'''simple docstring'''


import numpy as np


def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = np.array(lowercase_  )
     UpperCAmelCase_				:  Optional[int]  = np.array(lowercase_  )

     UpperCAmelCase_				:  List[str]  = abs(predict - actual  )
     UpperCAmelCase_				:  List[Any]  = difference.mean()

     return score


def    _lowerCamelCase       ( lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				Union[str, Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = np.array(lowercase_  )
     UpperCAmelCase_				:  List[Any]  = np.array(lowercase_  )

     UpperCAmelCase_				:  Optional[Any]  = predict - actual
     UpperCAmelCase_				:  Any  = np.square(lowercase_  )

     UpperCAmelCase_				:  List[str]  = square_diff.mean()
     return score


def    _lowerCamelCase       ( lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = np.array(lowercase_  )
     UpperCAmelCase_				:  Union[str, Any]  = np.array(lowercase_  )

     UpperCAmelCase_				:  Optional[int]  = predict - actual
     UpperCAmelCase_				:  Tuple  = np.square(lowercase_  )
     UpperCAmelCase_				:  Optional[Any]  = square_diff.mean()
     UpperCAmelCase_				:  Optional[Any]  = np.sqrt(lowercase_  )
     return score


def    _lowerCamelCase       ( lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				List[str]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = np.array(lowercase_  )
     UpperCAmelCase_				:  Optional[Any]  = np.array(lowercase_  )

     UpperCAmelCase_				:  List[str]  = np.log(predict + 1  )
     UpperCAmelCase_				:  Any  = np.log(actual + 1  )

     UpperCAmelCase_				:  List[str]  = log_predict - log_actual
     UpperCAmelCase_				:  Optional[int]  = np.square(lowercase_  )
     UpperCAmelCase_				:  Tuple  = square_diff.mean()

     UpperCAmelCase_				:  List[Any]  = np.sqrt(lowercase_  )

     return score


def    _lowerCamelCase       ( lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				Any  ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = np.array(lowercase_  )
     UpperCAmelCase_				:  Optional[Any]  = np.array(lowercase_  )

     UpperCAmelCase_				:  List[str]  = predict - actual
     UpperCAmelCase_				:  Tuple  = np.sum(lowercase_  ) / len(lowercase_  )
     UpperCAmelCase_				:  Optional[int]  = np.sum(lowercase_  ) / len(lowercase_  )
     # print(numerator, denumerator)
     UpperCAmelCase_				:  Any  = float(lowercase_  ) / denumerator * 100

     return score





def    _lowerCamelCase       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     return np.mean(np.array(lowercase_  ) == np.array(lowercase_  )  )









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_					  =   random.Random()



def   UpperCamelCase(   lowercase_     ,   lowercase_=1.0     ,   lowercase_=None     ,   lowercase_=None				)      ->							List[str]:





  '''simple docstring'''





  if rng is None:
    snake_case_					= global_rng

  snake_case_					= []
  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 					__lowerCamelCase							(      unittest.TestCase      ):







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= """facebook/musicgen-small"""
        snake_case_					= tempfile.mkdtemp()







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			int:
        return TaTokenizer.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    **lowerCamelCase  )		->			Dict:
        return EncodecFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        shutil.rmtree(self.tmpdirname  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        snake_case_					= self.get_tokenizer()
        snake_case_					= self.get_feature_extractor()

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

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

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

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







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= MusicgenProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()  )
        processor.save_pretrained(self.tmpdirname  )

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

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

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

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







      def       lowerCAmelCase_       (	self  )		->			Tuple:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

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

        snake_case_					= floats_list((3, 1000)  )

        snake_case_					= feature_extractor(lowerCamelCase				,    return_tensors="""np"""  )
        snake_case_					= processor(lowerCamelCase				,    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       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

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

        snake_case_					= """This is a test string"""

        snake_case_					= processor(text=lowerCamelCase  )

        snake_case_					= tokenizer(lowerCamelCase  )

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







      def       lowerCAmelCase_       (	self  )		->			int:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

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

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

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

        self.assertListEqual(lowerCamelCase				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        snake_case_					= self.get_feature_extractor()
        snake_case_					= self.get_tokenizer()

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

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







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        snake_case_					= self.get_feature_extractor(padding_side="""left"""  )
        snake_case_					= self.get_tokenizer()

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

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

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

        self.assertIsInstance(lowerCamelCase				,    lowerCamelCase  )

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