



import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

a_    :  Optional[Any]						  =		collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
a_    :  Union[str, Any]						  =		'https://storage.googleapis.com/cvdf-datasets/mnist/'

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		numpy.dtype(numpy.uintaa			).newbyteorder(">"			)
      return numpy.frombuffer(bytestream.read(4			) , dtype=UpperCAmelCase__			)[0]

@deprecated(UpperCAmelCase__ , "Please use tf.data to implement this functionality."			)
def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      print("Extracting" , f.name			)
      with gzip.GzipFile(fileobj=UpperCAmelCase__			) as bytestream:
            lowerCamelCase             =		_readaa(UpperCAmelCase__			)
            if magic != 2051:
                  raise ValueError(
                      "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)			)
            lowerCamelCase             =		_readaa(UpperCAmelCase__			)
            lowerCamelCase             =		_readaa(UpperCAmelCase__			)
            lowerCamelCase             =		_readaa(UpperCAmelCase__			)
            lowerCamelCase             =		bytestream.read(rows * cols * num_images			)
            lowerCamelCase             =		numpy.frombuffer(UpperCAmelCase__ , dtype=numpy.uinta			)
            lowerCamelCase             =		data.reshape(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , 1			)
            return data

@deprecated(UpperCAmelCase__ , "Please use tf.one_hot on tensors."			)
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		labels_dense.shape[0]
      lowerCamelCase             =		numpy.arange(UpperCAmelCase__			) * num_classes
      lowerCamelCase             =		numpy.zeros((num_labels, num_classes)			)
      lowerCamelCase             =		1
      return labels_one_hot

@deprecated(UpperCAmelCase__ , "Please use tf.data to implement this functionality."			)
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__=False , UpperCAmelCase__=10			):


      """simple docstring"""



      print("Extracting" , f.name			)
      with gzip.GzipFile(fileobj=UpperCAmelCase__			) as bytestream:
            lowerCamelCase             =		_readaa(UpperCAmelCase__			)
            if magic != 2049:
                  raise ValueError(
                      "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)			)
            lowerCamelCase             =		_readaa(UpperCAmelCase__			)
            lowerCamelCase             =		bytestream.read(UpperCAmelCase__			)
            lowerCamelCase             =		numpy.frombuffer(UpperCAmelCase__ , dtype=numpy.uinta			)
            if one_hot:
                  return _dense_to_one_hot(UpperCAmelCase__ , UpperCAmelCase__			)
            return labels





class  lowerCamelCase__	:
     """simple docstring"""



     @deprecated(
         __a					,      "Please use alternatives such as official/mnist/_DataSet.py"
         " from tensorflow/models."					,      )
     def __init__(self					,      __a					,      __a					,      __a=False					,      __a=False					,      __a=dtypes.floataa					,      __a=True					,      __a=None					,      ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		random_seed.get_seed(__a     )
           # If op level seed is not set, use whatever graph level seed is returned
           numpy.random.seed(seeda if seed is None else seeda     )
           lowerCamelCase             =		dtypes.as_dtype(__a     ).base_dtype
           if dtype not in (dtypes.uinta, dtypes.floataa):
                 raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype     )
           if fake_data:
                 lowerCamelCase             =		1_00_00
                 lowerCamelCase             =		one_hot
           else:
                 assert (
                     images.shape[0] == labels.shape[0]
                 ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
                 lowerCamelCase             =		images.shape[0]

                 # Convert shape from [num examples, rows, columns, depth]
                 # to [num examples, rows*columns] (assuming depth == 1)
                 if reshape:
                       assert images.shape[3] == 1
                       lowerCamelCase             =		images.reshape(
                           images.shape[0]					,      images.shape[1] * images.shape[2]     )
                 if dtype == dtypes.floataa:
                       # Convert from [0, 255] -> [0.0, 1.0].
                       lowerCamelCase             =		images.astype(numpy.floataa     )
                       lowerCamelCase             =		numpy.multiply(__a					,      1.0 / 255.0     )
           lowerCamelCase             =		images
           lowerCamelCase             =		labels
           lowerCamelCase             =		0
           lowerCamelCase             =		0







