

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  _snake_case		(snake_case_					:     List[str]    ,       snake_case_					:     str=1.0    ,       snake_case_					:     Dict=None    ,       snake_case_					:     Dict=None  )							->		Union[str, Any]:
  if rng is None:
    __UpperCAmelCase	:  List[str]		=     global_rng

  __UpperCAmelCase	:  Dict		=     []
  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     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     'facebook/musicgen-small'
   __UpperCAmelCase	:  int		=     tempfile.mkdtemp()




 def     a       (			self  ,  **_UpperCAmelCase							):
   return TaTokenizer.from_pretrained(self.checkpoint  ,  **A_							)




 def     a       (			self  ,  **_UpperCAmelCase							):
   return EncodecFeatureExtractor.from_pretrained(self.checkpoint  ,  **A_							)




 def     a       (			self							):
   shutil.rmtree(self.tmpdirname							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     self.get_tokenizer()
   __UpperCAmelCase	:  Dict		=     self.get_feature_extractor()

   __UpperCAmelCase	:  Dict		=     MusicgenProcessor(tokenizer=A_  ,  feature_extractor=A_							)

   processor.save_pretrained(self.tmpdirname							)
   __UpperCAmelCase	:  Optional[int]		=     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     a       (			self							):
   __UpperCAmelCase	:  int		=     MusicgenProcessor(tokenizer=self.get_tokenizer()  ,  feature_extractor=self.get_feature_extractor()							)
   processor.save_pretrained(self.tmpdirname							)

   __UpperCAmelCase	:  Optional[int]		=     self.get_tokenizer(bos_token='(BOS)'  ,  eos_token='(EOS)'							)
   __UpperCAmelCase	:  str		=     self.get_feature_extractor(do_normalize=A_  ,  padding_value=1.0							)

   __UpperCAmelCase	:  Tuple		=     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     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     self.get_feature_extractor()
   __UpperCAmelCase	:  List[Any]		=     self.get_tokenizer()

   __UpperCAmelCase	:  List[Any]		=     MusicgenProcessor(tokenizer=A_  ,  feature_extractor=A_							)

   __UpperCAmelCase	:  int		=     floats_list((3, 1_000)							)

   __UpperCAmelCase	:  Tuple		=     feature_extractor(A_  ,  return_tensors='np'							)
   __UpperCAmelCase	:  Tuple		=     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     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     self.get_feature_extractor()
   __UpperCAmelCase	:  Optional[Any]		=     self.get_tokenizer()

   __UpperCAmelCase	:  List[str]		=     MusicgenProcessor(tokenizer=A_  ,  feature_extractor=A_							)

   __UpperCAmelCase	:  Dict		=     'This is a test string'

   __UpperCAmelCase	:  Optional[int]		=     processor(text=A_							)

   __UpperCAmelCase	:  Any		=     tokenizer(A_							)

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




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     self.get_feature_extractor()
   __UpperCAmelCase	:  Dict		=     self.get_tokenizer()

   __UpperCAmelCase	:  Tuple		=     MusicgenProcessor(tokenizer=A_  ,  feature_extractor=A_							)

   __UpperCAmelCase	:  List[Any]		=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   __UpperCAmelCase	:  Tuple		=     processor.batch_decode(sequences=A_							)
   __UpperCAmelCase	:  int		=     tokenizer.batch_decode(A_							)

   self.assertListEqual(A_  ,  A_							)




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     self.get_feature_extractor()
   __UpperCAmelCase	:  str		=     self.get_tokenizer()

   __UpperCAmelCase	:  int		=     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     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     self.get_feature_extractor(padding_side='left'							)
   __UpperCAmelCase	:  Any		=     self.get_tokenizer()

   __UpperCAmelCase	:  Union[str, Any]		=     MusicgenProcessor(tokenizer=A_  ,  feature_extractor=A_							)

   __UpperCAmelCase	:  Any		=     [floats_list((1, x)							)[0] for x in range(5  ,  20  ,  5							)]
   __UpperCAmelCase	:  Optional[Any]		=     processor(A_							).padding_mask

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








# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Any  ,	A_     :					Union[str, Any]  ,	A_     :					Optional[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    key
      SCREAMING_SNAKE_CASE__         =    freq







  def __str__(						self     :					Union[str, Any]						)       ->			List[str]:
      return f"Node(key={self.key}, freq={self.freq})"





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A						):
    if i > j or i < 0 or j > len(__A						) - 1:
        return

    SCREAMING_SNAKE_CASE__         =    root[i][j]
    if parent == -1:  # root does not have a parent
        print(f"{key[node]} is the root of the binary search tree."						)
    elif is_left:
        print(f"{key[node]} is the left child of key {parent}."						)
    else:
        print(f"{key[node]} is the right child of key {parent}."						)

    print_binary_search_tree(__A			,							__A			,							__A			,							node - 1			,							key[node]			,							__A						)
    print_binary_search_tree(__A			,							__A			,							node + 1			,							__A			,							key[node]			,							__A						)





def 				__a							(       __A						):
    nodes.sort(key=lambda __A						: node.key						)

    SCREAMING_SNAKE_CASE__         =    len(__A						)

    SCREAMING_SNAKE_CASE__         =    [nodes[i].key for i in range(__A						)]
    SCREAMING_SNAKE_CASE__         =    [nodes[i].freq for i in range(__A						)]

    # This 2D array stores the overall tree cost (which's as minimized as possible);
    # for a single key, cost is equal to frequency of the key.
    SCREAMING_SNAKE_CASE__         =    [[freqs[i] if i == j else 0 for j in range(__A						)] for i in range(__A						)]
    # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
    # array
    SCREAMING_SNAKE_CASE__         =    [[freqs[i] if i == j else 0 for j in range(__A						)] for i in range(__A						)]
    # stores tree roots that will be used later for constructing binary search tree
    SCREAMING_SNAKE_CASE__         =    [[i if i == j else 0 for j in range(__A						)] for i in range(__A						)]

    for interval_length in range(2			,							n + 1						):
        for i in range(n - interval_length + 1						):
            SCREAMING_SNAKE_CASE__         =    i + interval_length - 1

            SCREAMING_SNAKE_CASE__         =    sys.maxsize  # set the value to "infinity"
            SCREAMING_SNAKE_CASE__         =    total[i][j - 1] + freqs[j]

            # Apply Knuth's optimization
            # Loop without optimization: for r in range(i, j + 1):
            for r in range(root[i][j - 1]			,							root[i + 1][j] + 1						):  # r is a temporal root
                SCREAMING_SNAKE_CASE__         =    dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                SCREAMING_SNAKE_CASE__         =    dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                SCREAMING_SNAKE_CASE__         =    left + total[i][j] + right

                if dp[i][j] > cost:
                    SCREAMING_SNAKE_CASE__         =    cost
                    SCREAMING_SNAKE_CASE__         =    r

    print("Binary search tree nodes:"						)
    for node in nodes:
        print(__A						)

    print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."						)
    print_binary_search_tree(__A			,							__A			,							0			,							n - 1			,							-1			,							__A						)





def 				__a							(       ):
    # A sample binary search tree
    SCREAMING_SNAKE_CASE__         =    [Node(__A			,							randint(1			,							50						)						) for i in range(10			,							0			,							-1						)]
    find_optimal_binary_search_tree(__A						)


if __name__ == "__main__":
  main()


