


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        __lowercase       (							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[int]			       =		BarthezTokenizer
		UpperCamelCase     :					str			       =		BarthezTokenizerFast
		UpperCamelCase     :					Optional[Any]			       =		True
		UpperCamelCase     :					List[str]			       =		True







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					super().setUp()

					lowerCamelCase    		=  BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""	)
					tokenizer.save_pretrained(self.tmpdirname	)
					tokenizer.save_pretrained(self.tmpdirname		,      legacy_format=A	)
					lowerCamelCase    		=  tokenizer







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  """<pad>"""
					lowerCamelCase    		=  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	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  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(A	)		,      10_11_22	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					self.assertEqual(self.get_tokenizer().vocab_size		,      10_11_22	)







		@require_torch
		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					lowerCamelCase    		=  [0, 57, 30_18, 7_03_07, 91, 2]

					lowerCamelCase    		=  self.tokenizer(
					    A		,      max_length=len(A	)		,      padding=A		,      truncation=A		,      return_tensors="""pt"""	)
					self.assertIsInstance(A		,      A	)

					self.assertEqual((2, 6)		,      batch.input_ids.shape	)
					self.assertEqual((2, 6)		,      batch.attention_mask.shape	)
					lowerCamelCase    		=  batch.input_ids.tolist()[0]
					self.assertListEqual(A		,      A	)







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					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(A	)
					lowerCamelCase    		=  rust_tokenizer.tokenize(A	)
					self.assertListEqual(A		,      A	)

					lowerCamelCase    		=  tokenizer.encode(A		,      add_special_tokens=A	)
					lowerCamelCase    		=  rust_tokenizer.encode(A		,      add_special_tokens=A	)
					self.assertListEqual(A		,      A	)

					lowerCamelCase    		=  self.get_rust_tokenizer()
					lowerCamelCase    		=  tokenizer.encode(A	)
					lowerCamelCase    		=  rust_tokenizer.encode(A	)
					self.assertListEqual(A		,      A	)







		@slow
		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  {"""input_ids""": [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 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], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], """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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
					# fmt: on

					# moussaKam/mbarthez is a french model. So we also use french texts.
					lowerCamelCase    		=  [
					    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
					    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
					    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
					    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
					    """telles que la traduction et la synthèse de texte.""",
					]

					self.tokenizer_integration_test_util(
					    expected_encoding=A		,      model_name="""moussaKam/mbarthez"""		,      revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""		,      sequences=A		,      )





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[Any]			       =		["audio_values", "audio_mask"]







		def __init__( self		,      A=20_48		,      A=1		,      A=[16, 16]		,      A=1_28		,      A=4_41_00		,      A=86		,      A=20_48		,      A=0.0		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(
					    feature_size=A		,      sampling_rate=A		,      padding_value=A		,      **A		,      )

					lowerCamelCase    		=  spectrogram_length
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  feature_size // self.patch_size[1]
					lowerCamelCase    		=  n_fft
					lowerCamelCase    		=  sampling_rate // hop_length_to_sampling_rate
					lowerCamelCase    		=  sampling_rate
					lowerCamelCase    		=  padding_value
					lowerCamelCase    		=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2		,      num_mel_filters=A		,      min_frequency=0.0		,      max_frequency=22050.0		,      sampling_rate=A		,      norm="""slaney"""		,      mel_scale="""slaney"""		,      ).T







		def 					__A   ( self		,      A	)  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase    		=  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.T		,      log_mel="""dB"""		,      db_range=80.0		,      )
					lowerCamelCase    		=  log_spec[:, :-1]
					lowerCamelCase    		=  log_spec - 20.0
					lowerCamelCase    		=  np.clip(log_spec / 40.0		,      -2.0		,      0.0	) + 1.0
					return log_spec







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







					'''simple docstring'''







					if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
											raise ValueError(
											    """This feature extractor is set to support sampling rate"""
											    F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
											    F' 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."""	)

					lowerCamelCase    		=  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}'	)
					lowerCamelCase    		=  is_batched_numpy or (
					    isinstance(A		,      (list, tuple)	) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)	))
					)
					if is_batched:
								lowerCamelCase    		=  [np.asarray([speech]		,      dtype=np.floataa	).T for speech in raw_speech]
					elif not is_batched and not isinstance(A		,      np.ndarray	):
								lowerCamelCase    		=  np.asarray(A		,      dtype=np.floataa	)
					elif isinstance(A		,      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    		=  [np.asarray([raw_speech]	).T]

					# Convert audio signals to log mel spectrograms, truncate by time axis
					lowerCamelCase    		=  [
					    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
					]
					if isinstance(audio_features[0]		,      A	):
								lowerCamelCase    		=  [np.asarray(A		,      dtype=np.floataa	) for feature in audio_features]

					# Create audio attention mask
					lowerCamelCase    		=  max(
					    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
					if return_attention_mask:
								lowerCamelCase    		=  [
								    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
								    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
								    for feature in audio_features
								]
								lowerCamelCase    		=  np.array(A	).astype(np.floataa	)

					# convert into correct format for padding
					lowerCamelCase    		=  max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
					lowerCamelCase    		=  np.ones([len(A	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
					lowerCamelCase    		=  padded_audio_features * self.padding_value
					for i in range(len(A	)	):
								lowerCamelCase    		=  audio_features[i]
								lowerCamelCase    		=  feature

					# return as BatchFeature
					if return_attention_mask:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
					else:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features}

					lowerCamelCase    		=  BatchFeature(data=A		,      tensor_type=A	)
					return encoded_inputs

