'''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_			:			Dict    	=   random.Random()


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Dict=1.0			,		__lowerCamelCase      :    List[str]=None			,		__lowerCamelCase      :    Tuple=None					)						->				List[str]:
      '''simple docstring'''


      if rng is None:
            lowercase_     			=   global_rng

      lowercase_     			=   []
      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 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Optional[Any]:
         lowercase_     			=   "facebook/musicgen-small"
         lowercase_     			=   tempfile.mkdtemp()


   def __UpperCAmelCase							( self					:						List[str] ,		**__lowerCAmelCase					:						Tuple)   ->       str:
         return TaTokenizer.from_pretrained(self.checkpoint ,		**__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		**__lowerCAmelCase					:						Tuple)   ->       Dict:
         return EncodecFeatureExtractor.from_pretrained(self.checkpoint ,		**__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Tuple)   ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname)


   def __UpperCAmelCase							( self					:						str)   ->       Dict:
         lowercase_     			=   self.get_tokenizer()
         lowercase_     			=   self.get_feature_extractor()

         lowercase_     			=   MusicgenProcessor(tokenizer=__lowerCAmelCase ,		feature_extractor=__lowerCAmelCase)

         processor.save_pretrained(self.tmpdirname)
         lowercase_     			=   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 __UpperCAmelCase							( self					:						str)   ->       int:
         lowercase_     			=   MusicgenProcessor(tokenizer=self.get_tokenizer() ,		feature_extractor=self.get_feature_extractor())
         processor.save_pretrained(self.tmpdirname)

         lowercase_     			=   self.get_tokenizer(bos_token="(BOS)" ,		eos_token="(EOS)")
         lowercase_     			=   self.get_feature_extractor(do_normalize=__lowerCAmelCase ,		padding_value=1.0)

         lowercase_     			=   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 __UpperCAmelCase							( self					:						Tuple)   ->       List[Any]:
         lowercase_     			=   self.get_feature_extractor()
         lowercase_     			=   self.get_tokenizer()

         lowercase_     			=   MusicgenProcessor(tokenizer=__lowerCAmelCase ,		feature_extractor=__lowerCAmelCase)

         lowercase_     			=   floats_list((3, 1000))

         lowercase_     			=   feature_extractor(__lowerCAmelCase ,		return_tensors="np")
         lowercase_     			=   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 __UpperCAmelCase							( self					:						int)   ->       Optional[int]:
         lowercase_     			=   self.get_feature_extractor()
         lowercase_     			=   self.get_tokenizer()

         lowercase_     			=   MusicgenProcessor(tokenizer=__lowerCAmelCase ,		feature_extractor=__lowerCAmelCase)

         lowercase_     			=   "This is a test string"

         lowercase_     			=   processor(text=__lowerCAmelCase)

         lowercase_     			=   tokenizer(__lowerCAmelCase)

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


   def __UpperCAmelCase							( self					:						str)   ->       Optional[int]:
         lowercase_     			=   self.get_feature_extractor()
         lowercase_     			=   self.get_tokenizer()

         lowercase_     			=   MusicgenProcessor(tokenizer=__lowerCAmelCase ,		feature_extractor=__lowerCAmelCase)

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

         lowercase_     			=   processor.batch_decode(sequences=__lowerCAmelCase)
         lowercase_     			=   tokenizer.batch_decode(__lowerCAmelCase)

         self.assertListEqual(__lowerCAmelCase ,		__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Union[str, Any]:
         lowercase_     			=   self.get_feature_extractor()
         lowercase_     			=   self.get_tokenizer()

         lowercase_     			=   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 __UpperCAmelCase							( self					:						int)   ->       str:
         lowercase_     			=   self.get_feature_extractor(padding_side="left")
         lowercase_     			=   self.get_tokenizer()

         lowercase_     			=   MusicgenProcessor(tokenizer=__lowerCAmelCase ,		feature_extractor=__lowerCAmelCase)

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

         lowercase_     			=   np.asarray(floats_list((3, 20)))[:, None, :]
         lowercase_     			=   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'''

import timeit





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Any)   ->       Optional[int]:
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):  # Initializes a matrix identical to the one provided.
               lowercase_     			=   arg
               lowercase_     			=   len(__lowerCAmelCase)
         else:  # Initializes a square matrix of the given size and set values to zero.
               lowercase_     			=   arg
               lowercase_     			=   [[0 for _ in range(self.n)] for _ in range(self.n)]


   def __mul__( self					:						int ,		__lowerCAmelCase					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   Matrix(self.n)
         for i in range(self.n):
               for j in range(self.n):
                     for k in range(self.n):
                           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
         return matrix




def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Dict					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   Matrix([[1, 0], [0, 1]]					)
      while b > 0:
            if b & 1:
                  matrix *= a
            a *= a
            b >>= 1
      return matrix


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    List[Any]					)						->				Dict:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa
      lowercase_     			=   Matrix([[1, 1], [1, 0]]					)
      lowercase_     			=   modular_exponentiation(__lowerCamelCase			,		n - 2					)
      return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def __a			(						__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    Union[str, Any]			,		__lowerCamelCase      :    List[str]					)						->				Optional[Any]:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa

      lowercase_     			=   fa
      lowercase_     			=   fa
      n -= 2

      while n > 0:
            lowercase_			,				lowercase_     			=   fn_a + fn_a, fn_a
            n -= 1

      return fn_a


def __a			(						)						->				str:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
      lowercase_     			=   "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print("With matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
      lowercase_     			=   "simple_fibonacci(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print(
          "Without matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Tuple:
      '''simple docstring'''


      matrix_exponentiation_time()
      simple_fibonacci_time()


if __name__ == "__main__":
       main()






