
"""simple docstring"""




import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir
@dataclass
class SCREAMING_SNAKE_CASE       :






	'''simple docstring'''







	snake_case__							:		str

class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Optional[Any]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     get_tests_dir('''fixtures/test_sentencepiece.model'''			)
			a__   		=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''			)

			a__   		=     FakeOriginalTokenizer(vocab_file=__magic_name__			)

			with warnings.catch_warnings(record=__magic_name__			) as w:
					a__   		=     SpmConverter(__magic_name__			)
			self.assertEqual(len(__magic_name__			)       ,    0			)

			a__   		=     FakeOriginalTokenizer(vocab_file=__magic_name__			)

			with warnings.catch_warnings(record=__magic_name__			) as w:
					a__   		=     SpmConverter(__magic_name__			)
			self.assertEqual(len(__magic_name__			)       ,    1			)

			self.assertIn(
			    '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
			    ''' which is not implemented in the fast tokenizers.'''       ,    str(w[0].message			)       ,    )






"""simple docstring"""




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


__lowerCAmelCase							:  Union[str, Any] 				=		random.Random()

def __snake_case (      UpperCamelCase						,					UpperCamelCase=1.0						,					UpperCamelCase=None						,					UpperCamelCase=None    )       ->    Tuple:


		"""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




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






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Optional[int]			)			->   List[str]:

			'''simple docstring'''






			a__   		=     '''facebook/musicgen-small'''
			a__   		=     tempfile.mkdtemp()


	def    _UpperCamelCase							(  self :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   str:

			'''simple docstring'''






			return TaTokenizer.from_pretrained(self.checkpoint       ,    **__magic_name__			)


	def    _UpperCamelCase							(  self :Optional[int]       ,    **__magic_name__ :Any			)			->   Optional[int]:

			'''simple docstring'''






			return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,    **__magic_name__			)


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

			'''simple docstring'''






			shutil.rmtree(self.tmpdirname			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_tokenizer()
			a__   		=     self.get_feature_extractor()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

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

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


	def    _UpperCamelCase							(  self :Dict			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     MusicgenProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()			)
			processor.save_pretrained(self.tmpdirname			)

			a__   		=     self.get_tokenizer(bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''			)
			a__   		=     self.get_feature_extractor(do_normalize=__magic_name__       ,    padding_value=1.0			)

			a__   		=     MusicgenProcessor.from_pretrained(
			    self.tmpdirname       ,    bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''       ,    do_normalize=__magic_name__       ,    padding_value=1.0			)

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

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


	def    _UpperCamelCase							(  self :Any			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     floats_list((3, 1000)			)

			a__   		=     feature_extractor(__magic_name__       ,    return_tensors='''np'''			)
			a__   		=     processor(__magic_name__       ,    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    _UpperCamelCase							(  self :Tuple			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     '''This is a test string'''

			a__   		=     processor(text=__magic_name__			)

			a__   		=     tokenizer(__magic_name__			)

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


	def    _UpperCamelCase							(  self :Tuple			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

			a__   		=     processor.batch_decode(sequences=__magic_name__			)
			a__   		=     tokenizer.batch_decode(__magic_name__			)

			self.assertListEqual(__magic_name__       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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






	def    _UpperCamelCase							(  self :Any			)			->   List[str]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor(padding_side='''left'''			)
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

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

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

			self.assertIsInstance(__magic_name__       ,    __magic_name__			)

			for audio in decoded_audios:
					self.assertIsInstance(__magic_name__       ,    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)			)




