


'''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  =				random.Random()

def 				lowerCamelCase_   (   __UpperCamelCase				:					str       ,						__UpperCamelCase				:					Optional[int]=1.0       ,						__UpperCamelCase				:					str=None       ,						__UpperCamelCase				:					int=None		)				->  Optional[int]:







     """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 					lowerCAmelCase_     (    unittest.TestCase  ):






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






       def      UpperCamelCase	(   self ,    **_UpperCamelCase      )->			Any:
            return TaTokenizer.from_pretrained(self.checkpoint ,    **_UpperCamelCase      )






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






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






       def      UpperCamelCase	(   self      )->			Optional[int]:
            _A				=       self.get_tokenizer()
            _A				=       self.get_feature_extractor()

            _A				=       MusicgenProcessor(tokenizer=_UpperCamelCase ,    feature_extractor=_UpperCamelCase      )

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

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

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






       def      UpperCamelCase	(   self      )->			Tuple:
            _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=_UpperCamelCase ,    padding_value=1.0      )

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

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

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






       def      UpperCamelCase	(   self      )->			Any:
            _A				=       self.get_feature_extractor()
            _A				=       self.get_tokenizer()

            _A				=       MusicgenProcessor(tokenizer=_UpperCamelCase ,    feature_extractor=_UpperCamelCase      )

            _A				=       floats_list((3, 1000)      )

            _A				=       feature_extractor(_UpperCamelCase ,    return_tensors='np'      )
            _A				=       processor(_UpperCamelCase ,    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      )->			int:
            _A				=       self.get_feature_extractor()
            _A				=       self.get_tokenizer()

            _A				=       MusicgenProcessor(tokenizer=_UpperCamelCase ,    feature_extractor=_UpperCamelCase      )

            _A				=       'This is a test string'

            _A				=       processor(text=_UpperCamelCase      )

            _A				=       tokenizer(_UpperCamelCase      )

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






       def      UpperCamelCase	(   self      )->			int:
            _A				=       self.get_feature_extractor()
            _A				=       self.get_tokenizer()

            _A				=       MusicgenProcessor(tokenizer=_UpperCamelCase ,    feature_extractor=_UpperCamelCase      )

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

            _A				=       processor.batch_decode(sequences=_UpperCamelCase      )
            _A				=       tokenizer.batch_decode(_UpperCamelCase      )

            self.assertListEqual(_UpperCamelCase ,    _UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			Any:
            _A				=       self.get_feature_extractor()
            _A				=       self.get_tokenizer()

            _A				=       MusicgenProcessor(tokenizer=_UpperCamelCase ,    feature_extractor=_UpperCamelCase      )

            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:
            _A				=       self.get_feature_extractor(padding_side='left'      )
            _A				=       self.get_tokenizer()

            _A				=       MusicgenProcessor(tokenizer=_UpperCamelCase ,    feature_extractor=_UpperCamelCase      )

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

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

            self.assertIsInstance(_UpperCamelCase ,    _UpperCamelCase      )

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





'''simple docstring'''
from __future__ import annotations

def 				lowerCamelCase_   (   __UpperCamelCase				:					list[int | float]		)				->  int | float:







     """simple docstring"""
     if len(__UpperCamelCase		) == 0:
          raise ValueError('find_max() arg is an empty sequence'		)
     _A				=       nums[0]
     for x in nums:
          if x > max_num:
               _A				=       x
     return max_num


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)





