'''simple docstring'''






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


__a									=   logging.get_logger(__name__)





class        A__				(			UpperCamelCase  ):





   """simple docstring"""





   UpperCamelCase_			:		Union[str, Any]       							=	['''input_features''']




   def __init__(					self	:						Dict		,	lowerCAmelCase__	:						Dict=8_0		,	lowerCAmelCase__	:						Optional[int]=1_6_0_0_0		,	lowerCAmelCase__	:						Optional[Any]=1_6_0		,	lowerCAmelCase__	:						str=3_0		,	lowerCAmelCase__	:						Dict=4_0_0		,	lowerCAmelCase__	:						Union[str, Any]=0.0		,	lowerCAmelCase__	:						Tuple=False		,	**lowerCAmelCase__	:						Optional[int]		,	)			->       Optional[int]:







      """simple docstring"""




      super().__init__(
          feature_size=A_		,	sampling_rate=A_		,	padding_value=A_		,	return_attention_mask=A_		,	**A_		,	)
      _UpperCAmelCase				:      Optional[Any]				=  n_fft
      _UpperCAmelCase				:      Dict				=  hop_length
      _UpperCAmelCase				:      Union[str, Any]				=  chunk_length
      _UpperCAmelCase				:      List[Any]				=  chunk_length * sampling_rate
      _UpperCAmelCase				:      Any				=  self.n_samples // hop_length
      _UpperCAmelCase				:      Any				=  sampling_rate
      _UpperCAmelCase				:      Union[str, Any]				=  mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2		,	num_mel_filters=A_		,	min_frequency=0.0		,	max_frequency=8_0_0_0.0		,	sampling_rate=A_		,	norm="slaney"		,	mel_scale="slaney"		,	)




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







      """simple docstring"""




      _UpperCAmelCase				:      int				=  spectrogram(
          A_		,	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"		,	)
      _UpperCAmelCase				:      Tuple				=  log_spec[:, :-1]
      _UpperCAmelCase				:      Union[str, Any]				=  np.maximum(A_		,	log_spec.max() - 8.0     )
      _UpperCAmelCase				:      Any				=  (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      _lowerCAmelCase						(					lowerCAmelCase__	:						str		,	lowerCAmelCase__	:						Tuple		,	lowerCAmelCase__	:						Union[str, Any] = 0.0     )			->       Any:







      """simple docstring"""




      if attention_mask is not None:
         _UpperCAmelCase				:      str				=  np.array(A_		,	np.intaa     )
         _UpperCAmelCase				:      Tuple				=  []

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

            normed_input_values.append(A_     )
      else:
         _UpperCAmelCase				:      List[Any]				=  [(x - x.mean()) / np.sqrt(x.var() + 1e-7     ) for x in input_values]

      return normed_input_values




   def __call__(					self	:						Dict		,	lowerCAmelCase__	:						List[Any]		,	lowerCAmelCase__	:						List[Any] = True		,	lowerCAmelCase__	:						List[str] = None		,	lowerCAmelCase__	:						Tuple = None		,	lowerCAmelCase__	:						str = None		,	lowerCAmelCase__	:						Optional[int] = "max_length"		,	lowerCAmelCase__	:						Optional[int] = None		,	lowerCAmelCase__	:						Dict = None		,	lowerCAmelCase__	:						Union[str, Any] = None		,	**lowerCAmelCase__	:						Any		,	)			->       Optional[Any]:







      """simple docstring"""




      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(A_		,	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				:      str				=  is_batched_numpy or (
          isinstance(A_		,	(list, tuple)     ) and (isinstance(raw_speech[0]		,	(np.ndarray, tuple, list)     ))
      )

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

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

      _UpperCAmelCase				:      List[Any]				=  BatchFeature({"input_features": raw_speech}     )

      # convert into correct format for padding

      _UpperCAmelCase				:      Dict				=  self.pad(
          A_		,	padding=A_		,	max_length=max_length if max_length else self.n_samples		,	truncation=A_		,	pad_to_multiple_of=A_		,	return_attention_mask=return_attention_mask or do_normalize		,	)

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

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

      _UpperCAmelCase				:      List[Any]				=  [self._np_extract_fbank_features(A_     ) for waveform in input_features[0]]

      if isinstance(input_features[0]		,	A_     ):
         _UpperCAmelCase				:      List[Any]				=  [np.asarray(A_		,	dtype=np.floataa     ) for feature in input_features]
      else:
         _UpperCAmelCase				:      Tuple				=  input_features

