'''simple docstring'''

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 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      int      							=   AudioLDMPipeline
      UpperCAmelCase_      :      Optional[int]      							=   TEXT_TO_AUDIO_PARAMS
      UpperCAmelCase_      :      Optional[Any]      							=   TEXT_TO_AUDIO_BATCH_PARAMS
      UpperCAmelCase_      :      Tuple      							=   frozenset(
          [
              '''num_inference_steps''',
              '''num_waveforms_per_prompt''',
              '''generator''',
              '''latents''',
              '''output_type''',
              '''return_dict''',
              '''callback''',
              '''callback_steps''',
          ]   )




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



         '''simple docstring'''



         torch.manual_seed(0							)
         UpperCAmelCase__							:				Tuple   				=  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=lowerCamelCase_					,)
         UpperCAmelCase__							:				Union[str, Any]   				=  DDIMScheduler(
             beta_start=0.0_0085					,beta_end=0.012					,beta_schedule='''scaled_linear'''					,clip_sample=lowerCamelCase_					,set_alpha_to_one=lowerCamelCase_					,)
         torch.manual_seed(0							)
         UpperCAmelCase__							:				List[str]   				=  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							)
         UpperCAmelCase__							:				List[str]   				=  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					,)
         UpperCAmelCase__							:				Tuple   				=  ClapTextModelWithProjection(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''					,model_max_length=77							)

         UpperCAmelCase__							:				List[str]   				=  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=lowerCamelCase_					,)

         UpperCAmelCase__							:				List[Any]   				=  SpeechTaHifiGan(lowerCamelCase_							)

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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=0							)			->			Optional[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         UpperCAmelCase__							:				Optional[int]   				=  self.get_dummy_components()
         UpperCAmelCase__							:				int   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  output.audios[0]

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

         UpperCAmelCase__							:				Optional[int]   				=  audio[:10]
         UpperCAmelCase__							:				Dict   				=  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 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.get_dummy_components()
         UpperCAmelCase__							:				str   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  audioldm_pipe.to(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  3 * [inputs['''prompt''']]

         # forward
         UpperCAmelCase__							:				int   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  output.audios[0]

         UpperCAmelCase__							:				str   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  3 * [inputs.pop('''prompt'''							)]

         UpperCAmelCase__							:				int   				=  audioldm_pipe.tokenizer(
             lowerCamelCase_					,padding='''max_length'''					,max_length=audioldm_pipe.tokenizer.model_max_length					,truncation=lowerCamelCase_					,return_tensors='''pt'''					,)
         UpperCAmelCase__							:				Optional[Any]   				=  text_inputs['''input_ids'''].to(lowerCamelCase_							)

         UpperCAmelCase__							:				List[Any]   				=  audioldm_pipe.text_encoder(
             lowerCamelCase_					,)
         UpperCAmelCase__							:				List[Any]   				=  prompt_embeds.text_embeds
         # additional L_2 normalization over each hidden-state
         UpperCAmelCase__							:				Union[str, Any]   				=  F.normalize(lowerCamelCase_					,dim=-1							)

         UpperCAmelCase__							:				List[Any]   				=  prompt_embeds

         # forward
         UpperCAmelCase__							:				int   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  output.audios[0]

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




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  self.get_dummy_components()
         UpperCAmelCase__							:				List[str]   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  audioldm_pipe.to(lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  3 * ['''this is a negative prompt''']
         UpperCAmelCase__							:				Optional[int]   				=  negative_prompt
         UpperCAmelCase__							:				Any   				=  3 * [inputs['''prompt''']]

         # forward
         UpperCAmelCase__							:				int   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  output.audios[0]

         UpperCAmelCase__							:				Optional[Any]   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  3 * [inputs.pop('''prompt'''							)]

         UpperCAmelCase__							:				Optional[Any]   				=  []
         for p in [prompt, negative_prompt]:
            UpperCAmelCase__							:				Optional[Any]   				=  audioldm_pipe.tokenizer(
                lowerCamelCase_					,padding='''max_length'''					,max_length=audioldm_pipe.tokenizer.model_max_length					,truncation=lowerCamelCase_					,return_tensors='''pt'''					,)
            UpperCAmelCase__							:				Tuple   				=  text_inputs['''input_ids'''].to(lowerCamelCase_							)

            UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe.text_encoder(
                lowerCamelCase_					,)
            UpperCAmelCase__							:				Optional[Any]   				=  text_embeds.text_embeds
            # additional L_2 normalization over each hidden-state
            UpperCAmelCase__							:				Dict   				=  F.normalize(lowerCamelCase_					,dim=-1							)

            embeds.append(lowerCamelCase_							)

         UpperCAmelCase__							:				int   				=  embeds

         # forward
         UpperCAmelCase__							:				Any   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  output.audios[0]

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




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__							:				Optional[Any]   				=  self.get_dummy_components()
         UpperCAmelCase__							:				Tuple   				=  PNDMScheduler(skip_prk_steps=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  '''egg cracking'''
         UpperCAmelCase__							:				Union[str, Any]   				=  audioldm_pipe(**lowerCamelCase_					,negative_prompt=lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  output.audios[0]

