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    :  Dict										= random.Random()

def   a_					(_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   str=1.0			,	_lowerCAmelCase  :   List[Any]=None			,	_lowerCAmelCase  :   Tuple=None       )->				Any:
    if rng is None:
        snake_case:						Dict         =   global_rng

    snake_case:						List[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 				lowerCamelCase			(							unittest.TestCase					):






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   'facebook/musicgen-small'
          snake_case:						Union[str, Any]         =   tempfile.mkdtemp()






      def 			lowerCAmelCase_   (      self  ,		**__lowerCamelCase						)     ->				str:


          '''simple docstring'''







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






      def 			lowerCAmelCase_   (      self  ,		**__lowerCamelCase						)     ->				Union[str, Any]:


          '''simple docstring'''







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






      def 			lowerCAmelCase_   (      self						)     ->				Any:


          '''simple docstring'''







          shutil.rmtree(self.tmpdirname						)






      def 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						Optional[int]         =   self.get_tokenizer()
          snake_case:						List[Any]         =   self.get_feature_extractor()

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

          processor.save_pretrained(self.tmpdirname						)
          snake_case:						Union[str, 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 			lowerCAmelCase_   (      self						)     ->				List[Any]:


          '''simple docstring'''







          snake_case:						Dict         =   MusicgenProcessor(tokenizer=self.get_tokenizer()  ,		feature_extractor=self.get_feature_extractor()						)
          processor.save_pretrained(self.tmpdirname						)

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

          snake_case:						List[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 			lowerCAmelCase_   (      self						)     ->				str:


          '''simple docstring'''







          snake_case:						Any         =   self.get_feature_extractor()
          snake_case:						List[str]         =   self.get_tokenizer()

          snake_case:						List[Any]         =   MusicgenProcessor(tokenizer=lowerCAmelCase__  ,		feature_extractor=lowerCAmelCase__						)

          snake_case:						Dict         =   floats_list((3, 10_00)						)

          snake_case:						Tuple         =   feature_extractor(lowerCAmelCase__  ,		return_tensors="""np"""						)
          snake_case:						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 			lowerCAmelCase_   (      self						)     ->				Dict:


          '''simple docstring'''







          snake_case:						Tuple         =   self.get_feature_extractor()
          snake_case:						Union[str, Any]         =   self.get_tokenizer()

          snake_case:						str         =   MusicgenProcessor(tokenizer=lowerCAmelCase__  ,		feature_extractor=lowerCAmelCase__						)

          snake_case:						str         =   'This is a test string'

          snake_case:						Optional[int]         =   processor(text=lowerCAmelCase__						)

          snake_case:						Optional[Any]         =   tokenizer(lowerCAmelCase__						)

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






      def 			lowerCAmelCase_   (      self						)     ->				Optional[int]:


          '''simple docstring'''







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

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

          snake_case:						Optional[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          snake_case:						Dict         =   processor.batch_decode(sequences=lowerCAmelCase__						)
          snake_case:						Dict         =   tokenizer.batch_decode(lowerCAmelCase__						)

          self.assertListEqual(lowerCAmelCase__  ,		lowerCAmelCase__						)






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


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   self.get_feature_extractor()
          snake_case:						Any         =   self.get_tokenizer()

          snake_case:						Optional[int]         =   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						)     ->				List[Any]:


          '''simple docstring'''







          snake_case:						Union[str, Any]         =   self.get_feature_extractor(padding_side="""left"""						)
          snake_case:						Tuple         =   self.get_tokenizer()

          snake_case:						str         =   MusicgenProcessor(tokenizer=lowerCAmelCase__  ,		feature_extractor=lowerCAmelCase__						)

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

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






def   snake_case_	(    snake_case	)    ->							str:
       lowercase__:  Optional[Any]         =  1
       lowercase__:  Optional[Any]         =  0
       lowercase__:  int         =  1
       lowercase__:  Any         =  1
       lowercase__:  Optional[Any]         =  3
       lowercase__:  List[str]         =  3
       lowercase__:  Optional[Any]         =  limit
       lowercase__:  List[Any]         =  0

       lowercase__:  int         =  ''
       while counter != decimal + 1:
              if 4 * q + r - t < n * t:
                     result += str(snake_case	)
                     if counter == 0:
                            result += "."

                     if decimal == counter:
                            break

                     counter += 1
                     lowercase__:  List[Any]         =  10 * (r - n * t)
                     lowercase__:  int         =  ((10 * (3 * q + r)) // t) - 10 * n
                     q *= 10
                     lowercase__:  Any         =  nr
              else:
                     lowercase__:  List[str]         =  (2 * q + r) * l
                     lowercase__:  Tuple         =  (q * (7 * k) + 2 + (r * l)) // (t * l)
                     q *= k
                     t *= l
                     l += 2
                     k += 1
                     lowercase__:  Dict         =  nn
                     lowercase__:  Optional[int]         =  nr
       return result




def   snake_case_	(    )    ->							None:
       print(f'{calculate_pi(50	) = }'	)
       import doctest

       doctest.testmod()


if __name__ == "__main__":
 main()


