






'''simple docstring'''


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_albert import AlbertTokenizer
else:
					lowerCamelCase_				: Tuple    	=							None

lowerCamelCase_				: Union[str, Any]    	=							logging.get_logger(__name__)
lowerCamelCase_				: Any    	=							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_				: str    	=							{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_				: Union[str, Any]    	=							{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

lowerCamelCase_				: List[str]    	=							'''▁'''
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Dict					     =					VOCAB_FILES_NAMES
			__a							:							List[Any]					     =					PRETRAINED_VOCAB_FILES_MAP
			__a							:							int					     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a							:							Tuple					     =					AlbertTokenizer






			def __init__(   self							:						str ,      lowercase							:						Optional[Any]=None ,      lowercase							:						Dict=None ,      lowercase							:						Tuple=True ,      lowercase							:						Union[str, Any]=True ,      lowercase							:						Optional[Any]=False ,      lowercase							:						Optional[Any]="[CLS]" ,      lowercase							:						Optional[Any]="[SEP]" ,      lowercase							:						Optional[Any]="<unk>" ,      lowercase							:						Optional[int]="[SEP]" ,      lowercase							:						Any="<pad>" ,      lowercase							:						Any="[CLS]" ,      lowercase							:						List[str]="[MASK]" ,      **lowercase							:						List[str] ,      )							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = (
									    AddedToken(lowercase ,      lstrip=lowercase ,      rstrip=lowercase ,      normalized=lowercase			)
									    if isinstance(lowercase ,      lowercase			)
									    else mask_token
									)

									super().__init__(
									    lowercase ,      tokenizer_file=lowercase ,      do_lower_case=lowercase ,      remove_space=lowercase ,      keep_accents=lowercase ,      bos_token=lowercase ,      eos_token=lowercase ,      unk_token=lowercase ,      sep_token=lowercase ,      pad_token=lowercase ,      cls_token=lowercase ,      mask_token=lowercase ,      **lowercase ,      )

									UpperCamelCase__         = do_lower_case
									UpperCamelCase__         = remove_space
									UpperCamelCase__         = keep_accents
									UpperCamelCase__         = vocab_file
									UpperCamelCase__         = False if not self.vocab_file else True






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

									'''simple docstring'''







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






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

									'''simple docstring'''







									UpperCamelCase__         = [self.sep_token_id]
									UpperCamelCase__         = [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]







			def 				A      (   self							:						List[Any] ,      lowercase							:						str ,      lowercase							:						Optional[str] = None			)							->    Tuple[str]:

									'''simple docstring'''







									if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""			)

									if not os.path.isdir(lowercase			):
															logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
															return
									UpperCamelCase__         = 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			):
															copyfile(self.vocab_file ,      lowercase			)

									return (out_vocab_file,)








'''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    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						Any			)							->    Any:

									'''simple docstring'''







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






			@property
			def 				A      (   self							:						Optional[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = 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 				A      (   self							:						List[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = 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 				A      (   self							:						int			)							->    Optional[Any]:

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = 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""") ,      )
									UpperCamelCase__         = 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 				A      (   self							:						Tuple			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase__         = Mel(
									    x_res=self.dummy_unet.config.sample_size[1] ,      y_res=self.dummy_unet.config.sample_size[0] ,      )

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

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

									UpperCamelCase__         = torch.Generator(device=lowercase			).manual_seed(4_2			)
									UpperCamelCase__         = pipe(generator=lowercase ,      steps=4 ,      return_dict=lowercase			)
									UpperCamelCase__         = 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]
									)
									UpperCamelCase__         = np.frombuffer(image.tobytes() ,      dtype="""uint8"""			)[:1_0]
									UpperCamelCase__         = np.frombuffer(image_from_tuple.tobytes() ,      dtype="""uint8"""			)[:1_0]
									UpperCamelCase__         = 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

									UpperCamelCase__         = 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] ,      )

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

									np.random.seed(0			)
									UpperCamelCase__         = np.random.uniform(-1 ,      1 ,      ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)			)
									UpperCamelCase__         = torch.Generator(device=lowercase			).manual_seed(4_2			)
									UpperCamelCase__         = pipe(raw_audio=lowercase ,      generator=lowercase ,      start_step=5 ,      steps=1_0			)
									UpperCamelCase__         = 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]
									)
									UpperCamelCase__         = np.frombuffer(image.tobytes() ,      dtype="""uint8"""			)[:1_0]
									UpperCamelCase__         = 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

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

									np.random.seed(0			)
									UpperCamelCase__         = torch.rand((1, 1, 1_0)			)
									UpperCamelCase__         = pipe(generator=lowercase ,      encoding=lowercase			)
									UpperCamelCase__         = output.images[0]
									UpperCamelCase__         = np.frombuffer(image.tobytes() ,      dtype="""uint8"""			)[:1_0]
									UpperCamelCase__         = 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    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						int			)							->    str:

									'''simple docstring'''







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







			def 				A      (   self							:						Any			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = torch_device

									UpperCamelCase__         = DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""			)
									UpperCamelCase__         = pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = torch.Generator(device=lowercase			).manual_seed(4_2			)
									UpperCamelCase__         = pipe(generator=lowercase			)
									UpperCamelCase__         = output.audios[0]
									UpperCamelCase__         = 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]
									UpperCamelCase__         = np.frombuffer(image.tobytes() ,      dtype="""uint8"""			)[:1_0]
									UpperCamelCase__         = 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
