34_153,
          450,
          5_608,
          959,
          1_119,
          57_702,
          136,
          186,
          47,
          1_098,
          29_367,
          47,
          # 4426, # What fairseq tokenizes from "<unk>": "_<"
          # 3678, # What fairseq tokenizes from "<unk>": "unk"
          # 2740, # What fairseq tokenizes from "<unk>": ">"
          3,  # What we tokenize from "<unk>": "<unk>"
          6,  # Residue from the tokenization: an extra sentencepiece underline
          4,
          6_044,
          237,
          6_284,
          50_901,
          528,
          31,
          90,
          34,
          927,
          2,
      ]
      # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
      # xlmr.eval()
      # xlmr.encode(symbols)

      self.assertListEqual(snake_case       ,      self.big_tokenizer.encode(snake_case   )   )





   @slow
   def 						_snake_case			(			self   )					->	Tuple:
      """simple docstring"""





      a__						:    Any   		=	{"input_ids": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=snake_case       ,      model_name="xlm-roberta-base"       ,      revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"       ,      )








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


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







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Tuple  		=					["""input_values""", """attention_mask"""]





   def __init__(			self       ,      snake_case = 1       ,      snake_case = 16_000       ,      snake_case = 0.0       ,      snake_case = False       ,      snake_case = 80       ,      snake_case = 16       ,      snake_case = 64       ,      snake_case = "hann_window"       ,      snake_case = 1.0       ,      snake_case = 80       ,      snake_case = 7_600       ,      snake_case = 1E-10       ,      snake_case = 2       ,      snake_case = True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      super().__init__(feature_size=snake_case       ,      sampling_rate=snake_case       ,      padding_value=snake_case       ,      **snake_case   )
      a__						:    Any   		=	do_normalize
      a__						:    List[str]   		=	return_attention_mask

      a__						:    List[Any]   		=	num_mel_bins
      a__						:    List[str]   		=	hop_length
      a__						:    int   		=	win_length
      a__						:    List[Any]   		=	win_function
      a__						:    List[str]   		=	frame_signal_scale
      a__						:    List[Any]   		=	fmin
      a__						:    Optional[Any]   		=	fmax
      a__						:    Union[str, Any]   		=	mel_floor
      a__						:    Union[str, Any]   		=	reduction_factor

      a__						:    List[str]   		=	win_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	hop_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	optimal_fft_length(self.sample_size   )
      a__						:    Dict   		=	(self.n_fft // 2) + 1

      a__						:    str   		=	window_function(window_length=self.sample_size       ,      name=self.win_function       ,      periodic=snake_case   )

      a__						:    Tuple   		=	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"       ,      snake_case       ,      )
      if reduction_factor != 2.0:
         warnings.warn(
             "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )





   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 						_snake_case			(			snake_case       ,      snake_case       ,      snake_case = 0.0   )					->	List[np.ndarray]:
      """simple docstring"""





      if attention_mask is not None:
         a__						:    Tuple   		=	np.array(snake_case       ,      np.intaa   )
         a__						:    List[str]   		=	[]

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

            normed_input_values.append(snake_case   )
      else:
         a__						:    Any   		=	[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

      return normed_input_values





   def 						_snake_case			(			self       ,      snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    str   		=	spectrogram(
          snake_case       ,      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       ,      snake_case = None       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	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:
         a__						:    Dict   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )
      else:
         a__						:    Optional[int]   		=	None

      if audio_target is not None:
         a__						:    List[Any]   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )

         if inputs is None:
            return inputs_target
         else:
            a__						:    Tuple   		=	inputs_target["input_values"]
            a__						:    Tuple   		=	inputs_target.get("attention_mask"   )
            if decoder_attention_mask is not None:
               a__						:    Tuple   		=	decoder_attention_mask

      return inputs





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





      a__						:    Optional[int]   		=	isinstance(snake_case       ,      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}"""   )
      a__						:    List[Any]   		=	is_batched_numpy or (
          isinstance(snake_case       ,      (list, tuple)   ) and (isinstance(speech[0]       ,      (np.ndarray, tuple, list)   ))
      )

      if is_batched:
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for speech in speech]
      elif not is_batched and not isinstance(snake_case       ,      np.ndarray   ):
         a__						:    Any   		=	np.asarray(snake_case       ,      dtype=np.floataa   )
      elif isinstance(snake_case       ,      np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
         a__						:    List[Any]   		=	speech.astype(np.floataa   )

      # always return batch
      if not is_batched:
         a__						:    Union[str, Any]   		=	[speech]

      # needed to make pad() work on spectrogram inputs
      a__						:    Optional[Any]   		=	self.feature_size

      # convert into correct format for padding
      if is_target:
         a__						:    List[str]   		=	[self._extract_mel_features(snake_case   ) for waveform in speech]
         a__						:    Optional[Any]   		=	BatchFeature({"input_values": features}   )
         a__						:    str   		=	self.num_mel_bins
      else:
         a__						:    int   		=	BatchFeature({"input_values": speech}   )

      a__						:    int   		=	self.pad(
          snake_case       ,      padding=snake_case       ,      max_length=snake_case       ,      truncation=snake_case       ,      pad_to_multiple_of=snake_case       ,      return_attention_mask=snake_case       ,      **snake_case       ,      )

      a__						:    Any   		=	feature_size_hack

      # convert input values to correct format
      a__						:    Tuple   		=	padded_inputs["input_values"]
      if not isinstance(input_values[0]       ,      np.ndarray   ):
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for array in input_values]
      elif (
          not isinstance(snake_case       ,      np.ndarray   )
          and isinstance(input_values[0]       ,      np.ndarray   )
          and input_values[0].dtype is np.dtype(np.floataa   )
      ):
         a__						:    Union[str, Any]   		=	[array.astype(np.floataa   ) for array in input_values]
      elif isinstance(snake_case       ,      np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
         a__						:    Optional[int]   		=	input_values.astype(np.floataa   )

      # convert attention_mask to correct format
      a__						:    Optional[int]   		=	padded_inputs.get("attention_mask"   )
      if attention_mask is not None:
         a__						:    Tuple   		=	[np.asarray(snake_case       ,      dtype=np.intaa   ) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if not is_target and self.do_normalize:
         a__						:    Any   		=	(
             attention_mask
             if self._get_padding_strategies(snake_case       ,      max_length=snake_case   ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a__						:    Optional[Any]   		=	self.zero_mean_unit_var_norm(
             padded_inputs["input_values"]       ,      attention_mask=snake_case       ,      padding_value=self.padding_value   )

      if return_tensors is not None:
         a__						:    int   		=	padded_inputs.convert_to_tensors(snake_case   )

      return padded_inputs





   def 						_snake_case			(			self   )					->	Dict[str, Any]:
      """simple docstring"""





      a__						:    int   		=	super().to_dict()

      # Don't serialize these as they are derived from the other properties.
      a__						:    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

