



"""simple docstring"""
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_				= logging.get_logger(__name__)




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["input_values", "attention_mask"]






							def __init__(   self			,     a_ = 1			,     a_ = 1_6_0_0_0			,     a_ = 0.0			,     a_ = False			,     a_ = 8_0			,     a_ = 1_6			,     a_ = 6_4			,     a_ = "hann_window"			,     a_ = 1.0			,     a_ = 8_0			,     a_ = 7_6_0_0			,     a_ = 1e-10			,     a_ = 2			,     a_ = True			,     **a_			,     ):
									super().__init__(feature_size=a_			,     sampling_rate=a_			,     padding_value=a_			,     **a_      )
									a_     :      Optional[int] 						=     do_normalize
									a_     :      Optional[Any] 						=     return_attention_mask

									a_     :      Tuple 						=     num_mel_bins
									a_     :      List[Any] 						=     hop_length
									a_     :      Dict 						=     win_length
									a_     :      Any 						=     win_function
									a_     :      Union[str, Any] 						=     frame_signal_scale
									a_     :      Dict 						=     fmin
									a_     :      Any 						=     fmax
									a_     :      Dict 						=     mel_floor
									a_     :      Tuple 						=     reduction_factor

									a_     :      Optional[int] 						=     win_length * sampling_rate // 1_0_0_0
									a_     :      Union[str, Any] 						=     hop_length * sampling_rate // 1_0_0_0
									a_     :      List[Any] 						=     optimal_fft_length(self.sample_size      )
									a_     :      str 						=     (self.n_fft // 2) + 1

									a_     :      List[Any] 						=     window_function(window_length=self.sample_size			,     name=self.win_function			,     periodic=a_      )

									a_     :      List[Any] 						=     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"			,     a_			,     )
									if reduction_factor != 2.0:
											warnings.warn(
											    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"			,     a_			,     )






							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def snake_case_  (   a_			,     a_			,     a_ = 0.0      ):
									if attention_mask is not None:
											a_     :      List[Any] 						=     np.array(a_			,     np.intaa      )
											a_     :      Optional[int] 						=     []

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

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

									return normed_input_values






							def snake_case_  (   self			,     a_			,     ):
									a_     :      Union[str, Any] 						=     spectrogram(
									    a_			,     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			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = None			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     **a_			,     ):
									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_     :      List[str] 						=     self._process_audio(
											    a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     **a_			,     )
									else:
											a_     :      Any 						=     None

									if audio_target is not None:
											a_     :      str 						=     self._process_audio(
											    a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     **a_			,     )

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

									return inputs






							def snake_case_  (   self			,     a_			,     a_ = False			,     a_ = False			,     a_ = None			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = None			,     **a_			,     ):
									a_     :      Any 						=     isinstance(a_			,     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_     :      Dict 						=     is_batched_numpy or (
									    isinstance(a_			,     (list, tuple)      ) and (isinstance(speech[0]			,     (np.ndarray, tuple, list)      ))
									)

									if is_batched:
											a_     :      Optional[int] 						=     [np.asarray(a_			,     dtype=np.floataa      ) for speech in speech]
									elif not is_batched and not isinstance(a_			,     np.ndarray      ):
											a_     :      Any 						=     np.asarray(a_			,     dtype=np.floataa      )
									elif isinstance(a_			,     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_     :      List[str] 						=     [speech]

									# needed to make pad() work on spectrogram inputs
									a_     :      Dict 						=     self.feature_size

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

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

									a_     :      List[str] 						=     feature_size_hack

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

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

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

									if return_tensors is not None:
											a_     :      str 						=     padded_inputs.convert_to_tensors(a_      )

									return padded_inputs






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     super().to_dict()

									# Don't serialize these as they are derived from the other properties.
									a_     :      Any 						=     ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
									for name in names:
											if name in output:
													del output[name]

									return output




"""simple docstring"""
import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					GPTSwaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False






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

									# We have a SentencePiece fixture for testing
									a_     :      Optional[int] 						=     GPTSwaTokenizer(a_			,     eos_token="<unk>"			,     bos_token="<unk>"			,     pad_token="<unk>"      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     "This is a test"
									a_     :      Tuple 						=     "This is a test"
									return input_text, output_text






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "<s>"
									a_     :      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      ):
									a_     :      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]			,     "j"      )
									self.assertEqual(len(a_      )			,     2_0_0_0      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     2_0_0_0      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     GPTSwaTokenizer(a_      )

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

									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_      )			,     [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]      )

									a_     :      str 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]			,     )
									# fmt: on

									a_     :      List[str] 						=     tokenizer.convert_tokens_to_ids(a_      )
									self.assertListEqual(
									    a_			,     [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]			,     )

									a_     :      Optional[int] 						=     tokenizer.convert_ids_to_tokens(a_      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]      )
									# fmt: on






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     GPTSwaTokenizer(a_      )
									a_     :      List[Any] 						=     ["This is a test", "I was born in 92000, and this is falsé."]
									a_     :      Optional[Any] 						=     [
									    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
									    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(a_			,     a_      ):
											self.assertListEqual(tokenizer.encode_fast(a_      )			,     a_      )

									# Test that decode_fast returns the input text
									for text, token_ids in zip(a_			,     a_      ):
											self.assertEqual(tokenizer.decode_fast(a_      )			,     a_      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Dict 						=     [
									    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
									    "Hey there, how are you doing this fine day?",
									    "This is a text with a trailing spaces followed by a dot     .",
									    "Häj sväjs lillebrör! =)",
									    "Det är inget fel på Mr. Cool",
									]

									# fmt: off
									a_     :      Union[str, Any] 						=     {"input_ids": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 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, 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, 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], [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]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="AI-Sweden/gpt-sw3-126m"			,     sequences=a_			,     )