





import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		Dict			 =	AudioLDMPipeline
   _UpperCamelCase   :		str			 =	TEXT_TO_AUDIO_PARAMS
   _UpperCamelCase   :		int			 =	TEXT_TO_AUDIO_BATCH_PARAMS
   _UpperCamelCase   :		Dict			 =	frozenset(
       [
           'num_inference_steps',
           'num_waveforms_per_prompt',
           'generator',
           'latents',
           'output_type',
           'return_dict',
           'callback',
           'callback_steps',
       ]				)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       str:





       """simple docstring"""







       torch.manual_seed(0	)
       lowercase__           =  UNetaDConditionModel(
           block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=4      ,					out_channels=4      ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,					cross_attention_dim=(32, 64)      ,					class_embed_type='simple_projection'      ,					projection_class_embeddings_input_dim=32      ,					class_embeddings_concat=a      ,					)
       lowercase__           =  DDIMScheduler(
           beta_start=0.00085      ,					beta_end=0.012      ,					beta_schedule='scaled_linear'      ,					clip_sample=a      ,					set_alpha_to_one=a      ,					)
       torch.manual_seed(0	)
       lowercase__           =  AutoencoderKL(
           block_out_channels=[32, 64]      ,					in_channels=1      ,					out_channels=1      ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,					latent_channels=4      ,					)
       torch.manual_seed(0	)
       lowercase__           =  ClapTextConfig(
           bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-0_5      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1_000      ,					projection_dim=32      ,					)
       lowercase__           =  ClapTextModelWithProjection(a	)
       lowercase__           =  RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'      ,					model_max_length=77	)

       lowercase__           =  SpeechTaHifiGanConfig(
           model_in_dim=8      ,					sampling_rate=16_000      ,					upsample_initial_channel=16      ,					upsample_rates=[2, 2]      ,					upsample_kernel_sizes=[4, 4]      ,					resblock_kernel_sizes=[3, 7]      ,					resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]      ,					normalize_before=a      ,					)

       lowercase__           =  SpeechTaHifiGan(a	)

       lowercase__           =  {
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'vocoder': vocoder,
       }
       return components


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    Optional[Any]      ,					a      :    Dict=0	)->       str:





       """simple docstring"""







       if str(a	).startswith('mps'	):
           lowercase__           =  torch.manual_seed(a	)
       else:
           lowercase__           =  torch.Generator(device=a	).manual_seed(a	)
       lowercase__           =  {
           'prompt': 'A hammer hitting a wooden surface',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
       }
       return inputs


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





       """simple docstring"""







       lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator

       lowercase__           =  self.get_dummy_components()
       lowercase__           =  AudioLDMPipeline(**a	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)

       lowercase__           =  self.get_dummy_inputs(a	)
       lowercase__           =  audioldm_pipe(**a	)
       lowercase__           =  output.audios[0]

       assert audio.ndim == 1
       assert len(a	) == 256

       lowercase__           =  audio[:10]
       lowercase__           =  np.array(
           [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]	)

       assert np.abs(audio_slice - expected_slice	).max() < 1E-2


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





       """simple docstring"""







       lowercase__           =  self.get_dummy_components()
       lowercase__           =  AudioLDMPipeline(**a	)
       lowercase__           =  audioldm_pipe.to(a	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)

       lowercase__           =  self.get_dummy_inputs(a	)
       lowercase__           =  3 * [inputs['prompt']]

       # forward
       lowercase__           =  audioldm_pipe(**a	)
       lowercase__           =  output.audios[0]

       lowercase__           =  self.get_dummy_inputs(a	)
       lowercase__           =  3 * [inputs.pop('prompt'	)]

       lowercase__           =  audioldm_pipe.tokenizer(
           a      ,					padding='max_length'      ,					max_length=audioldm_pipe.tokenizer.model_max_length      ,					truncation=a      ,					return_tensors='pt'      ,					)
       lowercase__           =  text_inputs['input_ids'].to(a	)

       lowercase__           =  audioldm_pipe.text_encoder(
           a      ,					)
       lowercase__           =  prompt_embeds.text_embeds
       # additional L_2 normalization over each hidden-state
       lowercase__           =  F.normalize(a      ,					dim=-1	)

       lowercase__           =  prompt_embeds

       # forward
       lowercase__           =  audioldm_pipe(**a	)
       lowercase__           =  output.audios[0]

       assert np.abs(audio_a - audio_a	).max() < 1E-2


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       Optional[int]:





