





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   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







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


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Any:





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Union[str, Any]:





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     int:





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						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')    ,  )
     lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







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

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

     lowercase__			    =						torch.Generator(device=a ).manual_seed(42 )
     lowercase__			    =						pipe(generator=a    ,  steps=4 )
     lowercase__			    =						output.audios[0]
     lowercase__			    =						output.images[0]

     lowercase__			    =						torch.Generator(device=a ).manual_seed(42 )
     lowercase__			    =						pipe(generator=a    ,  steps=4    ,  return_dict=a )
     lowercase__			    =						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]
     )
     lowercase__			    =						np.frombuffer(image.tobytes()    ,  dtype='uint8' )[:10]
     lowercase__			    =						np.frombuffer(image_from_tuple.tobytes()    ,  dtype='uint8' )[:10]
     lowercase__			    =						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

     lowercase__			    =						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]    ,  )

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

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

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

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

     np.random.seed(0 )
     lowercase__			    =						torch.rand((1, 1, 10) )
     lowercase__			    =						pipe(generator=a    ,  encoding=a )
     lowercase__			    =						output.images[0]
     lowercase__			    =						np.frombuffer(image.tobytes()    ,  dtype='uint8' )[:10]
     lowercase__			    =						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        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


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





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     str:





     """simple docstring"""







     lowercase__			    =						torch_device

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

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

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









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_barthez import BarthezTokenizer
else:
      lowercase_					     =	None

lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_					     =	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase_					     =	{
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

lowercase_					     =	"""▁"""
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Dict				      = VOCAB_FILES_NAMES
   _UpperCamelCase					:	str				      = PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase					:	Optional[int]				      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase					:	Optional[Any]				      = ['input_ids', 'attention_mask']
   _UpperCamelCase					:	int				      = BarthezTokenizer


   def __init__(					self    :       List[Any]    ,  a    :       Union[str, Any]=None    ,  a    :       Optional[Any]=None    ,  a    :       Dict="<s>"    ,  a    :       Union[str, Any]="</s>"    ,  a    :       List[str]="</s>"    ,  a    :       Optional[Any]="<s>"    ,  a    :       int="<unk>"    ,  a    :       str="<pad>"    ,  a    :       Optional[int]="<mask>"    ,  **a    :       Union[str, Any]    ,  )->     Tuple:





     """simple docstring"""







     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else mask_token

     super().__init__(
         a    ,  tokenizer_file=a    ,  bos_token=a    ,  eos_token=a    ,  unk_token=a    ,  sep_token=a    ,  cls_token=a    ,  pad_token=a    ,  mask_token=a    ,  **a    ,  )

     lowercase__			    =						vocab_file
     lowercase__			    =						False if not self.vocab_file else True


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     List[int]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     List[int]:





     """simple docstring"""







     lowercase__			    =						[self.sep_token_id]
     lowercase__			    =						[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]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       str    ,  a    :       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(a ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
       return
     lowercase__			    =						os.path.join(
         a    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(a ):
       copyfile(self.vocab_file    ,  a )

     return (out_vocab_file,)


