





"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_snake_case  :   int						   =  logging.get_logger(__name__)

_snake_case  :   Optional[int]						   =  '▁'

_snake_case  :   int						   =  {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_snake_case  :   Optional[Any]						   =  {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_snake_case  :   List[str]						   =  {'vinai/bartpho-syllable': 1024}



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			VOCAB_FILES_NAMES
  a_      =			PRETRAINED_VOCAB_FILES_MAP
  a_      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_      =			["""input_ids""", """attention_mask"""]






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		int="<s>" ,  lowerCAmelCase_			:		List[str]="</s>" ,  lowerCAmelCase_			:		int="</s>" ,  lowerCAmelCase_			:		Union[str, Any]="<s>" ,  lowerCAmelCase_			:		List[str]="<unk>" ,  lowerCAmelCase_			:		Any="<pad>" ,  lowerCAmelCase_			:		int="<mask>" ,  lowerCAmelCase_			:		Optional[Dict[str, Any]] = None ,  **lowerCAmelCase_			:		int ,  )							->						None:
     # Mask token behave like a normal word, i.e. include the space before it
     __lowerCAmelCase							     =     AddedToken(lowerCAmelCase_ ,  lstrip=lowerCAmelCase_ ,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) else mask_token

     __lowerCAmelCase							     =     {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=lowerCAmelCase_ ,  eos_token=lowerCAmelCase_ ,  unk_token=lowerCAmelCase_ ,  sep_token=lowerCAmelCase_ ,  cls_token=lowerCAmelCase_ ,  pad_token=lowerCAmelCase_ ,  mask_token=lowerCAmelCase_ ,  sp_model_kwargs=self.sp_model_kwargs ,  **lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     vocab_file
     __lowerCAmelCase							     =     monolingual_vocab_file
     __lowerCAmelCase							     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.Load(str(lowerCAmelCase_					)					)

     # Load the reduced vocab

     # Keep order of special tokens for backward compatibility
     __lowerCAmelCase							     =     {}
     __lowerCAmelCase							     =     0
     for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
        if str(lowerCAmelCase_					) not in self.fairseq_tokens_to_ids:
           __lowerCAmelCase							     =     cnt
           cnt += 1
     with open(lowerCAmelCase_ ,  'r' ,  encoding='utf-8'					) as f:
        for line in f.readlines():
           __lowerCAmelCase							     =     line.strip().split()[0]
           __lowerCAmelCase							     =     len(self.fairseq_tokens_to_ids					)
     if str(lowerCAmelCase_					) not in self.fairseq_tokens_to_ids:
        __lowerCAmelCase							     =     len(self.fairseq_tokens_to_ids					)

     __lowerCAmelCase							     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def __getstate__(    self			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.__dict__.copy()
     __lowerCAmelCase							     =     None
     __lowerCAmelCase							     =     self.sp_model.serialized_model_proto()
     return state






  def __setstate__(    self			:		int ,  lowerCAmelCase_			:		str					)							->						List[str]:
     __lowerCAmelCase							     =     d

     # for backward compatibility
     if not hasattr(self ,  'sp_model_kwargs'					):
        __lowerCAmelCase							     =     {}

     __lowerCAmelCase							     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:

     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __lowerCAmelCase							     =     [self.cls_token_id]
     __lowerCAmelCase							     =     [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None ,  lowerCAmelCase_			:		bool = False					)							->						List[int]:

     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCAmelCase_ ,  token_ids_a=lowerCAmelCase_ ,  already_has_special_tokens=lowerCAmelCase_					)

     if token_ids_a is None:
        return [1] + ([0] * len(lowerCAmelCase_					)) + [1]
     return [1] + ([0] * len(lowerCAmelCase_					)) + [1, 1] + ([0] * len(lowerCAmelCase_					)) + [1]






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:
     __lowerCAmelCase							     =     [self.sep_token_id]
     __lowerCAmelCase							     =     [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep					) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[Any]:
     return len(self.fairseq_ids_to_tokens					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     {self.convert_ids_to_tokens(lowerCAmelCase_					): i for i in range(self.vocab_size					)}
     vocab.update(self.added_tokens_encoder					)
     return vocab