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self._images







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self._labels







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self._num_examples







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self._epochs_completed







     def     _a   (self					,      __a					,      __a=False					,      __a=True     ):






           '''simple docstring'''



           if fake_data:
                 lowerCamelCase             =		[1] * 7_84
                 lowerCamelCase             =		[1] + [0] * 9 if self.one_hot else 0
                 return (
                     [fake_image for _ in range(__a     )],
                     [fake_label for _ in range(__a     )],
                 )
           lowerCamelCase             =		self._index_in_epoch
           # Shuffle for the first epoch
           if self._epochs_completed == 0 and start == 0 and shuffle:
                 lowerCamelCase             =		numpy.arange(self._num_examples     )
                 numpy.random.shuffle(__a     )
                 lowerCamelCase             =		self.images[perma]
                 lowerCamelCase             =		self.labels[perma]
           # Go to the next epoch
           if start + batch_size > self._num_examples:
                 # Finished epoch
                 self._epochs_completed += 1
                 # Get the rest examples in this epoch
                 lowerCamelCase             =		self._num_examples - start
                 lowerCamelCase             =		self._images[start : self._num_examples]
                 lowerCamelCase             =		self._labels[start : self._num_examples]
                 # Shuffle the data
                 if shuffle:
                       lowerCamelCase             =		numpy.arange(self._num_examples     )
                       numpy.random.shuffle(__a     )
                       lowerCamelCase             =		self.images[perm]
                       lowerCamelCase             =		self.labels[perm]
                 # Start next epoch
                 lowerCamelCase             =		0
                 lowerCamelCase             =		batch_size - rest_num_examples
                 lowerCamelCase             =		self._index_in_epoch
                 lowerCamelCase             =		self._images[start:end]
                 lowerCamelCase             =		self._labels[start:end]
                 return (
                     numpy.concatenate((images_rest_part, images_new_part)					,      axis=0     ),
                     numpy.concatenate((labels_rest_part, labels_new_part)					,      axis=0     ),
                 )
           else:
                 self._index_in_epoch += batch_size
                 lowerCamelCase             =		self._index_in_epoch
                 return self._images[start:end], self._labels[start:end]



@deprecated(UpperCAmelCase__ , "Please write your own downloading logic."			)
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if not gfile.Exists(UpperCAmelCase__			):
            gfile.MakeDirs(UpperCAmelCase__			)
      lowerCamelCase             =		os.path.join(UpperCAmelCase__ , UpperCAmelCase__			)
      if not gfile.Exists(UpperCAmelCase__			):
            urllib.request.urlretrieve(UpperCAmelCase__ , UpperCAmelCase__			)  # noqa: S310
            with gfile.GFile(UpperCAmelCase__			) as f:
                  lowerCamelCase             =		f.size()
            print("Successfully downloaded" , UpperCAmelCase__ , UpperCAmelCase__ , "bytes."			)
      return filepath






@deprecated(
    UpperCAmelCase__ , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"			)
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__=False , UpperCAmelCase__=False , UpperCAmelCase__=dtypes.floataa , UpperCAmelCase__=True , UpperCAmelCase__=5000 , UpperCAmelCase__=None , UpperCAmelCase__=DEFAULT_SOURCE_URL , ):


      """simple docstring"""



      if fake_data:

            def fake():
                  return _DataSet(
                      [] , [] , fake_data=UpperCAmelCase__ , one_hot=UpperCAmelCase__ , dtype=UpperCAmelCase__ , seed=UpperCAmelCase__			)

            lowerCamelCase             =		fake()
            lowerCamelCase             =		fake()
            lowerCamelCase             =		fake()
            return _Datasets(train=UpperCAmelCase__ , validation=UpperCAmelCase__ , test=UpperCAmelCase__			)

      if not source_url:  # empty string check
            lowerCamelCase             =		DEFAULT_SOURCE_URL

