






'''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      UpperCamelCase (				snake_case__			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   AudioLDMPipeline
		snake_case    =   TEXT_TO_AUDIO_PARAMS
		snake_case    =   TEXT_TO_AUDIO_BATCH_PARAMS
		snake_case    =   frozenset(
		    [
		        "num_inference_steps",
		        "num_waveforms_per_prompt",
		        "generator",
		        "latents",
		        "output_type",
		        "return_dict",
		        "callback",
		        "callback_steps",
		    ] )
		def       A(  self		:       Optional[int]		)    ->   List[Any]:


						'''simple docstring'''
						torch.manual_seed(0		)
						A	 =      UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)						,layers_per_block=2						,sample_size=3_2						,in_channels=4						,out_channels=4						,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,cross_attention_dim=(3_2, 6_4)						,class_embed_type='simple_projection'						,projection_class_embeddings_input_dim=3_2						,class_embeddings_concat=_SCREAMING_SNAKE_CASE						,)
						A	 =      DDIMScheduler(
						    beta_start=0.00085						,beta_end=0.012						,beta_schedule='scaled_linear'						,clip_sample=_SCREAMING_SNAKE_CASE						,set_alpha_to_one=_SCREAMING_SNAKE_CASE						,)
						torch.manual_seed(0		)
						A	 =      AutoencoderKL(
						    block_out_channels=[3_2, 6_4]						,in_channels=1						,out_channels=1						,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,latent_channels=4						,)
						torch.manual_seed(0		)
						A	 =      ClapTextConfig(
						    bos_token_id=0						,eos_token_id=2						,hidden_size=3_2						,intermediate_size=3_7						,layer_norm_eps=1E-05						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=1_0_0_0						,projection_dim=3_2						,)
						A	 =      ClapTextModelWithProjection(_SCREAMING_SNAKE_CASE		)
						A	 =      RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'						,model_max_length=7_7		)

						A	 =      SpeechTaHifiGanConfig(
						    model_in_dim=8						,sampling_rate=1_6_0_0_0						,upsample_initial_channel=1_6						,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=_SCREAMING_SNAKE_CASE						,)

						A	 =      SpeechTaHifiGan(_SCREAMING_SNAKE_CASE		)

						A	 =      {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'vocoder': vocoder,
						}
						return components
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[Any]=0		)    ->   str:


						'''simple docstring'''
						if str(_SCREAMING_SNAKE_CASE		).startswith('mps'		):
										A	 =      torch.manual_seed(_SCREAMING_SNAKE_CASE		)
						else:
										A	 =      torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
						A	 =      {
						    'prompt': 'A hammer hitting a wooden surface',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						}
						return inputs
		def       A(  self		:       Optional[int]		)    ->   str:


						'''simple docstring'''
						A	 =      'cpu'  # ensure determinism for the device-dependent torch.Generator

						A	 =      self.get_dummy_components()
						A	 =      AudioLDMPipeline(**_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe.to(_SCREAMING_SNAKE_CASE		)
						audioldm_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe(**_SCREAMING_SNAKE_CASE		)
						A	 =      output.audios[0]

						assert audio.ndim == 1
						assert len(_SCREAMING_SNAKE_CASE		) == 2_5_6

						A	 =      audio[:1_0]
						A	 =      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		:       Any		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.get_dummy_components()
						A	 =      AudioLDMPipeline(**_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe.to(_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe.to(_SCREAMING_SNAKE_CASE		)
						audioldm_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
						A	 =      3 * [inputs['prompt']]

						# forward
						A	 =      audioldm_pipe(**_SCREAMING_SNAKE_CASE		)
						A	 =      output.audios[0]

						A	 =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
						A	 =      3 * [inputs.pop('prompt'		)]

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

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

						A	 =      prompt_embeds

						# forward
						A	 =      audioldm_pipe(**_SCREAMING_SNAKE_CASE		)
						A	 =      output.audios[0]

						assert np.abs(audio_a - audio_a		).max() < 1E-2
		def       A(  self		:       Tuple		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.get_dummy_components()
						A	 =      AudioLDMPipeline(**_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe.to(_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe.to(_SCREAMING_SNAKE_CASE		)
						audioldm_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

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

						# forward
						A	 =      audioldm_pipe(**_SCREAMING_SNAKE_CASE		)
						A	 =      output.audios[0]

						A	 =      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
						A	 =      3 * [inputs.pop('prompt'		)]

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

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

										embeds.append(_SCREAMING_SNAKE_CASE		)

						A ,		A	 =      embeds

						# forward
						A	 =      audioldm_pipe(**_SCREAMING_SNAKE_CASE		)
						A	 =      output.audios[0]

						assert np.abs(audio_a - audio_a		).max() < 1E-2
		def       A(  self		:       int		)    ->   int:


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

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

						assert audio.ndim == 1
						assert len(_SCREAMING_SNAKE_CASE		) == 2_5_6

						A	 =      audio[:1_0]
						A	 =      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		:       Tuple		)    ->   int:


