


"""simple docstring"""


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


a_       							=    "▁"

a_       							=    get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class snake_case      (  _UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			BertGenerationTokenizer
		__UpperCamelCase           =			False
		__UpperCamelCase           =			True







		def  a_ (					self      :       Any		)       ->						Optional[int]:





						'''simple docstring'''







						super().setUp()

						_A					  =			BertGenerationTokenizer(a__    ,   keep_accents=a__		)
						tokenizer.save_pretrained(self.tmpdirname		)







		def  a_ (					self      :       Dict		)       ->						Dict:





						'''simple docstring'''







						_A					  =			"<s>"
						_A					  =			1

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







		def  a_ (					self      :       int		)       ->						int:





						'''simple docstring'''







						_A					  =			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  a_ (					self      :       List[Any]		)       ->						List[Any]:





						'''simple docstring'''







						self.assertEqual(self.get_tokenizer().vocab_size    ,   10_00		)







		def  a_ (					self      :       List[str]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			BertGenerationTokenizer(a__    ,   keep_accents=a__		)

						_A					  =			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]    ,   )

						_A					  =			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",
						        ".",
						    ]    ,   )
						_A					  =			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]    ,   )

						_A					  =			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  a_ (					self      :       str		)       ->						List[Any]:





						'''simple docstring'''







						return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"		)







		@slow
		def  a_ (					self      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			"Hello World!"
						_A					  =			[1_85_36, 22_60, 1_01]

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







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





						'''simple docstring'''







						_A					  =			(
						    "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					  =			[
						    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  a_ (					self      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						import torch

						from transformers import BertGenerationConfig, BertGenerationEncoder

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

						_A					  =			BertGenerationConfig()
						_A					  =			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  a_ (					self      :       Tuple		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			{"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"    ,   )



"""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 snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			['input_features']







		def __init__(					self      :       int    ,   a__      :       Optional[Any]=80    ,   a__      :       Optional[int]=1_60_00    ,   a__      :       int=1_60    ,   a__      :       Union[str, Any]=30    ,   a__      :       Tuple=4_00    ,   a__      :       List[Any]=0.0    ,   a__      :       Optional[Any]=False    ,   **a__      :       List[Any]    ,   )       ->						str:





						'''simple docstring'''







						super().__init__(
						    feature_size=a__    ,   sampling_rate=a__    ,   padding_value=a__    ,   return_attention_mask=a__    ,   **a__    ,   )
						_A					  =			n_fft
						_A					  =			hop_length
						_A					  =			chunk_length
						_A					  =			chunk_length * sampling_rate
						_A					  =			self.n_samples // hop_length
						_A					  =			sampling_rate
						_A					  =			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  a_ (					self      :       int    ,   a__      :       np.array		)       ->						np.ndarray:





						'''simple docstring'''







						_A					  =			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"    ,   )
						_A					  =			log_spec[:, :-1]
						_A					  =			np.maximum(a__    ,   log_spec.max() - 8.0		)
						_A					  =			(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  a_ (					a__      :       List[np.ndarray]    ,   a__      :       List[np.ndarray]    ,   a__      :       float = 0.0		)       ->						List[np.ndarray]:





						'''simple docstring'''







						if attention_mask is not None:
										_A					  =			np.array(a__    ,   np.intaa		)
										_A					  =			[]

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

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

						return normed_input_values







		def __call__(					self      :       Optional[int]    ,   a__      :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,   a__      :       bool = True    ,   a__      :       Optional[int] = None    ,   a__      :       Optional[Union[str, TensorType]] = None    ,   a__      :       Optional[bool] = None    ,   a__      :       Optional[str] = "max_length"    ,   a__      :       Optional[int] = None    ,   a__      :       Optional[int] = None    ,   a__      :       Optional[bool] = None    ,   **a__      :       Dict    ,   )       ->						BatchFeature:





						'''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."		)

						_A					  =			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}"""		)
						_A					  =			is_batched_numpy or (
						    isinstance(a__    ,   (list, tuple)		) and (isinstance(raw_speech[0]    ,   (np.ndarray, tuple, list)		))
						)

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

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

						_A					  =			BatchFeature({"input_features": raw_speech}		)

						# convert into correct format for padding

						_A					  =			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:
										_A					  =			self.zero_mean_unit_var_norm(
										    padded_inputs["input_features"]    ,   attention_mask=padded_inputs["attention_mask"]    ,   padding_value=self.padding_value    ,   )
										_A					  =			np.stack(padded_inputs["input_features"]    ,   axis=0		)

						# make sure list is in array format
						_A					  =			padded_inputs.get("input_features"		).transpose(2    ,   0    ,   1		)

						_A					  =			[self._np_extract_fbank_features(a__		) for waveform in input_features[0]]

						if isinstance(input_features[0]    ,   a__		):
										_A					  =			[np.asarray(a__    ,   dtype=np.floataa		) for feature in input_features]
						else:
										_A					  =			input_features

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

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

						return padded_inputs







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





						'''simple docstring'''







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