      lowerCamelCase             =		"train-images-idx3-ubyte.gz"
      lowerCamelCase             =		"train-labels-idx1-ubyte.gz"
      lowerCamelCase             =		"t10k-images-idx3-ubyte.gz"
      lowerCamelCase             =		"t10k-labels-idx1-ubyte.gz"

      lowerCamelCase             =		_maybe_download(
          UpperCAmelCase__ , UpperCAmelCase__ , source_url + train_images_file			)
      with gfile.Open(UpperCAmelCase__ , "rb"			) as f:
            lowerCamelCase             =		_extract_images(UpperCAmelCase__			)

      lowerCamelCase             =		_maybe_download(
          UpperCAmelCase__ , UpperCAmelCase__ , source_url + train_labels_file			)
      with gfile.Open(UpperCAmelCase__ , "rb"			) as f:
            lowerCamelCase             =		_extract_labels(UpperCAmelCase__ , one_hot=UpperCAmelCase__			)

      lowerCamelCase             =		_maybe_download(
          UpperCAmelCase__ , UpperCAmelCase__ , source_url + test_images_file			)
      with gfile.Open(UpperCAmelCase__ , "rb"			) as f:
            lowerCamelCase             =		_extract_images(UpperCAmelCase__			)

      lowerCamelCase             =		_maybe_download(
          UpperCAmelCase__ , UpperCAmelCase__ , source_url + test_labels_file			)
      with gfile.Open(UpperCAmelCase__ , "rb"			) as f:
            lowerCamelCase             =		_extract_labels(UpperCAmelCase__ , one_hot=UpperCAmelCase__			)

      if not 0 <= validation_size <= len(UpperCAmelCase__			):
            lowerCamelCase             =		(
                "Validation size should be between 0 and "
                F"""{len(UpperCAmelCase__			)}. Received: {validation_size}."""
            )
            raise ValueError(UpperCAmelCase__			)

      lowerCamelCase             =		train_images[:validation_size]
      lowerCamelCase             =		train_labels[:validation_size]
      lowerCamelCase             =		train_images[validation_size:]
      lowerCamelCase             =		train_labels[validation_size:]

      lowerCamelCase             =		{"dtype": dtype, "reshape": reshape, "seed": seed}

      lowerCamelCase             =		_DataSet(UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__			)
      lowerCamelCase             =		_DataSet(UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__			)
      lowerCamelCase             =		_DataSet(UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__			)

      return _Datasets(train=UpperCAmelCase__ , validation=UpperCAmelCase__ , test=UpperCAmelCase__			)




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  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      AudioLDMPipeline
     _A      =      TEXT_TO_AUDIO_PARAMS
     _A      =      TEXT_TO_AUDIO_BATCH_PARAMS
     _A      =      frozenset(
         [
             'num_inference_steps',
             'num_waveforms_per_prompt',
             'generator',
             'latents',
             'output_type',
             'return_dict',
             'callback',
             'callback_steps',
         ])







     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		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					,      )
           lowerCamelCase             =		DDIMScheduler(
               beta_start=0.0_0085					,      beta_end=0.012					,      beta_schedule="scaled_linear"					,      clip_sample=__a					,      set_alpha_to_one=__a					,      )
           torch.manual_seed(0     )
           lowerCamelCase             =		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     )
           lowerCamelCase             =		ClapTextConfig(
               bos_token_id=0					,      eos_token_id=2					,      hidden_size=32					,      intermediate_size=37					,      layer_norm_eps=1E-05					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=10_00					,      projection_dim=32					,      )
           lowerCamelCase             =		ClapTextModelWithProjection(__a     )
           lowerCamelCase             =		RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"					,      model_max_length=77     )

           lowerCamelCase             =		SpeechTaHifiGanConfig(
               model_in_dim=8					,      sampling_rate=1_60_00					,      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					,      )

           lowerCamelCase             =		SpeechTaHifiGan(__a     )

           lowerCamelCase             =		{
               "unet": unet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "vocoder": vocoder,
           }
           return components







     def     _a   (self					,      __a					,      __a=0     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator

           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 2_56

           lowerCamelCase             =		audio[:10]
           lowerCamelCase             =		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     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs["prompt"]]

