


'''simple docstring'''
import os
import re
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


UpperCAmelCase_ :     Tuple								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Tuple								=      {'vocab_file': 'spiece.model'}

UpperCAmelCase_ :     Dict								=      {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

UpperCAmelCase_ :     List[str]								=      {
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Union[str, Any]    							=						VOCAB_FILES_NAMES
 __lowercase				:		Union[str, Any]    							=						PRETRAINED_VOCAB_FILES_MAP
 __lowercase				:		Union[str, Any]    							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase				:		Tuple    							=						['''input_ids''', '''attention_mask''']
 __lowercase				:		List[int]    							=						[]


 def __init__(      self      ,      __lowercase      ,      __lowercase="<unk>"      ,      __lowercase="<s>"      ,      __lowercase="</s>"      ,      __lowercase="<pad>"      ,      __lowercase="[SEP]"      ,      __lowercase="[MASK]"      ,      __lowercase="[CLS]"      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Optional[int]       =			AddedToken(__lowercase      ,      lstrip=__lowercase      ,      rstrip=__lowercase						) if isinstance(__lowercase      ,      __lowercase						) else bos_token
  __A  :		Union[str, Any]       =			AddedToken(__lowercase      ,      lstrip=__lowercase      ,      rstrip=__lowercase						) if isinstance(__lowercase      ,      __lowercase						) else eos_token
  __A  :		Optional[Any]       =			AddedToken(__lowercase      ,      lstrip=__lowercase      ,      rstrip=__lowercase						) if isinstance(__lowercase      ,      __lowercase						) else unk_token
  __A  :		int       =			AddedToken(__lowercase      ,      lstrip=__lowercase      ,      rstrip=__lowercase						) if isinstance(__lowercase      ,      __lowercase						) else pad_token
  __A  :		Any       =			AddedToken(__lowercase      ,      lstrip=__lowercase      ,      rstrip=__lowercase						) if isinstance(__lowercase      ,      __lowercase						) else cls_token
  __A  :		List[Any]       =			AddedToken(__lowercase      ,      lstrip=__lowercase      ,      rstrip=__lowercase						) if isinstance(__lowercase      ,      __lowercase						) else sep_token

  # Mask token behave like a normal word, i.e. include the space before it
  __A  :		List[str]       =			AddedToken(__lowercase      ,      lstrip=__lowercase      ,      rstrip=__lowercase						) if isinstance(__lowercase      ,      __lowercase						) else mask_token

  __A  :		Dict       =			{} if sp_model_kwargs is None else sp_model_kwargs

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

  __A  :		str       =			vocab_file

  __A  :		str       =			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
  self.sp_model.Load(__lowercase						)


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return self.sp_model.get_piece_size()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			{self.convert_ids_to_tokens(__lowercase						): i for i in range(self.vocab_size						)}
  vocab.update(self.added_tokens_encoder						)
  return vocab


 def __getstate__(      self						):

  """simple docstring"""







  __A  :		Dict       =			self.__dict__.copy()
  __A  :		Optional[int]       =			None
  return state


 def __setstate__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			d

  # for backward compatibility
  if not hasattr(self      ,      'sp_model_kwargs'						):
   __A  :		Optional[int]       =			{}

  __A  :		Optional[Any]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
  self.sp_model.Load(self.vocab_file						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  return self.sp_model.encode(__lowercase      ,      out_type=__lowercase						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  return self.sp_model.piece_to_id(__lowercase						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.sp_model.IdToPiece(__lowercase						)
  return token


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			[]
  __A  :		Any       =			''
  __A  :		Optional[int]       =			False
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    if not prev_is_special:
     out_string += " "
    out_string += self.sp_model.decode(__lowercase						) + token
    __A  :		Any       =			True
    __A  :		List[Any]       =			[]
   else:
    current_sub_tokens.append(__lowercase						)
    __A  :		Tuple       =			False
  out_string += self.sp_model.decode(__lowercase						)
  return out_string.strip()


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = False      ,      __lowercase = None      ,      __lowercase = True      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		str       =			kwargs.pop('use_source_tokenizer'      ,      __lowercase						)