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

						A	 =      'A hammer hitting a wooden surface'

						# test num_waveforms_per_prompt=1 (default)
						A	 =      audioldm_pipe(_SCREAMING_SNAKE_CASE						,num_inference_steps=2		).audios

						assert audios.shape == (1, 2_5_6)

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

						assert audios.shape == (batch_size, 2_5_6)

						# test num_waveforms_per_prompt for single prompt
						A	 =      2
						A	 =      audioldm_pipe(_SCREAMING_SNAKE_CASE						,num_inference_steps=2						,num_waveforms_per_prompt=_SCREAMING_SNAKE_CASE		).audios

						assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

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

						assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)
		def       A(  self		:       Any		)    ->   Any:


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

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

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

						A	 =      audioldm_pipe(audio_length_in_s=0.032						,**_SCREAMING_SNAKE_CASE		)
						A	 =      output.audios[0]

						assert audio.ndim == 1
						assert len(_SCREAMING_SNAKE_CASE		) / vocoder_sampling_rate == 0.032
		def       A(  self		:       Optional[int]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.get_dummy_components()
						A	 =      AudioLDMPipeline(**_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe.to(_SCREAMING_SNAKE_CASE		)
						audioldm_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      ['hey']

						A	 =      audioldm_pipe(_SCREAMING_SNAKE_CASE						,num_inference_steps=1		)
						A	 =      output.audios.shape
						assert audio_shape == (1, 2_5_6)

						A	 =      audioldm_pipe.vocoder.config
						config.model_in_dim *= 2
						A	 =      SpeechTaHifiGan(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe(_SCREAMING_SNAKE_CASE						,num_inference_steps=1		)
						A	 =      output.audios.shape
						# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
						assert audio_shape == (1, 2_5_6)
		def       A(  self		:       Union[str, Any]		)    ->   List[Any]:


						'''simple docstring'''
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       int		)    ->   Any:


						'''simple docstring'''
						self._test_inference_batch_single_identical(test_mean_pixel_difference=_SCREAMING_SNAKE_CASE		)







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


						'''simple docstring'''
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_SCREAMING_SNAKE_CASE		)


@slow
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Dict="cpu"						,_SCREAMING_SNAKE_CASE		:       int=torch.floataa						,_SCREAMING_SNAKE_CASE		:       List[str]=0		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
						A	 =      np.random.RandomState(_SCREAMING_SNAKE_CASE		).standard_normal((1, 8, 1_2_8, 1_6)		)
						A	 =      torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE						,dtype=_SCREAMING_SNAKE_CASE		)
						A	 =      {
						    'prompt': 'A hammer hitting a wooden surface',
						    'latents': latents,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 2.5,
						}
						return inputs
		def       A(  self		:       Union[str, Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      AudioLDMPipeline.from_pretrained('cvssp/audioldm'		)
						A	 =      audioldm_pipe.to(_SCREAMING_SNAKE_CASE		)
						audioldm_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      self.get_inputs(_SCREAMING_SNAKE_CASE		)
						A	 =      2_5
						A	 =      audioldm_pipe(**_SCREAMING_SNAKE_CASE		).audios[0]

						assert audio.ndim == 1
						assert len(_SCREAMING_SNAKE_CASE		) == 8_1_9_2_0

						A	 =      audio[7_7_2_3_0:7_7_2_4_0]
						A	 =      np.array(
						    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]		)
						A	 =      np.abs(expected_slice - audio_slice		).max()
						assert max_diff < 1E-2







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


						'''simple docstring'''
						A	 =      AudioLDMPipeline.from_pretrained('cvssp/audioldm'		)
						A	 =      LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config		)
						A	 =      audioldm_pipe.to(_SCREAMING_SNAKE_CASE		)
						audioldm_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      self.get_inputs(_SCREAMING_SNAKE_CASE		)
						A	 =      audioldm_pipe(**_SCREAMING_SNAKE_CASE		).audios[0]

						assert audio.ndim == 1
						assert len(_SCREAMING_SNAKE_CASE		) == 8_1_9_2_0

						A	 =      audio[2_7_7_8_0:2_7_7_9_0]
						A	 =      np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]		)
						A	 =      np.abs(expected_slice - audio_slice		).max()
						assert max_diff < 3E-2













import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCAmelCase_           = '.'


if __name__ == "__main__":
					lowerCAmelCase_           = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
					lowerCAmelCase_           = []
					lowerCAmelCase_           = []
					with open(doctest_file_path) as fp:
										for line in fp:
															lowerCAmelCase_           = line.strip()
															lowerCAmelCase_           = os.path.join(REPO_PATH, line)
															if not (os.path.isfile(path) or os.path.isdir(path)):
																				non_existent_paths.append(line)
															all_paths.append(path)
					if len(non_existent_paths) > 0:
										lowerCAmelCase_           = '\n'.join(non_existent_paths)
										raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
					if all_paths != sorted(all_paths):
										raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')





