





'''simple docstring'''

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class lowerCamelCase						(unittest.TestCase					):







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






        """simple docstring"""





        _snake_case		:	str					     =     """ylacombe/bark-small"""
        _snake_case		:	List[Any]					     =     tempfile.mkdtemp()
        _snake_case		:	Dict					     =     """en_speaker_1"""
        _snake_case		:	Optional[int]					     =     """This is a test string"""
        _snake_case		:	str					     =     """speaker_embeddings_path.json"""
        _snake_case		:	List[str]					     =     """speaker_embeddings"""







  def 				UpperCAmelCase_				(		self     ,     **lowercase__		)		->						List[str]:






        """simple docstring"""





        return AutoTokenizer.from_pretrained(self.checkpoint     ,     **_a		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        shutil.rmtree(self.tmpdirname		)







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	str					     =     self.get_tokenizer()

        _snake_case		:	Optional[int]					     =     BarkProcessor(tokenizer=_a		)

        processor.save_pretrained(self.tmpdirname		)
        _snake_case		:	Optional[Any]					     =     BarkProcessor.from_pretrained(self.tmpdirname		)

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







  @slow
  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint     ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,     )
        processor.save_pretrained(
            self.tmpdirname     ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,     speaker_embeddings_directory=self.speaker_embeddings_directory     ,     )

        _snake_case		:	Tuple					     =     self.get_tokenizer(bos_token='''(BOS)'''     ,     eos_token='''(EOS)'''		)

        _snake_case		:	str					     =     BarkProcessor.from_pretrained(
            self.tmpdirname     ,     self.speaker_embeddings_dict_path     ,     bos_token='''(BOS)'''     ,     eos_token='''(EOS)'''     ,     )

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







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint     ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,     )

        _snake_case		:	Optional[Any]					     =     35
        _snake_case		:	Any					     =     2
        _snake_case		:	Tuple					     =     8

        _snake_case		:	Optional[int]					     =     {
            """semantic_prompt""": np.ones(_a		),
            """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)		),
            """fine_prompt""": np.ones((nb_codebooks_total, seq_len)		),
        }

        # test providing already loaded voice_preset
        _snake_case		:	Tuple					     =     processor(text=self.input_string     ,     voice_preset=_a		)

        _snake_case		:	Any					     =     inputs["""history_prompt"""]
        for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()     ,     processed_voice_preset.get(_a     ,     np.array([]		)		).tolist()		)

        # test loading voice preset from npz file
        _snake_case		:	str					     =     os.path.join(self.tmpdirname     ,     '''file.npz'''		)
        np.savez(_a     ,     **_a		)
        _snake_case		:	List[Any]					     =     processor(text=self.input_string     ,     voice_preset=_a		)
        _snake_case		:	Tuple					     =     inputs["""history_prompt"""]

        for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()     ,     processed_voice_preset.get(_a     ,     np.array([]		)		).tolist()		)

        # test loading voice preset from the hub
        _snake_case		:	List[Any]					     =     processor(text=self.input_string     ,     voice_preset=self.voice_preset		)







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	List[str]					     =     self.get_tokenizer()

        _snake_case		:	List[Any]					     =     BarkProcessor(tokenizer=_a		)

        _snake_case		:	Union[str, Any]					     =     processor(text=self.input_string		)

        _snake_case		:	Dict					     =     tokenizer(
            self.input_string     ,     padding='''max_length'''     ,     max_length=256     ,     add_special_tokens=_a     ,     return_attention_mask=_a     ,     return_token_type_ids=_a     ,     )

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









'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCAmelCase					:		Any		=							TypeVar('T')
UpperCAmelCase					:		str		=							TypeVar('U')





class lowerCamelCase						(Generic[T, U]					):







  def __init__(		self     ,     lowercase__     ,     lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     key
        _snake_case		:	Optional[int]					     =     val
        _snake_case		:	DoubleLinkedListNode[T, U] | None					     =     None
        _snake_case		:	DoubleLinkedListNode[T, U] | None					     =     None







  def __repr__(		self		)		->						str:






        """simple docstring"""





        return (
            F'''Node: key: {self.key}, val: {self.val}, '''
            F'''has next: {bool(self.next		)}, has prev: {bool(self.prev		)}'''
        )





class lowerCamelCase						(Generic[T, U]					):







  def __init__(		self		)		->						None:






        """simple docstring"""