       """simple docstring"""







       lowercase__           =  self.get_dummy_components()
       lowercase__           =  AudioLDMPipeline(**a	)
       lowercase__           =  audioldm_pipe.to(a	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)

       lowercase__           =  self.get_dummy_inputs(a	)
       lowercase__           =  3 * ['this is a negative prompt']
       lowercase__           =  negative_prompt
       lowercase__           =  3 * [inputs['prompt']]

       # forward
       lowercase__           =  audioldm_pipe(**a	)
       lowercase__           =  output.audios[0]

       lowercase__           =  self.get_dummy_inputs(a	)
       lowercase__           =  3 * [inputs.pop('prompt'	)]

       lowercase__           =  []
       for p in [prompt, negative_prompt]:
           lowercase__           =  audioldm_pipe.tokenizer(
               a      ,					padding='max_length'      ,					max_length=audioldm_pipe.tokenizer.model_max_length      ,					truncation=a      ,					return_tensors='pt'      ,					)
           lowercase__           =  text_inputs['input_ids'].to(a	)

           lowercase__           =  audioldm_pipe.text_encoder(
               a      ,					)
           lowercase__           =  text_embeds.text_embeds
           # additional L_2 normalization over each hidden-state
           lowercase__           =  F.normalize(a      ,					dim=-1	)

           embeds.append(a	)

       lowercase__      ,   lowercase__           =  embeds

       # forward
       lowercase__           =  audioldm_pipe(**a	)
       lowercase__           =  output.audios[0]

       assert np.abs(audio_a - audio_a	).max() < 1E-2


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       int:





       """simple docstring"""







       lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowercase__           =  self.get_dummy_components()
       lowercase__           =  PNDMScheduler(skip_prk_steps=a	)
       lowercase__           =  AudioLDMPipeline(**a	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)

       lowercase__           =  self.get_dummy_inputs(a	)
       lowercase__           =  'egg cracking'
       lowercase__           =  audioldm_pipe(**a      ,					negative_prompt=a	)
       lowercase__           =  output.audios[0]

       assert audio.ndim == 1
       assert len(a	) == 256

       lowercase__           =  audio[:10]
       lowercase__           =  np.array(
           [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]	)

       assert np.abs(audio_slice - expected_slice	).max() < 1E-2


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Any:





       """simple docstring"""







       lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowercase__           =  self.get_dummy_components()
       lowercase__           =  PNDMScheduler(skip_prk_steps=a	)
       lowercase__           =  AudioLDMPipeline(**a	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)

       lowercase__           =  'A hammer hitting a wooden surface'

       # test num_waveforms_per_prompt=1 (default)
       lowercase__           =  audioldm_pipe(a      ,					num_inference_steps=2	).audios

       assert audios.shape == (1, 256)

       # test num_waveforms_per_prompt=1 (default) for batch of prompts
       lowercase__           =  2
       lowercase__           =  audioldm_pipe([prompt] * batch_size      ,					num_inference_steps=2	).audios

       assert audios.shape == (batch_size, 256)

       # test num_waveforms_per_prompt for single prompt
       lowercase__           =  2
       lowercase__           =  audioldm_pipe(a      ,					num_inference_steps=2      ,					num_waveforms_per_prompt=a	).audios

       assert audios.shape == (num_waveforms_per_prompt, 256)

       # test num_waveforms_per_prompt for batch of prompts
       lowercase__           =  2
       lowercase__           =  audioldm_pipe(
           [prompt] * batch_size      ,					num_inference_steps=2      ,					num_waveforms_per_prompt=a	).audios

       assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)


