





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


snake_case_				=      random.Random()


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=1.0				,							SCREAMING_SNAKE_CASE_=None				,							SCREAMING_SNAKE_CASE_=None		) ->  Any:
		if rng is None:
				lowerCamelCase :			List[Any]    		=global_rng

		lowerCamelCase :			Tuple    		=[]
		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 							snake_case_		(      unittest.TestCase):







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			Optional[Any]    		='''facebook/musicgen-small'''
				lowerCamelCase :			Dict    		=tempfile.mkdtemp()







		def       __lowercase (		self      ,  **__lowercase )					-> Optional[Any]:
				return TaTokenizer.from_pretrained(self.checkpoint      ,  **__lowercase )







		def       __lowercase (		self      ,  **__lowercase )					-> Tuple:
				return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,  **__lowercase )







		def       __lowercase (		self )					-> Union[str, Any]:
				shutil.rmtree(self.tmpdirname )







		def       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			Dict    		=self.get_tokenizer()
				lowerCamelCase :			int    		=self.get_feature_extractor()

				lowerCamelCase :			Dict    		=MusicgenProcessor(tokenizer=__lowercase      ,  feature_extractor=__lowercase )

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

				lowerCamelCase :			int    		=self.get_tokenizer(bos_token='''(BOS)'''      ,  eos_token='''(EOS)''' )
				lowerCamelCase :			int    		=self.get_feature_extractor(do_normalize=__lowercase      ,  padding_value=1.0 )

				lowerCamelCase :			Dict    		=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 )					-> Optional[int]:
				lowerCamelCase :			Any    		=self.get_feature_extractor()
				lowerCamelCase :			List[str]    		=self.get_tokenizer()

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

				lowerCamelCase :			Union[str, Any]    		=floats_list((3, 1_0_0_0) )

				lowerCamelCase :			Union[str, Any]    		=feature_extractor(__lowercase      ,  return_tensors='''np''' )
				lowerCamelCase :			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 )					-> Optional[Any]:
				lowerCamelCase :			Optional[Any]    		=self.get_feature_extractor()
				lowerCamelCase :			Any    		=self.get_tokenizer()

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

				lowerCamelCase :			Tuple    		='''This is a test string'''

				lowerCamelCase :			str    		=processor(text=__lowercase )

				lowerCamelCase :			Union[str, Any]    		=tokenizer(__lowercase )

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







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			str    		=self.get_feature_extractor()
				lowerCamelCase :			Tuple    		=self.get_tokenizer()

				lowerCamelCase :			str    		=MusicgenProcessor(tokenizer=__lowercase      ,  feature_extractor=__lowercase )

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

				lowerCamelCase :			Any    		=processor.batch_decode(sequences=__lowercase )
				lowerCamelCase :			Optional[int]    		=tokenizer.batch_decode(__lowercase )

				self.assertListEqual(__lowercase      ,  __lowercase )







		def       __lowercase (		self )					-> int:
				lowerCamelCase :			Union[str, Any]    		=self.get_feature_extractor()
				lowerCamelCase :			int    		=self.get_tokenizer()

				lowerCamelCase :			Any    		=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 )					-> Union[str, Any]:
				lowerCamelCase :			str    		=self.get_feature_extractor(padding_side='''left''' )
				lowerCamelCase :			int    		=self.get_tokenizer()

				lowerCamelCase :			List[str]    		=MusicgenProcessor(tokenizer=__lowercase      ,  feature_extractor=__lowercase )

				lowerCamelCase :			str    		=[floats_list((1, x) )[0] for x in range(5      ,  2_0      ,  5 )]
				lowerCamelCase :			Optional[int]    		=processor(__lowercase ).padding_mask

				lowerCamelCase :			str    		=np.asarray(floats_list((3, 2_0) ) )[:, None, :]
				lowerCamelCase :			int    		=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, 1_0) )
				self.assertTrue(decoded_audios[1].shape == (1, 1_5) )
				self.assertTrue(decoded_audios[2].shape == (1, 2_0) )








import pytest

from datasets.utils.version import Version


@pytest.mark.parametrize(
    '''other, expected_equality'''				,							[
        (Version('''1.0.0'''		), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''		), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]				,							)
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			str    		=Version('''1.0.0'''		)
		assert (version == other) is expected_equality
		assert (version != other) is not expected_equality
		assert (hash(SCREAMING_SNAKE_CASE_		) == hash(SCREAMING_SNAKE_CASE_		)) is expected_equality