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs.pop("prompt"     )]

           lowerCamelCase             =		audioldm_pipe.tokenizer(
               __a					,      padding="max_length"					,      max_length=audioldm_pipe.tokenizer.model_max_length					,      truncation=__a					,      return_tensors="pt"					,      )
           lowerCamelCase             =		text_inputs["input_ids"].to(__a     )

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

           lowerCamelCase             =		prompt_embeds

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

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







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * ["this is a negative prompt"]
           lowerCamelCase             =		negative_prompt
           lowerCamelCase             =		3 * [inputs["prompt"]]

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs.pop("prompt"     )]

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

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

                 embeds.append(__a     )

           lowerCamelCase      ,				lowerCamelCase             =		embeds

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

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







     def     _a   (self     ):






           '''simple docstring'''



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

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		"egg cracking"
           lowerCamelCase             =		audioldm_pipe(**__a					,      negative_prompt=__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 2_56

           lowerCamelCase             =		audio[:10]
           lowerCamelCase             =		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     _a   (self     ):






           '''simple docstring'''



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

           lowerCamelCase             =		"A hammer hitting a wooden surface"

           # test num_waveforms_per_prompt=1 (default)
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=2     ).audios

           assert audios.shape == (1, 2_56)

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

           assert audios.shape == (batch_size, 2_56)

           # test num_waveforms_per_prompt for single prompt
           lowerCamelCase             =		2
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=2					,      num_waveforms_per_prompt=__a     ).audios

           assert audios.shape == (num_waveforms_per_prompt, 2_56)

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

           assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)







     def     _a   (self     ):






           '''simple docstring'''



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

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

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

           lowerCamelCase             =		audioldm_pipe(audio_length_in_s=0.032					,      **__a     )
           lowerCamelCase             =		output.audios[0]

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







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		["hey"]

           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=1     )
           lowerCamelCase             =		output.audios.shape
           assert audio_shape == (1, 2_56)

           lowerCamelCase             =		audioldm_pipe.vocoder.config
           config.model_in_dim *= 2
           lowerCamelCase             =		SpeechTaHifiGan(__a     ).to(__a     )
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=1     )
           lowerCamelCase             =		output.audios.shape
           # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
           assert audio_shape == (1, 2_56)







     def     _a   (self     ):






           '''simple docstring'''



           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__a     )







     def     _a   (self     ):






           '''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     _a   (self     ):






           '''simple docstring'''



           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a     )





@slow
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self					,      __a					,      __a="cpu"					,      __a=torch.floataa					,      __a=0     ):






           '''simple docstring'''



           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(__a     )
           lowerCamelCase             =		np.random.RandomState(__a     ).standard_normal((1, 8, 1_28, 16)     )
           lowerCamelCase             =		torch.from_numpy(__a     ).to(device=__a					,      dtype=__a     )
           lowerCamelCase             =		{
               "prompt": "A hammer hitting a wooden surface",
               "latents": latents,
               "generator": generator,
               "num_inference_steps": 3,
               "guidance_scale": 2.5,
           }
           return inputs







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		AudioLDMPipeline.from_pretrained("cvssp/audioldm"     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_inputs(__a     )
           lowerCamelCase             =		25
           lowerCamelCase             =		audioldm_pipe(**__a     ).audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 8_19_20

           lowerCamelCase             =		audio[7_72_30:7_72_40]
           lowerCamelCase             =		np.array(
               [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]     )
           lowerCamelCase             =		np.abs(expected_slice - audio_slice     ).max()
           assert max_diff < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		AudioLDMPipeline.from_pretrained("cvssp/audioldm"     )
           lowerCamelCase             =		LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_inputs(__a     )
           lowerCamelCase             =		audioldm_pipe(**__a     ).audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 8_19_20

           lowerCamelCase             =		audio[2_77_80:2_77_90]
           lowerCamelCase             =		np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]     )
           lowerCamelCase             =		np.abs(expected_slice - audio_slice     ).max()
           assert max_diff < 3E-2