

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


A__										=      random.Random()





def 				__a							(       __A			,							__A=1.0			,							__A=None			,							__A=None						):
    if rng is None:
        SCREAMING_SNAKE_CASE__         =    global_rng

    SCREAMING_SNAKE_CASE__         =    []
    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      ):


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					int						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    "facebook/musicgen-small"
      SCREAMING_SNAKE_CASE__         =    tempfile.mkdtemp()



  def   _UpperCamelCase     (						self     :					Optional[int]  ,	**A_     :					List[str]						)       ->			Union[str, Any]:
      return TaTokenizer.from_pretrained(self.checkpoint  ,	**A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	**A_     :					Optional[int]						)       ->			Dict:
      return EncodecFeatureExtractor.from_pretrained(self.checkpoint  ,	**A_						)



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



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

      SCREAMING_SNAKE_CASE__         =    MusicgenProcessor(tokenizer=A_  ,	feature_extractor=A_						)

      processor.save_pretrained(self.tmpdirname						)
      SCREAMING_SNAKE_CASE__         =    MusicgenProcessor.from_pretrained(self.tmpdirname						)

      self.assertEqual(processor.tokenizer.get_vocab()  ,	tokenizer.get_vocab()						)
      self.assertIsInstance(processor.tokenizer  ,	A_						)

      self.assertEqual(processor.feature_extractor.to_json_string()  ,	feature_extractor.to_json_string()						)
      self.assertIsInstance(processor.feature_extractor  ,	A_						)



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

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

      SCREAMING_SNAKE_CASE__         =    MusicgenProcessor.from_pretrained(
          self.tmpdirname  ,	bos_token="(BOS)"  ,	eos_token="(EOS)"  ,	do_normalize=A_  ,	padding_value=1.0						)

      self.assertEqual(processor.tokenizer.get_vocab()  ,	tokenizer_add_kwargs.get_vocab()						)
      self.assertIsInstance(processor.tokenizer  ,	A_						)

      self.assertEqual(processor.feature_extractor.to_json_string()  ,	feature_extractor_add_kwargs.to_json_string()						)
      self.assertIsInstance(processor.feature_extractor  ,	A_						)



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

      SCREAMING_SNAKE_CASE__         =    MusicgenProcessor(tokenizer=A_  ,	feature_extractor=A_						)

      SCREAMING_SNAKE_CASE__         =    floats_list((3, 1000)						)

      SCREAMING_SNAKE_CASE__         =    feature_extractor(A_  ,	return_tensors="np"						)
      SCREAMING_SNAKE_CASE__         =    processor(A_  ,	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     :					str						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__         =    self.get_tokenizer()

      SCREAMING_SNAKE_CASE__         =    MusicgenProcessor(tokenizer=A_  ,	feature_extractor=A_						)

      SCREAMING_SNAKE_CASE__         =    "This is a test string"

      SCREAMING_SNAKE_CASE__         =    processor(text=A_						)

      SCREAMING_SNAKE_CASE__         =    tokenizer(A_						)

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



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

      SCREAMING_SNAKE_CASE__         =    MusicgenProcessor(tokenizer=A_  ,	feature_extractor=A_						)

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

      SCREAMING_SNAKE_CASE__         =    processor.batch_decode(sequences=A_						)
      SCREAMING_SNAKE_CASE__         =    tokenizer.batch_decode(A_						)

      self.assertListEqual(A_  ,	A_						)



  def   _UpperCamelCase     (						self     :					int						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__         =    self.get_tokenizer()

      SCREAMING_SNAKE_CASE__         =    MusicgenProcessor(tokenizer=A_  ,	feature_extractor=A_						)

      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     :					Optional[Any]						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    self.get_feature_extractor(padding_side="left"						)
      SCREAMING_SNAKE_CASE__         =    self.get_tokenizer()

      SCREAMING_SNAKE_CASE__         =    MusicgenProcessor(tokenizer=A_  ,	feature_extractor=A_						)

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

      SCREAMING_SNAKE_CASE__         =    np.asarray(floats_list((3, 20)						)						)[:, None, :]
      SCREAMING_SNAKE_CASE__         =    processor.batch_decode(A_  ,	padding_mask=A_						)

      self.assertIsInstance(A_  ,	A_						)

      for audio in decoded_audios:
          self.assertIsInstance(A_  ,	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)						)





from __future__ import annotations





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    {1}
    SCREAMING_SNAKE_CASE__         =    [1]
    for _ in range(2			,							depth + 1						):
        SCREAMING_SNAKE_CASE__         =    [*previous_coefficients, 0]
        SCREAMING_SNAKE_CASE__         =    [0, *previous_coefficients]
        SCREAMING_SNAKE_CASE__         =    []
        for x, y in zip(__A			,							__A						):
            coefficients.add(x + y						)
            previous_coefficients.append(x + y						)
    return coefficients





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    set()
    for number in unique_coefficients:
        SCREAMING_SNAKE_CASE__         =    2
        SCREAMING_SNAKE_CASE__         =    number
        while divisor**2 <= copy_number:
            SCREAMING_SNAKE_CASE__         =    0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(__A						)
                break
            divisor += 1

    return unique_coefficients.difference(__A						)





def 				__a							(       __A = 51						):
    SCREAMING_SNAKE_CASE__         =    get_pascal_triangle_unique_coefficients(__A						)
    SCREAMING_SNAKE_CASE__         =    get_squarefrees(__A						)
    return sum(__A						)


if __name__ == "__main__":
  print(f'''{solution() = }''')


