





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


_UpperCamelCase   :							List[Any]     =	random.Random()
def 				__UpperCamelCase  (		snake_case    ,	snake_case=1.0    ,	snake_case=None    ,	snake_case=None     )  ->						str:


   '''simple docstring'''







   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 						_lowerCAmelCase(						unittest.TestCase):

  """simple docstring"""







  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       '''facebook/musicgen-small'''
     __A				 =       tempfile.mkdtemp()

  def 		SCREAMING_SNAKE_CASE__	( self  ,    **UpperCAmelCase     )-> Dict:
     return TaTokenizer.from_pretrained(self.checkpoint  ,    **UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    **UpperCAmelCase     )-> str:
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint  ,    **UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     shutil.rmtree(self.tmpdirname     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       self.get_tokenizer()
     __A				 =       self.get_feature_extractor()

     __A				 =       MusicgenProcessor(tokenizer=UpperCAmelCase  ,    feature_extractor=UpperCAmelCase     )

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

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

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

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
     __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=UpperCAmelCase  ,    padding_value=1.0     )

     __A				 =       MusicgenProcessor.from_pretrained(
         self.tmpdirname  ,    bos_token='''(BOS)'''  ,    eos_token='''(EOS)'''  ,    do_normalize=UpperCAmelCase  ,    padding_value=1.0     )

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

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

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       self.get_feature_extractor()
     __A				 =       self.get_tokenizer()

     __A				 =       MusicgenProcessor(tokenizer=UpperCAmelCase  ,    feature_extractor=UpperCAmelCase     )

     __A				 =       floats_list((3, 10_00)     )

     __A				 =       feature_extractor(UpperCAmelCase  ,    return_tensors='''np'''     )
     __A				 =       processor(UpperCAmelCase  ,    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     )-> Tuple:
     __A				 =       self.get_feature_extractor()
     __A				 =       self.get_tokenizer()

     __A				 =       MusicgenProcessor(tokenizer=UpperCAmelCase  ,    feature_extractor=UpperCAmelCase     )

     __A				 =       '''This is a test string'''

     __A				 =       processor(text=UpperCAmelCase     )

     __A				 =       tokenizer(UpperCAmelCase     )

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

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     __A				 =       self.get_feature_extractor()
     __A				 =       self.get_tokenizer()

     __A				 =       MusicgenProcessor(tokenizer=UpperCAmelCase  ,    feature_extractor=UpperCAmelCase     )

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

     __A				 =       processor.batch_decode(sequences=UpperCAmelCase     )
     __A				 =       tokenizer.batch_decode(UpperCAmelCase     )

     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       self.get_feature_extractor()
     __A				 =       self.get_tokenizer()

     __A				 =       MusicgenProcessor(tokenizer=UpperCAmelCase  ,    feature_extractor=UpperCAmelCase     )

     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     )-> Tuple:
     __A				 =       self.get_feature_extractor(padding_side='''left'''     )
     __A				 =       self.get_tokenizer()

     __A				 =       MusicgenProcessor(tokenizer=UpperCAmelCase  ,    feature_extractor=UpperCAmelCase     )

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

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

     self.assertIsInstance(UpperCAmelCase  ,    UpperCAmelCase     )

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








class 						_lowerCAmelCase:

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase     )-> List[Any]:
     __A				 =       val
     # Number of nodes in left subtree
     __A				 =       0
     __A				 =       None
     __A				 =       None
     __A				 =       None





  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> List[Any]:
     assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

     if self.val < other.val:
        __A				 =       self.right
        __A				 =       None
        if self.right:
           __A				 =       other
        __A				 =       other
        __A				 =       self.left_tree_size * 2 + 1
        return self
     else:
        __A				 =       other.right
        __A				 =       None
        if other.right:
           __A				 =       self
        __A				 =       self
        __A				 =       other.left_tree_size * 2 + 1
        return other







class 						_lowerCAmelCase:

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase=None  ,    UpperCAmelCase=None  ,    UpperCAmelCase=0     )-> List[Any]:
     __A				 =       heap_size
     __A				 =       bottom_root
     __A				 =       min_node

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> Optional[int]:

     # Empty heaps corner cases
     if other.size == 0:
        return None
     if self.size == 0:
        __A				 =       other.size
        __A				 =       other.bottom_root
        __A				 =       other.min_node
        return None
     # Update size
     __A				 =       self.size + other.size

     # Update min.node
     if self.min_node.val > other.min_node.val:
        __A				 =       other.min_node
     # Merge

     # Order roots by left_subtree_size
     __A				 =       []
     __A ,						__A				 =       self.bottom_root, other.bottom_root
     while i or j:
        if i and ((not j) or i.left_tree_size < j.left_tree_size):
           combined_roots_list.append((i, True)     )
           __A				 =       i.parent
        else:
           combined_roots_list.append((j, False)     )
           __A				 =       j.parent
        # Insert links between them
     for i in range(len(UpperCAmelCase     ) - 1     ):
        if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
           __A				 =       combined_roots_list[i + 1][0]
           __A				 =       combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
     __A				 =       combined_roots_list[0][0]
     while i.parent:
        if (
            (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
        ) or (
            i.left_tree_size == i.parent.left_tree_size
            and i.left_tree_size != i.parent.parent.left_tree_size
        ):
           # Neighbouring Nodes
           __A				 =       i.left
           __A				 =       i.parent.parent

