





"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      ['input_features', 'attention_mask']





      def __init__(       self      ,						lowercase=80      ,						lowercase=16_000      ,						lowercase=80      ,						lowercase=0.0      ,						lowercase=True      ,						lowercase=True      ,						lowercase=True      ,						**lowercase      ,						)		->     Optional[Any]:
            super().__init__(feature_size=lowercase      ,						sampling_rate=lowercase      ,						padding_value=lowercase      ,						**lowercase )
            lowerCAmelCase  						=       num_mel_bins
            lowerCAmelCase  						=       do_ceptral_normalize
            lowerCAmelCase  						=       normalize_means
            lowerCAmelCase  						=       normalize_vars
            lowerCAmelCase  						=       True





      def     _snake_case			(       self      ,						lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
            lowerCAmelCase  						=       torch.from_numpy(lowercase ).unsqueeze(0 )
            lowerCAmelCase  						=       ta_kaldi.fbank(lowercase      ,						num_mel_bins=self.num_mel_bins      ,						sample_frequency=self.sampling_rate )
            return features.numpy()





      @staticmethod
      def     _snake_case			(       lowercase      ,						lowercase      ,						lowercase = True      ,						lowercase = True      ,						lowercase = 0.0      ,						)		->     np.ndarray:
            # make sure we normalize float32 arrays
            if normalize_means:
                  lowerCAmelCase  						=       x[:input_length].mean(axis=0 )
                  lowerCAmelCase  						=       np.subtract(lowercase      ,						lowercase )
            if normalize_vars:
                  lowerCAmelCase  						=       x[:input_length].std(axis=0 )
                  lowerCAmelCase  						=       np.divide(lowercase      ,						lowercase )

            if input_length < x.shape[0]:
                  lowerCAmelCase  						=       padding_value

            # make sure array is in float32
            lowerCAmelCase  						=       x.astype(np.floataa )

            return x





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     List[np.ndarray]:
            lowerCAmelCase  						=       attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
            return [
                self.utterance_cmvn(lowercase      ,						lowercase      ,						self.normalize_means      ,						self.normalize_vars      ,						self.padding_value )
                for x, n in zip(lowercase      ,						lowercase )
            ]





      def __call__(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = None      ,						lowercase = False      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						**lowercase      ,						)		->     BatchFeature:

            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 `raw_speech` 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.""" )

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

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

            # always return batch
            if not is_batched:
                  lowerCAmelCase  						=       [raw_speech]

            # extract fbank features
            lowerCAmelCase  						=       [self._extract_fbank_features(lowercase ) for waveform in raw_speech]

            # convert into correct format for padding
            lowerCAmelCase  						=       BatchFeature({"""input_features""": features} )

            lowerCAmelCase  						=       self.pad(
                lowercase      ,						padding=lowercase      ,						max_length=lowercase      ,						truncation=lowercase      ,						pad_to_multiple_of=lowercase      ,						return_attention_mask=lowercase      ,						**lowercase      ,						)

            # make sure list is in array format
            lowerCAmelCase  						=       padded_inputs.get("""input_features""" )
            if isinstance(input_features[0]      ,						lowercase ):
                  lowerCAmelCase  						=       [np.asarray(lowercase      ,						dtype=np.floataa ) for feature in input_features]

            lowerCAmelCase  						=       padded_inputs.get("""attention_mask""" )
            if attention_mask is not None:
                  lowerCAmelCase  						=       [np.asarray(lowercase      ,						dtype=np.intaa ) for array in attention_mask]

            # Utterance-level cepstral mean and variance normalization
            if self.do_ceptral_normalize:
                  lowerCAmelCase  						=       (
                      np.array(lowercase      ,						dtype=np.intaa )
                      if self._get_padding_strategies(lowercase      ,						max_length=lowercase ) is not PaddingStrategy.DO_NOT_PAD
                      else None
                  )
                  lowerCAmelCase  						=       self.normalize(
                      padded_inputs["""input_features"""]      ,						attention_mask=lowercase )

            if return_tensors is not None:
                  lowerCAmelCase  						=       padded_inputs.convert_to_tensors(lowercase )

            return padded_inputs













"""simple docstring"""





import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__					=   "▁"

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

@require_sentencepiece
@require_tokenizers
class 		lowercase					(					_UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      BigBirdTokenizer
      _SCREAMING_SNAKE_CASE					=      BigBirdTokenizerFast
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      True





      def     _snake_case			(       self )		->     List[str]:
            super().setUp()

            lowerCAmelCase  						=       self.tokenizer_class(lowercase      ,						keep_accents=lowercase )
            tokenizer.save_pretrained(self.tmpdirname )





