





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A							:			Union[str, Any]         =			logging.get_logger(__name__)

__A							:			List[Any]         =			{"vocab_file": "sentencepiece.bpe.model"}

__A							:			str         =			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

__A							:			Dict         =			{
    "moussaKam/mbarthez": 1_024,
    "moussaKam/barthez": 1_024,
    "moussaKam/barthez-orangesum-title": 1_024,
}

__A							:			Dict         =			"▁"







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			VOCAB_FILES_NAMES
     __magic_name__   =			PRETRAINED_VOCAB_FILES_MAP
     __magic_name__   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__   =			['input_ids', 'attention_mask']




     def __init__(    self				,     snake_case_				,     snake_case_="<s>"				,     snake_case_="</s>"				,     snake_case_="</s>"				,     snake_case_="<s>"				,     snake_case_="<unk>"				,     snake_case_="<pad>"				,     snake_case_="<mask>"				,     snake_case_ = None				,     **snake_case_				,     ):
         # Mask token behave like a normal word, i.e. include the space before it
         _A     =							AddedToken(snake_case_				,     lstrip=snake_case_				,     rstrip=snake_case_							) if isinstance(snake_case_				,     snake_case_							) else mask_token

         _A     =							{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=snake_case_				,     eos_token=snake_case_				,     unk_token=snake_case_				,     sep_token=snake_case_				,     cls_token=snake_case_				,     pad_token=snake_case_				,     mask_token=snake_case_				,     sp_model_kwargs=self.sp_model_kwargs				,     **snake_case_				,     )

         _A     =							vocab_file
         _A     =							spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(str(snake_case_							)							)

         _A     =							{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         _A     =							len(self.sp_model							) - 1
         _A     =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _A     =							[self.cls_token_id]
         _A     =							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = False							):
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=snake_case_				,     token_ids_a=snake_case_				,     already_has_special_tokens=snake_case_							)

         if token_ids_a is None:
             return [1] + ([0] * len(snake_case_							)) + [1]
         return [1] + ([0] * len(snake_case_							)) + [1, 1] + ([0] * len(snake_case_							)) + [1]




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         _A     =							[self.sep_token_id]
         _A     =							[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




     @property
     def        lowerCAmelCase__		(    self							):
         return len(self.sp_model							)




     def        lowerCAmelCase__		(    self							):
         _A     =							{self.convert_ids_to_tokens(snake_case_							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return self.sp_model.encode(snake_case_				,     out_type=snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         _A     =							self.sp_model.PieceToId(snake_case_							)

         return spm_id if spm_id else self.unk_token_id




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							[]
         _A     =							''
         _A     =							False
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 if not prev_is_special:
                     out_string += " "
                 out_string += self.sp_model.decode(snake_case_							) + token
                 _A     =							True
                 _A     =							[]
             else:
                 current_sub_tokens.append(snake_case_							)
                 _A     =							False
         out_string += self.sp_model.decode(snake_case_							)
         return out_string.strip()




     def __getstate__(    self							):
         _A     =							self.__dict__.copy()
         _A     =							None
         return state




     def __setstate__(    self				,     snake_case_							):
         _A     =							d

         # for backward compatibility
         if not hasattr(self				,     'sp_model_kwargs'							):
             _A     =							{}

         _A     =							spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(self.vocab_file							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         if not os.path.isdir(snake_case_							):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
             return
         _A     =							os.path.join(
             snake_case_				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							) and os.path.isfile(self.vocab_file							):
             copyfile(self.vocab_file				,     snake_case_							)
         elif not os.path.isfile(self.vocab_file							):
             with open(snake_case_				,     'wb'							) as fi:
                 _A     =							self.sp_model.serialized_model_proto()
                 fi.write(snake_case_							)

         return (out_vocab_file,)












import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

__A							:			str         =			random.Random()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=1.0							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    if rng is None:
        _A     =							global_rng

