



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


__A             =random.Random()

def 	a						(				_UpperCAmelCase							: Optional[Any]			,       _UpperCAmelCase							: Any=1.0			,       _UpperCAmelCase							: Dict=None			,       _UpperCAmelCase							: Union[str, Any]=None						):







		'''simple docstring'''





		if rng is None:
				__UpperCAmelCase   :					Optional[Any]													=     global_rng

		__UpperCAmelCase   :					List[Any]													=     []
		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 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     '''facebook/musicgen-small'''
					__UpperCAmelCase   :					Optional[int]													=     tempfile.mkdtemp()




			def 			snake_case__					(       self							:			Dict	,	**a_							:			int			):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			int	,	**a_							:			List[str]			):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname			)




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     self.get_tokenizer()
					__UpperCAmelCase   :					Optional[Any]													=     self.get_feature_extractor()

					__UpperCAmelCase   :					Dict													=     MusicgenProcessor(tokenizer=_A	,	feature_extractor=_A			)

					processor.save_pretrained(self.tmpdirname			)
					__UpperCAmelCase   :					Union[str, Any]													=     MusicgenProcessor.from_pretrained(self.tmpdirname			)

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

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




			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     MusicgenProcessor(tokenizer=self.get_tokenizer()	,	feature_extractor=self.get_feature_extractor()			)
					processor.save_pretrained(self.tmpdirname			)

					__UpperCAmelCase   :					int													=     self.get_tokenizer(bos_token='''(BOS)'''	,	eos_token='''(EOS)'''			)
					__UpperCAmelCase   :					Optional[Any]													=     self.get_feature_extractor(do_normalize=_A	,	padding_value=1.0			)

					__UpperCAmelCase   :					Optional[Any]													=     MusicgenProcessor.from_pretrained(
					    self.tmpdirname	,	bos_token='''(BOS)'''	,	eos_token='''(EOS)'''	,	do_normalize=_A	,	padding_value=1.0			)

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

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




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     self.get_feature_extractor()
					__UpperCAmelCase   :					Dict													=     self.get_tokenizer()

					__UpperCAmelCase   :					List[str]													=     MusicgenProcessor(tokenizer=_A	,	feature_extractor=_A			)

					__UpperCAmelCase   :					Union[str, Any]													=     floats_list((3, 10_00)			)

					__UpperCAmelCase   :					List[Any]													=     feature_extractor(_A	,	return_tensors='''np'''			)
					__UpperCAmelCase   :					Optional[int]													=     processor(_A	,	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							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Tuple													=     self.get_feature_extractor()
					__UpperCAmelCase   :					Tuple													=     self.get_tokenizer()

					__UpperCAmelCase   :					List[str]													=     MusicgenProcessor(tokenizer=_A	,	feature_extractor=_A			)

					__UpperCAmelCase   :					Any													=     '''This is a test string'''

					__UpperCAmelCase   :					List[Any]													=     processor(text=_A			)

					__UpperCAmelCase   :					Dict													=     tokenizer(_A			)

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




			def 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     self.get_feature_extractor()
					__UpperCAmelCase   :					Tuple													=     self.get_tokenizer()

					__UpperCAmelCase   :					Any													=     MusicgenProcessor(tokenizer=_A	,	feature_extractor=_A			)

					__UpperCAmelCase   :					Optional[Any]													=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase   :					int													=     processor.batch_decode(sequences=_A			)
					__UpperCAmelCase   :					Any													=     tokenizer.batch_decode(_A			)

					self.assertListEqual(_A	,	_A			)




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     self.get_feature_extractor()
					__UpperCAmelCase   :					List[Any]													=     self.get_tokenizer()

					__UpperCAmelCase   :					Any													=     MusicgenProcessor(tokenizer=_A	,	feature_extractor=_A			)

					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							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     self.get_feature_extractor(padding_side='''left'''			)
					__UpperCAmelCase   :					int													=     self.get_tokenizer()

					__UpperCAmelCase   :					str													=     MusicgenProcessor(tokenizer=_A	,	feature_extractor=_A			)

					__UpperCAmelCase   :					Tuple													=     [floats_list((1, x)			)[0] for x in range(5	,	20	,	5			)]
					__UpperCAmelCase   :					Tuple													=     processor(_A			).padding_mask

					__UpperCAmelCase   :					List[str]													=     np.asarray(floats_list((3, 20)			)			)[:, None, :]
					__UpperCAmelCase   :					Union[str, Any]													=     processor.batch_decode(_A	,	padding_mask=_A			)

					self.assertIsInstance(_A	,	_A			)

					for audio in decoded_audios:
							self.assertIsInstance(_A	,	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 Optional, Tuple

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


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				__A             =None


__A             =logging.get_logger(__name__)


__A             ="▁"

__A             ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A             ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

__A             ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	VOCAB_FILES_NAMES
			UpperCamelCase      						=	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase      						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase      						=	["""input_ids""", """attention_mask"""]
			UpperCamelCase      						=	ReformerTokenizer




			def __init__(       self							:			Tuple	,	a_							:			Optional[int]=None	,	a_							:			int=None	,	a_							:			Dict="</s>"	,	a_							:			Dict="<unk>"	,	a_							:			List[Any]=[]	,	**a_							:			int	,	):
					'''simple docstring'''





					super().__init__(
					    a_	,	tokenizer_file=a_	,	eos_token=a_	,	unk_token=a_	,	additional_special_tokens=a_	,	**a_	,	)

					__UpperCAmelCase   :					Optional[Any]													=     vocab_file
					__UpperCAmelCase   :					Optional[Any]													=     False if not self.vocab_file else True




			def 			snake_case__					(       self							:			Dict	,	a_							:			str	,	a_							:			Optional[str] = None			):
					'''simple docstring'''





					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(a_			):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
							return
					__UpperCAmelCase   :					Tuple													=     os.path.join(
					    a_	,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

					return (out_vocab_file,)





