





'''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 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	AudioLDMPipeline
     lowerCAmelCase		      =	TEXT_TO_AUDIO_PARAMS
     lowerCAmelCase		      =	TEXT_TO_AUDIO_BATCH_PARAMS
     lowerCAmelCase		      =	frozenset(
         [
             '''num_inference_steps''',
             '''num_waveforms_per_prompt''',
             '''generator''',
             '''latents''',
             '''output_type''',
             '''return_dict''',
             '''callback''',
             '''callback_steps''',
         ]							)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A						:			Union[str, Any]								=	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=_UpperCAmelCase						,     )
          __A						:			Optional[int]								=	DDIMScheduler(
              beta_start=0.00085						,     beta_end=0.012						,     beta_schedule='scaled_linear'						,     clip_sample=_UpperCAmelCase						,     set_alpha_to_one=_UpperCAmelCase						,     )
          torch.manual_seed(0)
          __A						:			Union[str, Any]								=	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)
          __A						:			str								=	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=1000						,     projection_dim=32						,     )
          __A						:			Any								=	ClapTextModelWithProjection(_UpperCAmelCase)
          __A						:			int								=	RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'						,     model_max_length=77)

          __A						:			Union[str, Any]								=	SpeechTaHifiGanConfig(
              model_in_dim=8						,     sampling_rate=1_6000						,     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=_UpperCAmelCase						,     )

          __A						:			Optional[Any]								=	SpeechTaHifiGan(_UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=0):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	'cpu'  # ensure determinism for the device-dependent torch.Generator

          __A						:			Dict								=	self.get_dummy_components()
          __A						:			Optional[int]								=	AudioLDMPipeline(**_UpperCAmelCase)
          __A						:			Optional[Any]								=	audioldm_pipe.to(_UpperCAmelCase)
          audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			int								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			Tuple								=	audioldm_pipe(**_UpperCAmelCase)
          __A						:			Optional[Any]								=	output.audios[0]

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

          __A						:			Any								=	audio[:10]
          __A						:			Union[str, Any]								=	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):

          '''simple docstring'''






          __A						:			Dict								=	self.get_dummy_components()
          __A						:			List[Any]								=	AudioLDMPipeline(**_UpperCAmelCase)
          __A						:			List[Any]								=	audioldm_pipe.to(_UpperCAmelCase)
          __A						:			List[Any]								=	audioldm_pipe.to(_UpperCAmelCase)
          audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			str								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			List[Any]								=	3 * [inputs['prompt']]

          # forward
          __A						:			Tuple								=	audioldm_pipe(**_UpperCAmelCase)
          __A						:			str								=	output.audios[0]

          __A						:			Union[str, Any]								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	3 * [inputs.pop('prompt')]

          __A						:			Any								=	audioldm_pipe.tokenizer(
              _UpperCAmelCase						,     padding='max_length'						,     max_length=audioldm_pipe.tokenizer.model_max_length						,     truncation=_UpperCAmelCase						,     return_tensors='pt'						,     )
          __A						:			Tuple								=	text_inputs['input_ids'].to(_UpperCAmelCase)

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

          __A						:			Optional[Any]								=	prompt_embeds

          # forward
          __A						:			Union[str, Any]								=	audioldm_pipe(**_UpperCAmelCase)
          __A						:			Optional[int]								=	output.audios[0]

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	self.get_dummy_components()
          __A						:			Tuple								=	AudioLDMPipeline(**_UpperCAmelCase)
          __A						:			Dict								=	audioldm_pipe.to(_UpperCAmelCase)
          __A						:			Optional[Any]								=	audioldm_pipe.to(_UpperCAmelCase)
          audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			Tuple								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			Union[str, Any]								=	3 * ['this is a negative prompt']
          __A						:			List[Any]								=	negative_prompt
          __A						:			int								=	3 * [inputs['prompt']]

          # forward
          __A						:			List[Any]								=	audioldm_pipe(**_UpperCAmelCase)
          __A						:			str								=	output.audios[0]

          __A						:			Any								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			Any								=	3 * [inputs.pop('prompt')]

          __A						:			Any								=	[]
          for p in [prompt, negative_prompt]:
               __A						:			Union[str, Any]								=	audioldm_pipe.tokenizer(
                   _UpperCAmelCase						,     padding='max_length'						,     max_length=audioldm_pipe.tokenizer.model_max_length						,     truncation=_UpperCAmelCase						,     return_tensors='pt'						,     )
               __A						:			Tuple								=	text_inputs['input_ids'].to(_UpperCAmelCase)

               __A						:			Any								=	audioldm_pipe.text_encoder(
                   _UpperCAmelCase						,     )
               __A						:			Tuple								=	text_embeds.text_embeds
               # additional L_2 normalization over each hidden-state
               __A						:			str								=	F.normalize(_UpperCAmelCase						,     dim=-1)

               embeds.append(_UpperCAmelCase)

          __A			,__A						:			List[str]								=	embeds

          # forward
          __A						:			str								=	audioldm_pipe(**_UpperCAmelCase)
          __A						:			Optional[int]								=	output.audios[0]

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

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

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

          __A						:			Dict								=	audio[:10]
          __A						:			Optional[int]								=	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):

          '''simple docstring'''






