




"""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


SCREAMING_SNAKE_CASE							=			random.Random()







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_=1.0     ,					lowercase_=None     ,					lowercase_=None		)		->						Any:
 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       UpperCAmelCase_    (		unittest.TestCase				):




    def 				__magic_name__				(       self		:			Tuple ) ->				Dict:




     '''simple docstring'''

     A__			=							'''facebook/musicgen-small'''
     A__			=							tempfile.mkdtemp()




    def 				__magic_name__				(       self		:			int      ,					**snake_case_		:			str ) ->				Optional[Any]:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			int      ,					**snake_case_		:			Union[str, Any] ) ->				Tuple:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			Dict ) ->				Union[str, Any]:




     '''simple docstring'''

     shutil.rmtree(self.tmpdirname )




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.get_tokenizer()
     A__			=							self.get_feature_extractor()

     A__			=							MusicgenProcessor(tokenizer=__lowercase      ,					feature_extractor=__lowercase )

     processor.save_pretrained(self.tmpdirname )
     A__			=							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 				__magic_name__				(       self		:			Dict ) ->				Union[str, Any]:




     '''simple docstring'''

     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=__lowercase      ,					padding_value=1.0 )

     A__			=							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 				__magic_name__				(       self		:			int ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							self.get_feature_extractor()
     A__			=							self.get_tokenizer()

     A__			=							MusicgenProcessor(tokenizer=__lowercase      ,					feature_extractor=__lowercase )

     A__			=							floats_list((3, 1_000) )

     A__			=							feature_extractor(__lowercase      ,					return_tensors="np" )
     A__			=							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 				__magic_name__				(       self		:			Tuple ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.get_feature_extractor()
     A__			=							self.get_tokenizer()

     A__			=							MusicgenProcessor(tokenizer=__lowercase      ,					feature_extractor=__lowercase )

     A__			=							'''This is a test string'''

     A__			=							processor(text=__lowercase )

     A__			=							tokenizer(__lowercase )

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




    def 				__magic_name__				(       self		:			Any ) ->				Dict:




     '''simple docstring'''

     A__			=							self.get_feature_extractor()
     A__			=							self.get_tokenizer()

     A__			=							MusicgenProcessor(tokenizer=__lowercase      ,					feature_extractor=__lowercase )

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

     A__			=							processor.batch_decode(sequences=__lowercase )
     A__			=							tokenizer.batch_decode(__lowercase )

     self.assertListEqual(__lowercase      ,					__lowercase )




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.get_feature_extractor()
     A__			=							self.get_tokenizer()

     A__			=							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 				__magic_name__				(       self		:			Tuple ) ->				Union[str, Any]:




     '''simple docstring'''

     A__			=							self.get_feature_extractor(padding_side="left" )
     A__			=							self.get_tokenizer()

     A__			=							MusicgenProcessor(tokenizer=__lowercase      ,					feature_extractor=__lowercase )

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

     A__			=							np.asarray(floats_list((3, 20) ) )[:, None, :]
     A__			=							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, 10) )
     self.assertTrue(decoded_audios[1].shape == (1, 15) )
     self.assertTrue(decoded_audios[2].shape == (1, 20) )












def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		assert b != 0, "This cannot accept modulo that is == 0"
		if n == 0:
				return 1

		if n % 2 == 1:
				return (bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n - 1				,							SCREAMING_SNAKE_CASE_		) * a) % b

		lowerCamelCase :			Tuple    		=bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n / 2				,							SCREAMING_SNAKE_CASE_		)
		return (r * r) % b


if __name__ == "__main__":
				try:
								snake_case_				=      int(input('''Enter Base : ''').strip())
								snake_case_				=      int(input('''Enter Power : ''').strip())
								snake_case_				=      int(input('''Enter Modulo : ''').strip())
				except ValueError:
								print('''Invalid literal for integer''')

				print(bin_exp_mod(BASE, POWER, MODULO))