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





       """simple docstring"""







       lowercase__           =  'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowercase__           =  self.get_dummy_components()
       lowercase__           =  AudioLDMPipeline(**a	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)
       lowercase__           =  audioldm_pipe.vocoder.config.sampling_rate

       lowercase__           =  self.get_dummy_inputs(a	)
       lowercase__           =  audioldm_pipe(audio_length_in_s=0.016      ,					**a	)
       lowercase__           =  output.audios[0]

       assert audio.ndim == 1
       assert len(a	) / vocoder_sampling_rate == 0.016

       lowercase__           =  audioldm_pipe(audio_length_in_s=0.032      ,					**a	)
       lowercase__           =  output.audios[0]

       assert audio.ndim == 1
       assert len(a	) / vocoder_sampling_rate == 0.032


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





       """simple docstring"""







       lowercase__           =  self.get_dummy_components()
       lowercase__           =  AudioLDMPipeline(**a	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)

       lowercase__           =  ['hey']

       lowercase__           =  audioldm_pipe(a      ,					num_inference_steps=1	)
       lowercase__           =  output.audios.shape
       assert audio_shape == (1, 256)

       lowercase__           =  audioldm_pipe.vocoder.config
       config.model_in_dim *= 2
       lowercase__           =  SpeechTaHifiGan(a	).to(a	)
       lowercase__           =  audioldm_pipe(a      ,					num_inference_steps=1	)
       lowercase__           =  output.audios.shape
       # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
       assert audio_shape == (1, 256)


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





       """simple docstring"""







       self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a	)


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





       """simple docstring"""







       self._test_inference_batch_single_identical(test_mean_pixel_difference=a	)


   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()      ,					reason='XFormers attention is only available with CUDA and `xformers` installed'      ,					)
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Any:





       """simple docstring"""







       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a	)






@slow
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       int:





       """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    List[str]      ,					a      :    Dict="cpu"      ,					a      :    int=torch.floataa      ,					a      :    int=0	)->       Optional[Any]:





       """simple docstring"""







       lowercase__           =  torch.Generator(device=a	).manual_seed(a	)
       lowercase__           =  np.random.RandomState(a	).standard_normal((1, 8, 128, 16)	)
       lowercase__           =  torch.from_numpy(a	).to(device=a      ,					dtype=a	)
       lowercase__           =  {
           'prompt': 'A hammer hitting a wooden surface',
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 2.5,
       }
       return inputs


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





       """simple docstring"""







       lowercase__           =  AudioLDMPipeline.from_pretrained('cvssp/audioldm'	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)

       lowercase__           =  self.get_inputs(a	)
       lowercase__           =  25
       lowercase__           =  audioldm_pipe(**a	).audios[0]

       assert audio.ndim == 1
       assert len(a	) == 81_920

       lowercase__           =  audio[77_230:77_240]
       lowercase__           =  np.array(
           [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]	)
       lowercase__           =  np.abs(expected_slice - audio_slice	).max()
       assert max_diff < 1E-2


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





       """simple docstring"""







       lowercase__           =  AudioLDMPipeline.from_pretrained('cvssp/audioldm'	)
       lowercase__           =  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config	)
       lowercase__           =  audioldm_pipe.to(a	)
       audioldm_pipe.set_progress_bar_config(disable=a	)

       lowercase__           =  self.get_inputs(a	)
       lowercase__           =  audioldm_pipe(**a	).audios[0]

       assert audio.ndim == 1
       assert len(a	) == 81_920

       lowercase__           =  audio[27_780:27_790]
       lowercase__           =  np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]	)
       lowercase__           =  np.abs(expected_slice - audio_slice	).max()
       assert max_diff < 3E-2









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_camembert import CamembertTokenizer
else:
      lowercase_          =		None


lowercase_          =		logging.get_logger(__name__)

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

lowercase_          =		{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

lowercase_          =		{
    """camembert-base""": 512,
}

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


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





     """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      ,					sep_token=a      ,					cls_token=a      ,					unk_token=a      ,					pad_token=a      ,					mask_token=a      ,					additional_special_tokens=a      ,					**a      ,					)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    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      :    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      :    Optional[int]      ,					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,)