  __A  :		int       =			self.convert_ids_to_tokens(__lowercase      ,      skip_special_tokens=__lowercase						)

  # To avoid mixing byte-level and unicode for byte-level BPT
  # we need to build string separately for added tokens and byte-level tokens
  # cf. https://github.com/huggingface/transformers/issues/1133
  __A  :		Optional[Any]       =			[]
  __A  :		Union[str, Any]       =			[]
  for token in filtered_tokens:
   if skip_special_tokens and token in self.all_special_ids:
    continue
   if token in self.added_tokens_encoder:
    if current_sub_text:
     sub_texts.append(self.convert_tokens_to_string(__lowercase						)						)
     __A  :		Tuple       =			[]
    sub_texts.append(__lowercase						)
   else:
    current_sub_text.append(__lowercase						)
  if current_sub_text:
   sub_texts.append(self.convert_tokens_to_string(__lowercase						)						)

  # Mimic the behavior of the Rust tokenizer:
  # No space before [MASK] and [SEP]
  if spaces_between_special_tokens:
   __A  :		List[str]       =			re.sub(R' (\[(MASK|SEP)\])'      ,      R'\1'      ,      ' '.join(__lowercase						)						)
  else:
   __A  :		Optional[int]       =			''.join(__lowercase						)

  __A  :		Any       =			(
      clean_up_tokenization_spaces
      if clean_up_tokenization_spaces is not None
      else self.clean_up_tokenization_spaces
  )
  if clean_up_tokenization_spaces:
   __A  :		Any       =			self.clean_up_tokenization(__lowercase						)
   return clean_text
  else:
   return text


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  if not os.path.isdir(__lowercase						):
   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
   return
  __A  :		Tuple       =			os.path.join(
      __lowercase      ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

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

  return (out_vocab_file,)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  __A  :		Dict       =			[self.cls_token_id]
  __A  :		Optional[Any]       =			[self.sep_token_id]
  return cls + token_ids_a + sep + token_ids_a + sep


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = False						):

  """simple docstring"""







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

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







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  __A  :		List[str]       =			[self.sep_token_id]
  __A  :		Tuple       =			[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						) * [0] + len(token_ids_a + sep						) * [1]










'''simple docstring'''
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 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Optional[int]       =			UNetaDModel(
      sample_size=(32, 64)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('AttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'AttnUpBlock2D')      ,      )
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Dict       =			UNetaDConditionModel(
      sample_size=(64, 32)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')      ,      cross_attention_dim=10      ,      )
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Union[str, Any]       =			AutoencoderKL(
      sample_size=(128, 64)      ,      in_channels=1      ,      out_channels=1      ,      latent_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')      ,      up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')      ,      )
  __A  :		Union[str, Any]       =			UNetaDModel(
      sample_size=(64, 32)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('AttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'AttnUpBlock2D')      ,      )
  return vqvae, unet







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
  __A  :		int       =			Mel(
      x_res=self.dummy_unet.config.sample_size[1]      ,      y_res=self.dummy_unet.config.sample_size[0]      ,      )

  __A  :		Tuple       =			DDPMScheduler()
  __A  :		Optional[int]       =			AudioDiffusionPipeline(vqvae=__lowercase      ,      unet=self.dummy_unet      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Tuple       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Tuple       =			pipe(generator=__lowercase      ,      steps=4						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		Dict       =			output.images[0]

  __A  :		Optional[Any]       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[int]       =			pipe(generator=__lowercase      ,      steps=4      ,      return_dict=__lowercase						)
  __A  :		int       =			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]
  )
  __A  :		Optional[int]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[Any]       =			np.frombuffer(image_from_tuple.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]						)

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

  __A  :		Optional[int]       =			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]      ,      )

  __A  :		Optional[Any]       =			DDIMScheduler()
  __A  :		List[Any]       =			self.dummy_vqvae_and_unet
  __A  :		int       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=dummy_vqvae_and_unet[1]      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			np.random.uniform(-1      ,      1      ,      ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
  __A  :		Dict       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		List[Any]       =			pipe(raw_audio=__lowercase      ,      generator=__lowercase      ,      start_step=5      ,      steps=10						)
  __A  :		Tuple       =			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]
  )
  __A  :		int       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[str]       =			np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

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

  __A  :		Any       =			self.dummy_unet_condition
  __A  :		Optional[int]       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=__lowercase      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Union[str, Any]       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			torch.rand((1, 1, 10)						)
  __A  :		Any       =			pipe(generator=__lowercase      ,      encoding=__lowercase						)
  __A  :		Union[str, Any]       =			output.images[0]
  __A  :		List[Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		int       =			np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]						)

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


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




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			torch_device

  __A  :		Union[str, Any]       =			DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Any       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[Any]       =			pipe(generator=__lowercase						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		List[Any]       =			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]
  __A  :		Union[str, Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

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