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

          __A						:			int								=	'A hammer hitting a wooden surface'

          # test num_waveforms_per_prompt=1 (default)
          __A						:			Dict								=	audioldm_pipe(_UpperCAmelCase						,     num_inference_steps=2).audios

          assert audios.shape == (1, 256)

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

          assert audios.shape == (batch_size, 256)

          # test num_waveforms_per_prompt for single prompt
          __A						:			Union[str, Any]								=	2
          __A						:			Union[str, Any]								=	audioldm_pipe(_UpperCAmelCase						,     num_inference_steps=2						,     num_waveforms_per_prompt=_UpperCAmelCase).audios

          assert audios.shape == (num_waveforms_per_prompt, 256)

          # test num_waveforms_per_prompt for batch of prompts
          __A						:			int								=	2
          __A						:			Any								=	audioldm_pipe(
              [prompt] * batch_size						,     num_inference_steps=2						,     num_waveforms_per_prompt=_UpperCAmelCase).audios

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
          __A						:			Optional[int]								=	self.get_dummy_components()
          __A						:			int								=	AudioLDMPipeline(**_UpperCAmelCase)
          __A						:			List[str]								=	audioldm_pipe.to(_UpperCAmelCase)
          audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          __A						:			List[str]								=	audioldm_pipe.vocoder.config.sampling_rate

          __A						:			Optional[Any]								=	self.get_dummy_inputs(_UpperCAmelCase)
          __A						:			Optional[int]								=	audioldm_pipe(audio_length_in_s=0.016						,     **_UpperCAmelCase)
          __A						:			List[str]								=	output.audios[0]

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

          __A						:			Optional[Any]								=	audioldm_pipe(audio_length_in_s=0.032						,     **_UpperCAmelCase)
          __A						:			List[Any]								=	output.audios[0]

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	self.get_dummy_components()
          __A						:			List[Any]								=	AudioLDMPipeline(**_UpperCAmelCase)
          __A						:			Tuple								=	audioldm_pipe.to(_UpperCAmelCase)
          audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			List[str]								=	['hey']

          __A						:			Dict								=	audioldm_pipe(_UpperCAmelCase						,     num_inference_steps=1)
          __A						:			Union[str, Any]								=	output.audios.shape
          assert audio_shape == (1, 256)

          __A						:			Any								=	audioldm_pipe.vocoder.config
          config.model_in_dim *= 2
          __A						:			Any								=	SpeechTaHifiGan(_UpperCAmelCase).to(_UpperCAmelCase)
          __A						:			int								=	audioldm_pipe(_UpperCAmelCase						,     num_inference_steps=1)
          __A						:			Any								=	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):

          '''simple docstring'''






          self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self._test_inference_batch_single_identical(test_mean_pixel_difference=_UpperCAmelCase)


     @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):

          '''simple docstring'''






          self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_UpperCAmelCase)




@slow
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase="cpu"						,     _UpperCAmelCase=torch.floataa						,     _UpperCAmelCase=0):

          '''simple docstring'''






          __A						:			List[str]								=	torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A						:			Tuple								=	np.random.RandomState(_UpperCAmelCase).standard_normal((1, 8, 128, 16))
          __A						:			Any								=	torch.from_numpy(_UpperCAmelCase).to(device=_UpperCAmelCase						,     dtype=_UpperCAmelCase)
          __A						:			str								=	{
              '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):

          '''simple docstring'''






          __A						:			List[str]								=	AudioLDMPipeline.from_pretrained('cvssp/audioldm')
          __A						:			List[str]								=	audioldm_pipe.to(_UpperCAmelCase)
          audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			str								=	self.get_inputs(_UpperCAmelCase)
          __A						:			str								=	25
          __A						:			Union[str, Any]								=	audioldm_pipe(**_UpperCAmelCase).audios[0]

          assert audio.ndim == 1
          assert len(_UpperCAmelCase) == 8_1920

          __A						:			Optional[Any]								=	audio[7_7230:7_7240]
          __A						:			Optional[Any]								=	np.array(
              [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315])
          __A						:			int								=	np.abs(expected_slice - audio_slice).max()
          assert max_diff < 1e-2


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	AudioLDMPipeline.from_pretrained('cvssp/audioldm')
          __A						:			List[Any]								=	LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
          __A						:			str								=	audioldm_pipe.to(_UpperCAmelCase)
          audioldm_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						:			List[str]								=	self.get_inputs(_UpperCAmelCase)
          __A						:			Optional[int]								=	audioldm_pipe(**_UpperCAmelCase).audios[0]

          assert audio.ndim == 1
          assert len(_UpperCAmelCase) == 8_1920

          __A						:			Optional[int]								=	audio[2_7780:2_7790]
          __A						:			Optional[Any]								=	np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])
          __A						:			Union[str, Any]								=	np.abs(expected_slice - audio_slice).max()
          assert max_diff < 3e-2






'''simple docstring'''




import itertools
import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5			,							int(math.sqrt(__snake_case      ) + 1      )			,							6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def 	_lowerCAmelCase      (  )					->					List[Any]:
     __A						:			Optional[Any]								=	2
     while True:
          if is_prime(__snake_case      ):
               yield num
          num += 1






def 	_lowerCAmelCase      (  __snake_case  :			int = 1_00_01      )					->					int:
     return next(itertools.islice(prime_generator()			,							nth - 1			,							__snake_case      )      )


if __name__ == "__main__":
  print(f"""{solution() = }""")