        _snake_case		:	DoubleLinkedListNode[T, U]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
        _snake_case		:	DoubleLinkedListNode[T, U]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
        _snake_case     ,							_snake_case		:	Union[str, Any]					     =     self.rear, self.head







  def __repr__(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     ['''DoubleLinkedList''']
        _snake_case		:	str					     =     self.head
        while node.next is not None:
              rep.append(str(lowercase__		)		)
              _snake_case		:	List[str]					     =     node.next
        rep.append(str(self.rear		)		)
        return ",\n    ".join(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						None:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.rear.prev

        # All nodes other than self.head are guaranteed to have non-None previous
        assert previous is not None

        _snake_case		:	Union[str, Any]					     =     node
        _snake_case		:	Optional[Any]					     =     previous
        _snake_case		:	int					     =     node
        _snake_case		:	Union[str, Any]					     =     self.rear







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						DoubleLinkedListNode[T, U] | None:






        """simple docstring"""





        if node.prev is None or node.next is None:
              return None

        _snake_case		:	Optional[int]					     =     node.next
        _snake_case		:	Any					     =     node.prev
        _snake_case		:	List[str]					     =     None
        _snake_case		:	Optional[int]					     =     None
        return node





class lowerCamelCase						(Generic[T, U]					):
  _lowercase  :	dict[Callable[[T], U], LRUCache[T, U]]       					=				{}







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






        """simple docstring"""





        _snake_case		:	DoubleLinkedList[T, U]					     =     DoubleLinkedList()
        _snake_case		:	Union[str, Any]					     =     capacity
        _snake_case		:	int					     =     0
        _snake_case		:	Dict					     =     0
        _snake_case		:	Union[str, Any]					     =     0
        _snake_case		:	dict[T, DoubleLinkedListNode[T, U]]					     =     {}







  def __repr__(		self		)		->						str:






        """simple docstring"""





        return (
            F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
            F'''capacity={self.capacity}, current size={self.num_keys})'''
        )







  def __contains__(		self     ,     lowercase__		)		->						bool:






        """simple docstring"""





        return key in self.cache







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						U | None:






        """simple docstring"""





        if key in self.cache:
              self.hits += 1
              _snake_case		:	DoubleLinkedListNode[T, U]					     =     self.cache[key]
              _snake_case		:	Tuple					     =     self.list.remove(self.cache[key]		)
              assert node == value_node

              # node is guaranteed not None because it is in self.cache
              assert node is not None
              self.list.add(lowercase__		)
              return node.val
        self.miss += 1
        return None







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						None:






        """simple docstring"""





        if key not in self.cache:
              if self.num_keys >= self.capacity:
                    # delete first node (oldest) when over capacity
                    _snake_case		:	Dict					     =     self.list.head.next

                    # guaranteed to have a non-None first node when num_keys > 0
                    # explain to type checker via assertions
                    assert first_node is not None
                    assert first_node.key is not None
                    assert (
                        self.list.remove(lowercase__		) is not None
                    )  # node guaranteed to be in list assert node.key is not None

                    del self.cache[first_node.key]
                    self.num_keys -= 1
              _snake_case		:	Optional[int]					     =     DoubleLinkedListNode(lowercase__     ,     lowercase__		)
              self.list.add(self.cache[key]		)
              self.num_keys += 1

        else:
              # bump node to the end of the list, update value
              _snake_case		:	Optional[Any]					     =     self.list.remove(self.cache[key]		)
              assert node is not None  # node guaranteed to be in list
              _snake_case		:	Optional[Any]					     =     value
              self.list.add(lowercase__		)







  @classmethod
  def 				UpperCAmelCase_				(		cls     ,     lowercase__ = 128		)		->						Callable[[Callable[[T], U]], Callable[..., U]]:






        """simple docstring"""





        def cache_decorator_inner(lowercase__		) -> Callable[..., U]:
              def cache_decorator_wrapper(*lowercase__		) -> U:
                    if func not in cls.decorator_function_to_instance_map:
                          _snake_case		:	Optional[Any]					     =     LRUCache(lowercase__		)

                    _snake_case		:	Union[str, Any]					     =     cls.decorator_function_to_instance_map[func].get(args[0]		)
                    if result is None:
                          _snake_case		:	Tuple					     =     func(*lowercase__		)
                          cls.decorator_function_to_instance_map[func].put(args[0]     ,     lowercase__		)
                    return result

              def cache_info() -> LRUCache[T, U]:
                    return cls.decorator_function_to_instance_map[func]

              setattr(lowercase__     ,     '''cache_info'''     ,     lowercase__		)  # noqa: B010

              return cache_decorator_wrapper

        return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()