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		str					)							->						List[str]:
     return self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_					)






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		List[Any]					)							->						int:
     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     else:
        return self.unk_token_id






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[int]					)							->						Tuple:
     return self.fairseq_ids_to_tokens[index]






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Any					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     ''.join(lowerCAmelCase_					).replace(lowerCAmelCase_ ,  ' '					).strip()
     return out_string






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[str] = None					)							->						Tuple[str]:
     if not os.path.isdir(lowerCAmelCase_					):
        logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
        return
     __lowerCAmelCase							     =     os.path.join(
         lowerCAmelCase_ ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
     __lowerCAmelCase							     =     os.path.join(
         lowerCAmelCase_ ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file'] ,  )

     if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase_					) and os.path.isfile(self.vocab_file					):
        copyfile(self.vocab_file ,  lowerCAmelCase_					)
     elif not os.path.isfile(self.vocab_file					):
        with open(lowerCAmelCase_ ,  'wb'					) as fi:
           __lowerCAmelCase							     =     self.sp_model.serialized_model_proto()
           fi.write(lowerCAmelCase_					)

     if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
         lowerCAmelCase_					) and os.path.isfile(self.monolingual_vocab_file					):
        copyfile(self.monolingual_vocab_file ,  lowerCAmelCase_					)
     elif not os.path.isfile(self.monolingual_vocab_file					):
        with open(lowerCAmelCase_ ,  'w' ,  encoding='utf-8'					) as fp:
           for token in self.fairseq_tokens_to_ids:
              if token not in self.all_special_tokens:
                 fp.write(f"""{str(lowerCAmelCase_					)} \n"""					)

     return out_vocab_file, out_monolingual_vocab_file








import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[str]					)							->						int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Any:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         sample_size=(3_2, 6_4) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,  )
     return model






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						List[Any]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDConditionModel(
         sample_size=(6_4, 3_2) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,  cross_attention_dim=1_0 ,  )
     return model






  @property
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     AutoencoderKL(
         sample_size=(1_2_8, 6_4) ,  in_channels=1 ,  out_channels=1 ,  latent_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,  up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,  )
     __lowerCAmelCase							     =     UNetaDModel(
         sample_size=(6_4, 3_2) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,  )
     return vqvae, unet






  @slow
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     Mel(
         x_res=self.dummy_unet.config.sample_size[1] ,  y_res=self.dummy_unet.config.sample_size[0] ,  )

     __lowerCAmelCase							     =     DDPMScheduler()
     __lowerCAmelCase							     =     AudioDiffusionPipeline(vqvae=lowerCAmelCase_ ,  unet=self.dummy_unet ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  steps=4					)
     __lowerCAmelCase							     =     output.audios[0]
     __lowerCAmelCase							     =     output.images[0]

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  steps=4 ,  return_dict=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output[0][0]

     assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
     assert (
         image.height == self.dummy_unet.config.sample_size[0]
         and image.width == self.dummy_unet.config.sample_size[1]
     )
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.frombuffer(image_from_tuple.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

     __lowerCAmelCase							     =     Mel(
         x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,  y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,  )

     __lowerCAmelCase							     =     DDIMScheduler()
     __lowerCAmelCase							     =     self.dummy_vqvae_and_unet
     __lowerCAmelCase							     =     AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0] ,  unet=dummy_vqvae_and_unet[1] ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     np.random.seed(0					)
     __lowerCAmelCase							     =     np.random.uniform(-1 ,  1 ,  ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(raw_audio=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  start_step=5 ,  steps=1_0					)
     __lowerCAmelCase							     =     output.images[0]

     assert (
         image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
         and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
     )
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

     __lowerCAmelCase							     =     self.dummy_unet_condition
     __lowerCAmelCase							     =     AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0] ,  unet=lowerCAmelCase_ ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     np.random.seed(0					)
     __lowerCAmelCase							     =     torch.rand((1, 1, 1_0)					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  encoding=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.images[0]
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0





@slow
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios[0]
     __lowerCAmelCase							     =     output.images[0]

     assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
     assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

