





import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
							pass

if is_speech_available():
							from transformers import EncodecFeatureExtractor, MusicgenProcessor


UpperCAmelCase__  :	Any     =random.Random()





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=1.0	,					_UpperCAmelCase=None	,					_UpperCAmelCase=None )  ->     Tuple:
				if rng is None:
								lowerCamelCase		       =global_rng

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

				return values




@require_torch
@require_sentencepiece
class 					__A   (  unittest.TestCase			):


				def _snake_case				(						self							):
								lowerCamelCase		       ="""facebook/musicgen-small"""
								lowerCamelCase		       =tempfile.mkdtemp()


				def _snake_case				(						self      ,       **UpperCAmelCase_							):
								return TaTokenizer.from_pretrained(self.checkpoint      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       **UpperCAmelCase_							):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,       **UpperCAmelCase_							)


				def _snake_case				(						self							):
								shutil.rmtree(self.tmpdirname							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_tokenizer()
								lowerCamelCase		       =self.get_feature_extractor()

								lowerCamelCase		       =MusicgenProcessor(tokenizer=UpperCAmelCase_      ,       feature_extractor=UpperCAmelCase_							)

								processor.save_pretrained(self.tmpdirname							)
								lowerCamelCase		       =MusicgenProcessor.from_pretrained(self.tmpdirname							)

								self.assertEqual(processor.tokenizer.get_vocab()      ,       tokenizer.get_vocab()							)
								self.assertIsInstance(processor.tokenizer      ,       UpperCAmelCase_							)

								self.assertEqual(processor.feature_extractor.to_json_string()      ,       feature_extractor.to_json_string()							)
								self.assertIsInstance(processor.feature_extractor      ,       UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =MusicgenProcessor(tokenizer=self.get_tokenizer()      ,       feature_extractor=self.get_feature_extractor()							)
								processor.save_pretrained(self.tmpdirname							)

								lowerCamelCase		       =self.get_tokenizer(bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""							)
								lowerCamelCase		       =self.get_feature_extractor(do_normalize=UpperCAmelCase_      ,       padding_value=1.0							)

								lowerCamelCase		       =MusicgenProcessor.from_pretrained(
								    self.tmpdirname      ,       bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""      ,       do_normalize=UpperCAmelCase_      ,       padding_value=1.0							)

								self.assertEqual(processor.tokenizer.get_vocab()      ,       tokenizer_add_kwargs.get_vocab()							)
								self.assertIsInstance(processor.tokenizer      ,       UpperCAmelCase_							)

								self.assertEqual(processor.feature_extractor.to_json_string()      ,       feature_extractor_add_kwargs.to_json_string()							)
								self.assertIsInstance(processor.feature_extractor      ,       UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_feature_extractor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =MusicgenProcessor(tokenizer=UpperCAmelCase_      ,       feature_extractor=UpperCAmelCase_							)

								lowerCamelCase		       =floats_list((3, 1000)							)

								lowerCamelCase		       =feature_extractor(UpperCAmelCase_      ,       return_tensors="""np"""							)
								lowerCamelCase		       =processor(UpperCAmelCase_      ,       return_tensors="""np"""							)

								for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()      ,       input_processor[key].sum()      ,       delta=1E-2							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_feature_extractor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =MusicgenProcessor(tokenizer=UpperCAmelCase_      ,       feature_extractor=UpperCAmelCase_							)

								lowerCamelCase		       ="""This is a test string"""

								lowerCamelCase		       =processor(text=UpperCAmelCase_							)

								lowerCamelCase		       =tokenizer(UpperCAmelCase_							)

								for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key]							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_feature_extractor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =MusicgenProcessor(tokenizer=UpperCAmelCase_      ,       feature_extractor=UpperCAmelCase_							)

								lowerCamelCase		       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase		       =processor.batch_decode(sequences=UpperCAmelCase_							)
								lowerCamelCase		       =tokenizer.batch_decode(UpperCAmelCase_							)

								self.assertListEqual(UpperCAmelCase_      ,       UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_feature_extractor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =MusicgenProcessor(tokenizer=UpperCAmelCase_      ,       feature_extractor=UpperCAmelCase_							)

								self.assertListEqual(
								    processor.model_input_names      ,       feature_extractor.model_input_names      ,       msg="""`processor` and `feature_extractor` model input names do not match"""      ,       )


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_feature_extractor(padding_side="""left"""							)
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =MusicgenProcessor(tokenizer=UpperCAmelCase_      ,       feature_extractor=UpperCAmelCase_							)

								lowerCamelCase		       =[floats_list((1, x)							)[0] for x in range(5      ,       20      ,       5							)]
								lowerCamelCase		       =processor(UpperCAmelCase_							).padding_mask

								lowerCamelCase		       =np.asarray(floats_list((3, 20)							)							)[:, None, :]
								lowerCamelCase		       =processor.batch_decode(UpperCAmelCase_      ,       padding_mask=UpperCAmelCase_							)

								self.assertIsInstance(UpperCAmelCase_      ,       UpperCAmelCase_							)

								for audio in decoded_audios:
												self.assertIsInstance(UpperCAmelCase_      ,       np.ndarray							)

								self.assertTrue(decoded_audios[0].shape == (1, 10)							)
								self.assertTrue(decoded_audios[1].shape == (1, 15)							)
								self.assertTrue(decoded_audios[2].shape == (1, 20)							)












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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							UpperCAmelCase__  :	Union[str, Any]     =None


UpperCAmelCase__  :	str     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[int]     ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__  :	str     ={
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__  :	int     ={
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class 					__A   (  a			):
				__A            = VOCAB_FILES_NAMES
				__A            = PRETRAINED_VOCAB_FILES_MAP
				__A            = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A            = ["""input_ids""", """attention_mask"""]
				__A            = XLMRobertaTokenizer


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

								super().__init__(
								    UpperCAmelCase_      ,       tokenizer_file=UpperCAmelCase_      ,       bos_token=UpperCAmelCase_      ,       eos_token=UpperCAmelCase_      ,       sep_token=UpperCAmelCase_      ,       cls_token=UpperCAmelCase_      ,       unk_token=UpperCAmelCase_      ,       pad_token=UpperCAmelCase_      ,       mask_token=UpperCAmelCase_      ,       **UpperCAmelCase_      ,       )

								lowerCamelCase		       =vocab_file
								lowerCamelCase		       =False if not self.vocab_file else True


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):

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


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								lowerCamelCase		       =[self.sep_token_id]
								lowerCamelCase		       =[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]


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								if not self.can_save_slow_tokenizer:
												raise ValueError(
												    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
												    """tokenizer."""							)

								if not os.path.isdir(UpperCAmelCase_							):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""							)
												return
								lowerCamelCase		       =os.path.join(
								    UpperCAmelCase_      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

								if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCAmelCase_							):
												copyfile(self.vocab_file      ,       UpperCAmelCase_							)

								return (out_vocab_file,)