    _A     =							[]
    for batch_idx in range(shape[0]				):
        values.append([]				)
        for _ in range(shape[1]				):
            values[-1].append(rng.random() * scale				)

    return values







class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=7				,     snake_case_=400				,     snake_case_=2000				,     snake_case_=2048				,     snake_case_=128				,     snake_case_=1				,     snake_case_=512				,     snake_case_=30				,     snake_case_=4_4100				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							min_seq_length
         _A     =							max_seq_length
         _A     =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _A     =							spectrogram_length
         _A     =							feature_size
         _A     =							num_audio_channels
         _A     =							hop_length
         _A     =							chunk_length
         _A     =							sampling_rate




     def        lowerCAmelCase__		(    self							):
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }




     def        lowerCAmelCase__		(    self				,     snake_case_=False				,     snake_case_=False							):
         def _flatten(snake_case_							):
             return list(itertools.chain(*snake_case_							)							)

         if equal_length:
             _A     =							[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
         else:
             # make sure that inputs increase in size
             _A     =							[
                 floats_list((x, self.feature_size)							)
                 for x in range(self.min_seq_length				,     self.max_seq_length				,     self.seq_length_diff							)
             ]
         if numpify:
             _A     =							[np.asarray(snake_case_							) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class      lowerCamelCase(						__snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			TvltFeatureExtractor




     def        lowerCAmelCase__		(    self							):
         _A     =							TvltFeatureExtractionTester(self							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)
         self.assertTrue(hasattr(snake_case_				,     'spectrogram_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'feature_size'							)							)
         self.assertTrue(hasattr(snake_case_				,     'num_audio_channels'							)							)
         self.assertTrue(hasattr(snake_case_				,     'hop_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'chunk_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'sampling_rate'							)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
             _A     =							feat_extract_first.save_pretrained(snake_case_							)[0]
             check_json_file_has_correct_format(snake_case_							)
             _A     =							self.feature_extraction_class.from_pretrained(snake_case_							)

         _A     =							feat_extract_first.to_dict()
         _A     =							feat_extract_second.to_dict()
         _A     =							dict_first.pop('mel_filters'							)
         _A     =							dict_second.pop('mel_filters'							)
         self.assertTrue(np.allclose(snake_case_				,     snake_case_							)							)
         self.assertEqual(snake_case_				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
             _A     =							os.path.join(snake_case_				,     'feat_extract.json'							)
             feat_extract_first.to_json_file(snake_case_							)
             _A     =							self.feature_extraction_class.from_json_file(snake_case_							)

         _A     =							feat_extract_first.to_dict()
         _A     =							feat_extract_second.to_dict()
         _A     =							dict_first.pop('mel_filters'							)
         _A     =							dict_second.pop('mel_filters'							)
         self.assertTrue(np.allclose(snake_case_				,     snake_case_							)							)
         self.assertEqual(snake_case_				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         # Initialize feature_extractor
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

         # create three inputs of length 800, 1000, and 1200
         _A     =							[floats_list((1, x)							)[0] for x in range(800				,     1400				,     200							)]
         _A     =							[np.asarray(snake_case_							) for speech_input in speech_inputs]

         # Test not batched input
         _A     =							feature_extractor(np_speech_inputs[0]				,     return_tensors='np'				,     sampling_rate=4_4100							).audio_values

         self.assertTrue(encoded_audios.ndim == 4							)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

         # Test batched
         _A     =							feature_extractor(snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100							).audio_values

         self.assertTrue(encoded_audios.ndim == 4							)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

         # Test audio masking
         _A     =							feature_extractor(
             snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100				,     mask_audio=snake_case_							).audio_values

         self.assertTrue(encoded_audios.ndim == 4							)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

         # Test 2-D numpy arrays are batched.
         _A     =							[floats_list((1, x)							)[0] for x in (800, 800, 800)]
         _A     =							np.asarray(snake_case_							)
         _A     =							feature_extractor(snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100							).audio_values
         self.assertTrue(encoded_audios.ndim == 4							)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							load_dataset('hf-internal-testing/librispeech_asr_dummy'				,     'clean'				,     split='validation'							)
         # automatic decoding with librispeech
         _A     =							ds.sort('id'							).select(range(snake_case_							)							)[:num_samples]['audio']

         return [x["array"] for x in speech_samples]




     def        lowerCAmelCase__		(    self							):
         _A     =							self._load_datasamples(1							)
         _A     =							TvltFeatureExtractor()
         _A     =							feature_extractor(snake_case_				,     return_tensors='pt'							).audio_values

         self.assertEquals(audio_values.shape				,     (1, 1, 192, 128)							)

         _A     =							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]							)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,     snake_case_				,     atol=1E-4							)							)





