





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							:      int						=	random.Random()

def      __snake_case						(					_lowerCAmelCase : List[Any]				,				_lowerCAmelCase : Any=1.0				,				_lowerCAmelCase : List[str]=None				,				_lowerCAmelCase : Any=None	) ->						Dict:
   if rng is None:
      A_					:				Optional[int]          =			global_rng

   A_					:				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 					__magic_name__  (							unittest.TestCase							):





  """simple docstring"""



  def 						SCREAMING_SNAKE_CASE		(							self       :Any  ):





     '''simple docstring'''




     A_					:				Any          =			"facebook/musicgen-small"
     A_					:				Optional[int]          =			tempfile.mkdtemp()



  def 						SCREAMING_SNAKE_CASE		(							self       :int			,      **snake_case       :List[Any]  ):





     '''simple docstring'''




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



  def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      **snake_case       :Optional[Any]  ):





     '''simple docstring'''




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



  def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





     '''simple docstring'''




     shutil.rmtree(self.tmpdirname  )



  def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





     '''simple docstring'''




     A_					:				Union[str, Any]          =			self.get_tokenizer()
     A_					:				Optional[int]          =			self.get_feature_extractor()

     A_					:				int          =			MusicgenProcessor(tokenizer=lowerCamelCase__			,      feature_extractor=lowerCamelCase__  )

     processor.save_pretrained(self.tmpdirname  )
     A_					:				List[str]          =			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       :int  ):





     '''simple docstring'''




     A_					:				List[str]          =			MusicgenProcessor(tokenizer=self.get_tokenizer()			,      feature_extractor=self.get_feature_extractor()  )
     processor.save_pretrained(self.tmpdirname  )

     A_					:				Any          =			self.get_tokenizer(bos_token="(BOS)"			,      eos_token="(EOS)"  )
     A_					:				str          =			self.get_feature_extractor(do_normalize=lowerCamelCase__			,      padding_value=1.0  )

     A_					:				Dict          =			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       :Optional[int]  ):





     '''simple docstring'''




     A_					:				Optional[Any]          =			self.get_feature_extractor()
     A_					:				str          =			self.get_tokenizer()

     A_					:				Dict          =			MusicgenProcessor(tokenizer=lowerCamelCase__			,      feature_extractor=lowerCamelCase__  )

     A_					:				Optional[Any]          =			floats_list((3, 1_000)  )

     A_					:				Tuple          =			feature_extractor(lowerCamelCase__			,      return_tensors="np"  )
     A_					:				Dict          =			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       :Optional[int]  ):





     '''simple docstring'''




     A_					:				Optional[int]          =			self.get_feature_extractor()
     A_					:				int          =			self.get_tokenizer()

     A_					:				int          =			MusicgenProcessor(tokenizer=lowerCamelCase__			,      feature_extractor=lowerCamelCase__  )

     A_					:				str          =			"This is a test string"

     A_					:				Union[str, Any]          =			processor(text=lowerCamelCase__  )

     A_					:				Any          =			tokenizer(lowerCamelCase__  )

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



  def 						SCREAMING_SNAKE_CASE		(							self       :Any  ):





     '''simple docstring'''




     A_					:				Optional[int]          =			self.get_feature_extractor()
     A_					:				List[str]          =			self.get_tokenizer()

     A_					:				Union[str, Any]          =			MusicgenProcessor(tokenizer=lowerCamelCase__			,      feature_extractor=lowerCamelCase__  )

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

     A_					:				Tuple          =			processor.batch_decode(sequences=lowerCamelCase__  )
     A_					:				List[Any]          =			tokenizer.batch_decode(lowerCamelCase__  )

     self.assertListEqual(lowerCamelCase__			,      lowerCamelCase__  )



  def 						SCREAMING_SNAKE_CASE		(							self       :Dict  ):





     '''simple docstring'''




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

     A_					:				Dict          =			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       :int  ):





     '''simple docstring'''




     A_					:				Tuple          =			self.get_feature_extractor(padding_side="left"  )
     A_					:				Tuple          =			self.get_tokenizer()

     A_					:				List[Any]          =			MusicgenProcessor(tokenizer=lowerCamelCase__			,      feature_extractor=lowerCamelCase__  )

     A_					:				Any          =			[floats_list((1, x)  )[0] for x in range(5			,      20			,      5  )]
     A_					:				str          =			processor(lowerCamelCase__  ).padding_mask

     A_					:				Optional[Any]          =			np.asarray(floats_list((3, 20)  )  )[:, None, :]
     A_					:				Dict          =			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)  )







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
