




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__  =		random.Random()






def       snake_case      (							A		,     A=1.0		,     A=None		,     A=None    )							->							Dict:




	'''simple docstring'''


	if rng is None:
		lowerCamelCase_							:	str							     =							global_rng

	lowerCamelCase_							:	Optional[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        __SCREAMING_SNAKE_CASE							(	unittest.TestCase						):

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						shutil.rmtree(self.tmpdirname      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							self.get_tokenizer()
						lowerCamelCase_							:	Tuple							     =							self.get_feature_extractor()

						lowerCamelCase_							:	int							     =							MusicgenProcessor(tokenizer=__lowercase				, feature_extractor=__lowercase      )

						processor.save_pretrained(self.tmpdirname      )
						lowerCamelCase_							:	Dict							     =							MusicgenProcessor.from_pretrained(self.tmpdirname      )

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

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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							MusicgenProcessor(tokenizer=self.get_tokenizer()				, feature_extractor=self.get_feature_extractor()      )
						processor.save_pretrained(self.tmpdirname      )

						lowerCamelCase_							:	Tuple							     =							self.get_tokenizer(bos_token='(BOS)'				, eos_token='(EOS)'      )
						lowerCamelCase_							:	Optional[int]							     =							self.get_feature_extractor(do_normalize=__lowercase				, padding_value=1.0      )

						lowerCamelCase_							:	Tuple							     =							MusicgenProcessor.from_pretrained(
						    self.tmpdirname				, bos_token='(BOS)'				, eos_token='(EOS)'				, do_normalize=__lowercase				, padding_value=1.0      )

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

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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

						lowerCamelCase_							:	Optional[int]							     =							MusicgenProcessor(tokenizer=__lowercase				, feature_extractor=__lowercase      )

						lowerCamelCase_							:	str							     =							floats_list((3, 1_000)      )

						lowerCamelCase_							:	Any							     =							feature_extractor(__lowercase				, return_tensors='np'      )
						lowerCamelCase_							:	Optional[Any]							     =							processor(__lowercase				, 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 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							self.get_feature_extractor()
						lowerCamelCase_							:	Tuple							     =							self.get_tokenizer()

						lowerCamelCase_							:	Tuple							     =							MusicgenProcessor(tokenizer=__lowercase				, feature_extractor=__lowercase      )

						lowerCamelCase_							:	int							     =							'This is a test string'

						lowerCamelCase_							:	str							     =							processor(text=__lowercase      )

						lowerCamelCase_							:	List[Any]							     =							tokenizer(__lowercase      )

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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	str							     =							self.get_feature_extractor()
						lowerCamelCase_							:	Tuple							     =							self.get_tokenizer()

						lowerCamelCase_							:	Union[str, Any]							     =							MusicgenProcessor(tokenizer=__lowercase				, feature_extractor=__lowercase      )

						lowerCamelCase_							:	str							     =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCamelCase_							:	str							     =							processor.batch_decode(sequences=__lowercase      )
						lowerCamelCase_							:	List[Any]							     =							tokenizer.batch_decode(__lowercase      )

						self.assertListEqual(__lowercase				, __lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Tuple							     =							self.get_feature_extractor()
						lowerCamelCase_							:	Dict							     =							self.get_tokenizer()

						lowerCamelCase_							:	Tuple							     =							MusicgenProcessor(tokenizer=__lowercase				, feature_extractor=__lowercase      )

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





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Tuple							     =							self.get_feature_extractor(padding_side='left'      )
						lowerCamelCase_							:	List[str]							     =							self.get_tokenizer()

						lowerCamelCase_							:	List[Any]							     =							MusicgenProcessor(tokenizer=__lowercase				, feature_extractor=__lowercase      )

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

						lowerCamelCase_							:	List[str]							     =							np.asarray(floats_list((3, 20)      )      )[:, None, :]
						lowerCamelCase_							:	List[str]							     =							processor.batch_decode(__lowercase				, padding_mask=__lowercase      )

						self.assertIsInstance(__lowercase				, __lowercase      )

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






# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/