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

      if return_tensors is not None:
         _UpperCAmelCase				:      int				=  padded_inputs.convert_to_tensors(A_     )

      return padded_inputs




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







      """simple docstring"""




      _UpperCAmelCase				:      Optional[Any]				=  copy.deepcopy(self.__dict__     )
      _UpperCAmelCase				:      Optional[Any]				=  self.__class__.__name__
      if "mel_filters" in output:
         del output["mel_filters"]
      return output



import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     '▁'

SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
class 		_SCREAMING_SNAKE_CASE     (						A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    BertGenerationTokenizer
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    True


     def        __snake_case(  self    ):
       super().setUp()

       _UpperCAmelCase		:							Optional[int]							=     BertGenerationTokenizer(A_							,					keep_accents=A_    )
       tokenizer.save_pretrained(self.tmpdirname    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     """<s>"""
       _UpperCAmelCase		:							Any							=     1

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


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]							,					"""<unk>"""    )
       self.assertEqual(vocab_keys[1]							,					"""<s>"""    )
       self.assertEqual(vocab_keys[-1]							,					"""<pad>"""    )
       self.assertEqual(len(A_    )							,					10_02    )


     def        __snake_case(  self    ):
       self.assertEqual(self.get_tokenizer().vocab_size							,					10_00    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     BertGenerationTokenizer(A_							,					keep_accents=A_    )

       _UpperCAmelCase		:							List[Any]							=     tokenizer.tokenize("""This is a test"""    )
       self.assertListEqual(A_							,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(A_    )							,					[2_85, 46, 10, 1_70, 3_82]							,					)

       _UpperCAmelCase		:							int							=     tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
       self.assertListEqual(
           A_							,					[
               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""",
               """.""",
           ]							,					)
       _UpperCAmelCase		:							List[str]							=     tokenizer.convert_tokens_to_ids(A_    )
       self.assertListEqual(
           A_							,					[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,					)

       _UpperCAmelCase		:							Tuple							=     tokenizer.convert_ids_to_tokens(A_    )
       self.assertListEqual(
           A_							,					[
               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    ):
       return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""    )


     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     """Hello World!"""
       _UpperCAmelCase		:							str							=     [1_85_36, 22_60, 1_01]

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


     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     (
           """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"""
       )
       _UpperCAmelCase		:							List[Any]							=     [
           8_71,
           4_19,
           3_58,
           9_46,
           9_91,
           25_21,
           4_52,
           3_58,
           13_57,
           3_87,
           77_51,
           35_36,
           1_12,
           9_85,
           4_56,
           1_26,
           8_65,
           9_38,
           54_00,
           57_34,
           4_58,
           13_68,
           4_67,
           7_86,
           24_62,
           52_46,
           11_59,
           6_33,
           8_65,
           45_19,
           4_57,
           5_82,
           8_52,
           25_57,
           4_27,
           9_16,
           5_08,
           4_05,
           3_43_24,
           4_97,
           3_91,
           4_08,
           1_13_42,
           12_44,
           3_85,
           1_00,
           9_38,
           9_85,
           4_56,
           5_74,
           3_62,
           1_25_97,
           32_00,
           31_29,
           11_72,
       ]

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


     @require_torch
     @slow
     def        __snake_case(  self    ):
       import torch

       from transformers import BertGenerationConfig, BertGenerationEncoder

       # Build sequence
       _UpperCAmelCase		:							Optional[Any]							=     list(self.big_tokenizer.get_vocab().keys()    )[:10]
       _UpperCAmelCase		:							Tuple							=     """ """.join(A_    )
       _UpperCAmelCase		:							List[Any]							=     self.big_tokenizer.encode_plus(A_							,					return_tensors="""pt"""							,					return_token_type_ids=A_    )
       _UpperCAmelCase		:							Any							=     self.big_tokenizer.batch_encode_plus(
           [sequence + """ """ + sequence]							,					return_tensors="""pt"""							,					return_token_type_ids=A_    )

       _UpperCAmelCase		:							int							=     BertGenerationConfig()
       _UpperCAmelCase		:							Dict							=     BertGenerationEncoder(A_    )

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

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




     @slow
     def        __snake_case(  self    ):
       # fmt: off
       _UpperCAmelCase		:							Optional[int]							=     {"""input_ids""": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=A_							,					model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""							,					revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""							,					)