           # Merging trees
           __A				 =       i.merge_trees(i.parent     )

           # Updating links
           __A				 =       previous_node
           __A				 =       next_node
           if previous_node:
              __A				 =       i
           if next_node:
              __A				 =       i
        else:
           __A				 =       i.parent
        # Updating self.bottom_root
     while i.left:
        __A				 =       i.left
     __A				 =       i

     # Update other
     __A				 =       self.size
     __A				 =       self.bottom_root
     __A				 =       self.min_node

     # Return the merged heap
     return self

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> Optional[Any]:
     if self.size == 0:
        __A				 =       Node(UpperCAmelCase     )
        __A				 =       1
        __A				 =       self.bottom_root
     else:
        # Create new node
        __A				 =       Node(UpperCAmelCase     )

        # Update size
        self.size += 1

        # update min_node
        if val < self.min_node.val:
           __A				 =       new_node
        # Put new_node as a bottom_root in heap
        __A				 =       new_node
        __A				 =       self.bottom_root
        __A				 =       new_node

        # Consecutively merge roots with same left_tree_size
        while (
            self.bottom_root.parent
            and self.bottom_root.left_tree_size
            == self.bottom_root.parent.left_tree_size
        ):
           # Next node
           __A				 =       self.bottom_root.parent.parent

           # Merge
           __A				 =       self.bottom_root.merge_trees(self.bottom_root.parent     )

           # Update Links
           __A				 =       next_node
           __A				 =       None
           if next_node:
              __A				 =       self.bottom_root

  def 		SCREAMING_SNAKE_CASE__	( self     )-> int:
     return self.min_node.val

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Tuple:
     return self.size == 0

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     __A				 =       self.min_node.val

     # Last element in heap corner case
     if self.size == 1:
        # Update size
        __A				 =       0

        # Update bottom root
        __A				 =       None

        # Update min_node
        __A				 =       None

        return min_value
     # No right subtree corner case
     # The structure of the tree implies that this should be the bottom root
     # and there is at least one other root
     if self.min_node.right is None:
        # Update size
        self.size -= 1

        # Update bottom root
        __A				 =       self.bottom_root.parent
        __A				 =       None

        # Update min_node
        __A				 =       self.bottom_root
        __A				 =       self.bottom_root.parent
        while i:
           if i.val < self.min_node.val:
              __A				 =       i
           __A				 =       i.parent
        return min_value
     # General case
     # Find the BinomialHeap of the right subtree of min_node
     __A				 =       self.min_node.right
     __A				 =       None
     __A				 =       bottom_of_new
     __A				 =       1

     # Size, min_node and bottom_root
     while bottom_of_new.left:
        __A				 =       size_of_new * 2 + 1
        __A				 =       bottom_of_new.left
        if bottom_of_new.val < min_of_new.val:
           __A				 =       bottom_of_new
        # Corner case of single root on top left path
     if (not self.min_node.left) and (not self.min_node.parent):
        __A				 =       size_of_new
        __A				 =       bottom_of_new
        __A				 =       min_of_new
        # print("Single root, multiple nodes case")
        return min_value
     # Remaining cases
     # Construct heap of right subtree
     __A				 =       BinomialHeap(
         bottom_root=UpperCAmelCase  ,    min_node=UpperCAmelCase  ,    heap_size=UpperCAmelCase     )

     # Update size
     __A				 =       self.size - 1 - size_of_new

     # Neighbour nodes
     __A				 =       self.min_node.left
     __A				 =       self.min_node.parent

     # Initialize new bottom_root and min_node
     __A				 =       previous_node or next_node
     __A				 =       next_node

     # Update links of previous_node and search below for new min_node and
     # bottom_root
     if previous_node:
        __A				 =       next_node

        # Update bottom_root and search for min_node below
        __A				 =       previous_node
        __A				 =       previous_node
        while self.bottom_root.left:
           __A				 =       self.bottom_root.left
           if self.bottom_root.val < self.min_node.val:
              __A				 =       self.bottom_root
     if next_node:
        __A				 =       previous_node

        # Search for new min_node above min_node
        __A				 =       next_node
        while i:
           if i.val < self.min_node.val:
              __A				 =       i
           __A				 =       i.parent
        # Merge heaps
     self.merge_heaps(UpperCAmelCase     )

     return min_value

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[str]:
     __A				 =       self.bottom_root
     while top_root.parent:
        __A				 =       top_root.parent
     # preorder
     __A				 =       []
     self.__traversal(UpperCAmelCase  ,    UpperCAmelCase     )
     return heap_pre_order

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase=0     )-> Optional[Any]:
     if curr_node:
        preorder.append((curr_node.val, level)     )
        self.__traversal(curr_node.left  ,    UpperCAmelCase  ,    level + 1     )
        self.__traversal(curr_node.right  ,    UpperCAmelCase  ,    level + 1     )
     else:
        preorder.append(('''#''', level)     )





  def __str__( self     )-> str:
     if self.is_empty():
        return ""
     __A				 =       self.pre_order()

     return "\n".join(('''-''' * level + str(UpperCAmelCase     )) for value, level in preorder_heap     )


# Unit Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()