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       """<s>"""
            lowerCAmelCase  						=       1

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





      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       list(self.get_tokenizer().get_vocab().keys() )

            self.assertEqual(vocab_keys[0]      ,						"""<unk>""" )
            self.assertEqual(vocab_keys[1]      ,						"""<s>""" )
            self.assertEqual(vocab_keys[-1]      ,						"""[MASK]""" )
            self.assertEqual(len(lowercase )      ,						1_004 )





      def     _snake_case			(       self )		->     Union[str, Any]:
            self.assertEqual(self.get_tokenizer().vocab_size      ,						1_000 )





      def     _snake_case			(       self )		->     List[str]:
            if not self.test_rust_tokenizer:
                  return

            lowerCAmelCase  						=       self.get_tokenizer()
            lowerCAmelCase  						=       self.get_rust_tokenizer()

            lowerCAmelCase  						=       """I was born in 92000, and this is falsé."""

            lowerCAmelCase  						=       tokenizer.tokenize(lowercase )
            lowerCAmelCase  						=       rust_tokenizer.tokenize(lowercase )
            self.assertListEqual(lowercase      ,						lowercase )

            lowerCAmelCase  						=       tokenizer.encode(lowercase      ,						add_special_tokens=lowercase )
            lowerCAmelCase  						=       rust_tokenizer.encode(lowercase      ,						add_special_tokens=lowercase )
            self.assertListEqual(lowercase      ,						lowercase )

            lowerCAmelCase  						=       self.get_rust_tokenizer()
            lowerCAmelCase  						=       tokenizer.encode(lowercase )
            lowerCAmelCase  						=       rust_tokenizer.encode(lowercase )
            self.assertListEqual(lowercase      ,						lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       BigBirdTokenizer(lowercase      ,						keep_accents=lowercase )

            lowerCAmelCase  						=       tokenizer.tokenize("""This is a test""" )
            self.assertListEqual(lowercase      ,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(lowercase )      ,						[285, 46, 10, 170, 382]      ,						)

            lowerCAmelCase  						=       tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
            self.assertListEqual(
                lowercase      ,						[
                    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""",
                    """.""",
                ]      ,						)
            lowerCAmelCase  						=       tokenizer.convert_tokens_to_ids(lowercase )
            self.assertListEqual(
                lowercase      ,						[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,						)

            lowerCAmelCase  						=       tokenizer.convert_ids_to_tokens(lowercase )
            self.assertListEqual(
                lowercase      ,						[
                    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>""",
                    """.""",
                ]      ,						)





      @cached_property
      def     _snake_case			(       self )		->     Tuple:
            return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""" )





      @slow
      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       """Hello World!"""
            lowerCAmelCase  						=       [65, 18_536, 2_260, 101, 66]

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





      @slow
      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       (
                """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"""
            )
            # fmt: off
            lowerCAmelCase  						=       [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
            # fmt: on
            self.assertListEqual(lowercase      ,						self.big_tokenizer.encode(lowercase ) )





      @require_torch
      @slow
      def     _snake_case			(       self )		->     Tuple:
            import torch

            from transformers import BigBirdConfig, BigBirdModel

            # Build sequence
            lowerCAmelCase  						=       list(self.big_tokenizer.get_vocab().keys() )[:10]
            lowerCAmelCase  						=       """ """.join(lowercase )
            lowerCAmelCase  						=       self.big_tokenizer.encode_plus(lowercase      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowercase )
            lowerCAmelCase  						=       self.big_tokenizer.batch_encode_plus(
                [sequence + """ """ + sequence]      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowercase )

            lowerCAmelCase  						=       BigBirdConfig(attention_type="""original_full""" )
            lowerCAmelCase  						=       BigBirdModel(lowercase )

            assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

            with torch.no_grad():
                  model(**lowercase )
                  model(**lowercase )





      @slow
      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""" )
            lowerCAmelCase  						=       tokenizer.decode(tokenizer("""Paris is the [MASK].""" ).input_ids )

            self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]""" )





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            # fmt: off
            lowerCAmelCase  						=       {"""input_ids""": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 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], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 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]], """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, 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, 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=lowercase      ,						model_name="""google/bigbird-roberta-base"""      ,						revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""      ,						)