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

         UpperCAmelCase__							:				Tuple   				=  audio[:10]
         UpperCAmelCase__							:				Tuple   				=  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 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__							:				int   				=  self.get_dummy_components()
         UpperCAmelCase__							:				Any   				=  PNDMScheduler(skip_prk_steps=lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  '''A hammer hitting a wooden surface'''

         # test num_waveforms_per_prompt=1 (default)
         UpperCAmelCase__							:				str   				=  audioldm_pipe(lowerCamelCase_					,num_inference_steps=2							).audios

         assert audios.shape == (1, 256)

         # test num_waveforms_per_prompt=1 (default) for batch of prompts
         UpperCAmelCase__							:				Union[str, Any]   				=  2
         UpperCAmelCase__							:				int   				=  audioldm_pipe([prompt] * batch_size					,num_inference_steps=2							).audios

         assert audios.shape == (batch_size, 256)

         # test num_waveforms_per_prompt for single prompt
         UpperCAmelCase__							:				Any   				=  2
         UpperCAmelCase__							:				Dict   				=  audioldm_pipe(lowerCamelCase_					,num_inference_steps=2					,num_waveforms_per_prompt=lowerCamelCase_							).audios

         assert audios.shape == (num_waveforms_per_prompt, 256)

         # test num_waveforms_per_prompt for batch of prompts
         UpperCAmelCase__							:				List[str]   				=  2
         UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe(
             [prompt] * batch_size					,num_inference_steps=2					,num_waveforms_per_prompt=lowerCamelCase_							).audios

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




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__							:				Tuple   				=  self.get_dummy_components()
         UpperCAmelCase__							:				str   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  audioldm_pipe.vocoder.config.sampling_rate

         UpperCAmelCase__							:				Optional[Any]   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  audioldm_pipe(audio_length_in_s=0.016					,**lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  output.audios[0]

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

         UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe(audio_length_in_s=0.032					,**lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  output.audios[0]

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




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  self.get_dummy_components()
         UpperCAmelCase__							:				Dict   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[int]   				=  ['''hey''']

         UpperCAmelCase__							:				int   				=  audioldm_pipe(lowerCamelCase_					,num_inference_steps=1							)
         UpperCAmelCase__							:				Tuple   				=  output.audios.shape
         assert audio_shape == (1, 256)

         UpperCAmelCase__							:				List[Any]   				=  audioldm_pipe.vocoder.config
         config.model_in_dim *= 2
         UpperCAmelCase__							:				Dict   				=  SpeechTaHifiGan(lowerCamelCase_							).to(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe(lowerCamelCase_					,num_inference_steps=1							)
         UpperCAmelCase__							:				Optional[int]   				=  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 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase_							)




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



         '''simple docstring'''



         self._test_inference_batch_single_identical(test_mean_pixel_difference=lowerCamelCase_							)




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



         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase_							)




@slow
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_="cpu"					,lowerCamelCase_=torch.floataa					,lowerCamelCase_=0							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.random.RandomState(lowerCamelCase_							).standard_normal((1, 8, 128, 16)							)
         UpperCAmelCase__							:				Optional[int]   				=  torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_					,dtype=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  {
             '''prompt''': '''A hammer hitting a wooden surface''',
             '''latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 2.5,
         }
         return inputs




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''							)
         UpperCAmelCase__							:				Any   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  self.get_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  25
         UpperCAmelCase__							:				Dict   				=  audioldm_pipe(**lowerCamelCase_							).audios[0]

         assert audio.ndim == 1
         assert len(lowerCamelCase_							) == 81920

         UpperCAmelCase__							:				Union[str, Any]   				=  audio[77230:77240]
         UpperCAmelCase__							:				Union[str, Any]   				=  np.array(
             [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]							)
         UpperCAmelCase__							:				List[Any]   				=  np.abs(expected_slice - audio_slice							).max()
         assert max_diff < 1e-2




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



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''							)
         UpperCAmelCase__							:				Tuple   				=  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config							)
         UpperCAmelCase__							:				int   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  audioldm_pipe(**lowerCamelCase_							).audios[0]

         assert audio.ndim == 1
         assert len(lowerCamelCase_							) == 81920

         UpperCAmelCase__							:				List[Any]   				=  audio[27780:27790]
         UpperCAmelCase__							:				Union[str, Any]   				=  np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]							)
         UpperCAmelCase__							:				Any   				=  np.abs(expected_slice - audio_slice							).max()
         assert max_diff < 3e-2





'''simple docstring'''

from __future__ import annotations

from collections import namedtuple







def __UpperCamelCase(   _A					:				float  ,			_A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  namedtuple('''result'''  ,			'''name value'''    )
   if (voltage, current, power).count(0    ) != 1:
      raise ValueError('''Only one argument must be 0'''    )
   elif power < 0:
      raise ValueError(
          '''Power cannot be negative in any electrical/electronics system'''    )
   elif voltage == 0:
      return result('''voltage'''  ,			power / current    )
   elif current == 0:
      return result('''current'''  ,			power / voltage    )
   elif power == 0:
      return result('''power'''  ,			float(round(abs(voltage * current    )  ,			2    )    )    )
   else:
      raise ValueError('''Exactly one argument must be 0'''    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




