



'''simple docstring'''

from typing import List, Optional, Union

import numpy as np

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


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					['input_values', 'padding_mask']







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       int = 24000	,_UpperCAmelCase					:       float = 0.0	,_UpperCAmelCase					:       float = None	,_UpperCAmelCase					:       float = None	,**_UpperCAmelCase					:       str	,):
									super().__init__(feature_size=_UpperCAmelCase	,sampling_rate=_UpperCAmelCase	,padding_value=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Optional[Any]        =							chunk_length_s
									_a     :      str        =							overlap







							@property
							def 							__lowercase			(							self					:       Union[str, Any]		):
									if self.chunk_length_s is None:
											return None
									else:
											return int(self.chunk_length_s * self.sampling_rate		)







							@property
							def 							__lowercase			(							self					:       Optional[int]		):
									if self.chunk_length_s is None or self.overlap is None:
											return None
									else:
											return max(1	,int((1.0 - self.overlap) * self.chunk_length		)		)







							def __call__(							self					:       Tuple	,_UpperCAmelCase					:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_UpperCAmelCase					:       Optional[Union[bool, str, PaddingStrategy]] = None	,_UpperCAmelCase					:       Optional[bool] = False	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       Optional[Union[str, TensorType]] = None	,_UpperCAmelCase					:       Optional[int] = None	,):
									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 padding and truncation:
											raise ValueError('Both padding and truncation were set. Make sure you only set one.'		)
									elif padding is None:
											# by default let's pad the inputs
											_a     :      Union[str, Any]        =							True

									_a     :      Optional[int]        =							bool(
									    isinstance(_UpperCAmelCase	,(list, tuple)		) and (isinstance(raw_audio[0]	,(np.ndarray, tuple, list)		))		)

									if is_batched:
											_a     :      Tuple        =							[np.asarray(_UpperCAmelCase	,dtype=np.floataa		).T for audio in raw_audio]
									elif not is_batched and not isinstance(_UpperCAmelCase	,np.ndarray		):
											_a     :      List[str]        =							np.asarray(_UpperCAmelCase	,dtype=np.floataa		)
									elif isinstance(_UpperCAmelCase	,np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
											_a     :      Optional[int]        =							raw_audio.astype(np.floataa		)

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

									# verify inputs are valid
									for idx, example in enumerate(_UpperCAmelCase		):
											if example.ndim > 2:
													raise ValueError(F"""Expected input shape (channels, length) but got shape {example.shape}"""		)
											if self.feature_size == 1 and example.ndim != 1:
													raise ValueError(F"""Expected mono audio but example has {example.shape[-1]} channels"""		)
											if self.feature_size == 2 and example.shape[-1] != 2:
													raise ValueError(F"""Expected stereo audio but example has {example.shape[-1]} channels"""		)

									_a     :      Tuple        =							None
									_a     :      str        =							BatchFeature({'input_values': raw_audio}		)
									if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
											if truncation:
													_a     :      Optional[Any]        =							min(array.shape[0] for array in raw_audio		)
													_a     :      Optional[int]        =							int(np.floor(max_length / self.chunk_stride		)		)
													_a     :      int        =							(nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
													_a     :      str        =							max(array.shape[0] for array in raw_audio		)
													_a     :      Optional[int]        =							int(np.ceil(max_length / self.chunk_stride		)		)
													_a     :      Optional[Any]        =							(nb_step - 1) * self.chunk_stride + self.chunk_length
													_a     :      str        =							'max_length'
											else:
													_a     :      Dict        =							input_values

        # normal padding on batch
									if padded_inputs is None:
											_a     :      Tuple        =							self.pad(
											    _UpperCAmelCase	,max_length=_UpperCAmelCase	,truncation=_UpperCAmelCase	,padding=_UpperCAmelCase	,return_attention_mask=_UpperCAmelCase	,)
											if padding:
													_a     :      Optional[Any]        =							padded_inputs.pop('attention_mask'		)

									_a     :      Any        =							[]
									for example in padded_inputs.pop('input_values'		):
											if self.feature_size == 1:
													_a     :      Optional[int]        =							example[..., None]
											input_values.append(example.T		)

									_a     :      Optional[Any]        =							input_values
									if return_tensors is not None:
											_a     :      str        =							padded_inputs.convert_to_tensors(_UpperCAmelCase		)

									return padded_inputs









'''simple docstring'''

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 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Optional[int]   				=					BarthezTokenizer
							lowerCAmelCase       :    int   				=					BarthezTokenizerFast
							lowerCAmelCase       :    Dict   				=					True
							lowerCAmelCase       :    str   				=					True







							def 							__lowercase			(							self					:       List[Any]		):
									super().setUp()

									_a     :      List[Any]        =							BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'		)
									tokenizer.save_pretrained(self.tmpdirname		)
									tokenizer.save_pretrained(self.tmpdirname	,legacy_format=_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							tokenizer







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      Optional[Any]        =							'<pad>'
									_a     :      List[Any]        =							1

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







							def 							__lowercase			(							self					:       str		):
									_a     :      Any        =							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(_UpperCAmelCase		)	,101122		)







							def 							__lowercase			(							self					:       Dict		):
									self.assertEqual(self.get_tokenizer().vocab_size	,101122		)







							@require_torch
							def 							__lowercase			(							self					:       Dict		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Dict        =							[0, 57, 3018, 70307, 91, 2]

									_a     :      Dict        =							self.tokenizer(
									    _UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

									self.assertEqual((2, 6)	,batch.input_ids.shape		)
									self.assertEqual((2, 6)	,batch.attention_mask.shape		)
									_a     :      Tuple        =							batch.input_ids.tolist()[0]
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									if not self.test_rust_tokenizer:
											return

									_a     :      str        =							self.get_tokenizer()
									_a     :      List[str]        =							self.get_rust_tokenizer()

									_a     :      Dict        =							'I was born in 92000, and this is falsé.'

									_a     :      List[Any]        =							tokenizer.tokenize(_UpperCAmelCase		)
									_a     :      Tuple        =							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Union[str, Any]        =							self.get_rust_tokenizer()
									_a     :      Any        =							tokenizer.encode(_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[int]		):
									# fmt: off
									_a     :      Optional[int]        =							{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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.
									_a     :      Optional[Any]        =							[
									    '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=_UpperCAmelCase	,model_name='moussaKam/mbarthez'	,revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,sequences=_UpperCAmelCase	,)




