





"""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     lowercase__			(    lowercase_						,lowercase_=1.0						,lowercase_=None						,lowercase_=None	)    ->						Dict:




      """simple docstring"""

      if rng is None:
            _UpperCamelCase					:		str  =				global_rng

      _UpperCamelCase					:		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      ):







  '''simple docstring'''


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Optional[int]:
        _UpperCamelCase					:		Union[str, Any]  =				'''facebook/musicgen-small'''
        _UpperCamelCase					:		Dict  =				tempfile.mkdtemp()

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      **__a      :				Dict		)						-> Optional[Any]:
        return TaTokenizer.from_pretrained(self.checkpoint    ,      **lowerCAmelCase__		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      **__a      :				Dict		)						-> int:
        return EncodecFeatureExtractor.from_pretrained(self.checkpoint    ,      **lowerCAmelCase__		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> List[str]:
        shutil.rmtree(self.tmpdirname		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> List[str]:
        _UpperCamelCase					:		Union[str, Any]  =				self.get_tokenizer()
        _UpperCamelCase					:		Tuple  =				self.get_feature_extractor()

        _UpperCamelCase					:		Any  =				MusicgenProcessor(tokenizer=lowerCAmelCase__    ,      feature_extractor=lowerCAmelCase__		)

        processor.save_pretrained(self.tmpdirname		)
        _UpperCamelCase					:		Any  =				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       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> Union[str, Any]:
        _UpperCamelCase					:		Optional[int]  =				MusicgenProcessor(tokenizer=self.get_tokenizer()    ,      feature_extractor=self.get_feature_extractor()		)
        processor.save_pretrained(self.tmpdirname		)

        _UpperCamelCase					:		Dict  =				self.get_tokenizer(bos_token="(BOS)"    ,      eos_token="(EOS)"		)
        _UpperCamelCase					:		Optional[Any]  =				self.get_feature_extractor(do_normalize=lowerCAmelCase__    ,      padding_value=1.0		)

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

        _UpperCamelCase					:		Dict  =				MusicgenProcessor(tokenizer=lowerCAmelCase__    ,      feature_extractor=lowerCAmelCase__		)

        _UpperCamelCase					:		Optional[Any]  =				floats_list((3, 1000)		)

        _UpperCamelCase					:		List[Any]  =				feature_extractor(lowerCAmelCase__    ,      return_tensors="np"		)
        _UpperCamelCase					:		Optional[Any]  =				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       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> List[Any]:
        _UpperCamelCase					:		Union[str, Any]  =				self.get_feature_extractor()
        _UpperCamelCase					:		Tuple  =				self.get_tokenizer()

        _UpperCamelCase					:		Union[str, Any]  =				MusicgenProcessor(tokenizer=lowerCAmelCase__    ,      feature_extractor=lowerCAmelCase__		)

        _UpperCamelCase					:		Union[str, Any]  =				'''This is a test string'''

        _UpperCamelCase					:		List[Any]  =				processor(text=lowerCAmelCase__		)

        _UpperCamelCase					:		int  =				tokenizer(lowerCAmelCase__		)

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

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> str:
        _UpperCamelCase					:		int  =				self.get_feature_extractor()
        _UpperCamelCase					:		int  =				self.get_tokenizer()

        _UpperCamelCase					:		Optional[Any]  =				MusicgenProcessor(tokenizer=lowerCAmelCase__    ,      feature_extractor=lowerCAmelCase__		)

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

        _UpperCamelCase					:		int  =				processor.batch_decode(sequences=lowerCAmelCase__		)
        _UpperCamelCase					:		Dict  =				tokenizer.batch_decode(lowerCAmelCase__		)

        self.assertListEqual(lowerCAmelCase__    ,      lowerCAmelCase__		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> List[Any]:
        _UpperCamelCase					:		Tuple  =				self.get_feature_extractor()
        _UpperCamelCase					:		List[Any]  =				self.get_tokenizer()

        _UpperCamelCase					:		Any  =				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       __SCREAMING_SNAKE_CASE (						self      :				str		)						-> Dict:
        _UpperCamelCase					:		Optional[int]  =				self.get_feature_extractor(padding_side="left"		)
        _UpperCamelCase					:		str  =				self.get_tokenizer()

        _UpperCamelCase					:		Union[str, Any]  =				MusicgenProcessor(tokenizer=lowerCAmelCase__    ,      feature_extractor=lowerCAmelCase__		)

        _UpperCamelCase					:		Optional[Any]  =				[floats_list((1, x)		)[0] for x in range(5    ,      20    ,      5		)]
        _UpperCamelCase					:		Optional[Any]  =				processor(lowerCAmelCase__		).padding_mask

        _UpperCamelCase					:		Union[str, Any]  =				np.asarray(floats_list((3, 20)		)		)[:, None, :]
        _UpperCamelCase					:		List[Any]  =				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)		)







"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



