




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   A(						UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


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

 def 				a__ (				self					:  List[str]  )   ->	str:

       """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=0.00085						,							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=1000						,							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=16000						,							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					:  Any						,							A_					:  Dict						,							A_					:  Any=0  )   ->	Optional[Any]:

       """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					:  Union[str, Any]  )   ->	Tuple:

       """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_  ) == 256

       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					:  Union[str, Any]  )   ->	Optional[int]:

       """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					:  Any  )   ->	Union[str, Any]:

       """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					:  Optional[int]  )   ->	int:

       """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_  ) == 256

       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					:  Dict  )   ->	int:

       """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, 256)

       # 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, 256)

       # 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, 256)

       # 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, 256)

 def 				a__ (				self					:  Dict  )   ->	List[Any]:

       """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					:  Any  )   ->	int:

       """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, 256)

       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, 256)

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

       """simple docstring"""






       self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A_  )

 def 				a__ (				self					:  Dict  )   ->	Dict:

       """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					:  Optional[int]  )   ->	List[Any]:

       """simple docstring"""






       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_  )







@slow
class   A(						unittest.TestCase   ):



 '''simple docstring'''


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

       """simple docstring"""






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

 def 				a__ (				self					:  str						,							A_					:  Any						,							A_					:  Optional[int]="cpu"						,							A_					:  Any=torch.floataa						,							A_					:  Dict=0  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    torch.Generator(device=A_  ).manual_seed(A_  )
       lowerCamelCase_		     =    np.random.RandomState(A_  ).standard_normal((1, 8, 128, 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					:  Optional[int]  )   ->	Any:

       """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_  ) == 81920

       lowerCamelCase_		     =    audio[77230:77240]
       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					:  int  )   ->	Tuple:

       """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_  ) == 81920

       lowerCamelCase_		     =    audio[27780:27790]
       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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Optional[Any]     =   {
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   ["WhisperTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "WhisperForConditionalGeneration",
     "WhisperModel",
     "WhisperPreTrainedModel",
     "WhisperForAudioClassification",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWhisperForConditionalGeneration",
     "TFWhisperModel",
     "TFWhisperPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "FlaxWhisperForConditionalGeneration",
     "FlaxWhisperModel",
     "FlaxWhisperPreTrainedModel",
     "FlaxWhisperForAudioClassification",
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:		Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



