

"""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    __a							(UpperCamelCase_      ,		unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE  :Union[str, Any]				=       AudioLDMPipeline
     _SCREAMING_SNAKE_CASE  :int				=       TEXT_TO_AUDIO_PARAMS
     _SCREAMING_SNAKE_CASE  :Any				=       TEXT_TO_AUDIO_BATCH_PARAMS
     _SCREAMING_SNAKE_CASE  :Dict				=       frozenset(
         [
             """num_inference_steps""",
             """num_waveforms_per_prompt""",
             """generator""",
             """latents""",
             """output_type""",
             """return_dict""",
             """callback""",
             """callback_steps""",
         ])

     def     _a	(  self     )     ->					str:


           """simple docstring"""


           torch.manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		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=lowercase_		,			)
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		DDIMScheduler(
               beta_start=0.00_085		,			beta_end=0.012		,			beta_schedule="""scaled_linear"""		,			clip_sample=lowercase_		,			set_alpha_to_one=lowercase_		,			)
           torch.manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					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     )
           SCREAMING_SNAKE_CASE__      :					List[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=1_000		,			projection_dim=32		,			)
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		ClapTextModelWithProjection(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					int											=		RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""		,			model_max_length=77     )

           SCREAMING_SNAKE_CASE__      :					int											=		SpeechTaHifiGanConfig(
               model_in_dim=8		,			sampling_rate=16_000		,			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=lowercase_		,			)

           SCREAMING_SNAKE_CASE__      :					Tuple											=		SpeechTaHifiGan(lowercase_     )

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		{
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'vocoder': vocoder,
           }
           return components

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


           """simple docstring"""


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

     def     _a	(  self     )     ->					Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Tuple											=		'cpu'  # ensure determinism for the device-dependent torch.Generator

           SCREAMING_SNAKE_CASE__      :					Any											=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		AudioLDMPipeline(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe.to(lowercase_     )
           audioldm_pipe.set_progress_bar_config(disable=lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		self.get_dummy_inputs(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Any											=		output.audios[0]

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

           SCREAMING_SNAKE_CASE__      :					int											=		audio[:10]
           SCREAMING_SNAKE_CASE__      :					Any											=		np.array(
               [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033]     )

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

     def     _a	(  self     )     ->					int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		AudioLDMPipeline(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Any											=		audioldm_pipe.to(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe.to(lowercase_     )
           audioldm_pipe.set_progress_bar_config(disable=lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Tuple											=		self.get_dummy_inputs(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		3 * [inputs['prompt']]

           # forward
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		audioldm_pipe(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Any											=		output.audios[0]

           SCREAMING_SNAKE_CASE__      :					Any											=		self.get_dummy_inputs(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		3 * [inputs.pop("""prompt"""     )]

           SCREAMING_SNAKE_CASE__      :					Dict											=		audioldm_pipe.tokenizer(
               lowercase_		,			padding="""max_length"""		,			max_length=audioldm_pipe.tokenizer.model_max_length		,			truncation=lowercase_		,			return_tensors="""pt"""		,			)
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		text_inputs['input_ids'].to(lowercase_     )

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

           SCREAMING_SNAKE_CASE__      :					List[str]											=		prompt_embeds

           # forward
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		output.audios[0]

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

     def     _a	(  self     )     ->					int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE__      :					Dict											=		AudioLDMPipeline(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		audioldm_pipe.to(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		audioldm_pipe.to(lowercase_     )
           audioldm_pipe.set_progress_bar_config(disable=lowercase_     )

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

           # forward
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		output.audios[0]

           SCREAMING_SNAKE_CASE__      :					Any											=		self.get_dummy_inputs(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		3 * [inputs.pop("""prompt"""     )]

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

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

                 embeds.append(lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		embeds

           # forward
           SCREAMING_SNAKE_CASE__      :					int											=		audioldm_pipe(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Any											=		output.audios[0]

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

     def     _a	(  self     )     ->					Dict:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__      :					Tuple											=		self.get_dummy_inputs(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					int											=		'egg cracking'
           SCREAMING_SNAKE_CASE__      :					Dict											=		audioldm_pipe(**lowercase_		,			negative_prompt=lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Any											=		output.audios[0]

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

           SCREAMING_SNAKE_CASE__      :					Any											=		audio[:10]
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		np.array(
               [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032]     )

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

     def     _a	(  self     )     ->					Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Any											=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE__      :					Any											=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		PNDMScheduler(skip_prk_steps=lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		AudioLDMPipeline(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		audioldm_pipe.to(lowercase_     )
           audioldm_pipe.set_progress_bar_config(disable=lowercase_     )

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

           # test num_waveforms_per_prompt=1 (default)
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe(lowercase_		,			num_inference_steps=2     ).audios

           assert audios.shape == (1, 256)

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

           assert audios.shape == (batch_size, 256)

           # test num_waveforms_per_prompt for single prompt
           SCREAMING_SNAKE_CASE__      :					str											=		2
           SCREAMING_SNAKE_CASE__      :					int											=		audioldm_pipe(lowercase_		,			num_inference_steps=2		,			num_waveforms_per_prompt=lowercase_     ).audios

           assert audios.shape == (num_waveforms_per_prompt, 256)

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

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

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


           """simple docstring"""


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

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

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

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe(audio_length_in_s=0.032		,			**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Any											=		output.audios[0]

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

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		self.get_dummy_components()
           SCREAMING_SNAKE_CASE__      :					str											=		AudioLDMPipeline(**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Any											=		audioldm_pipe.to(lowercase_     )
           audioldm_pipe.set_progress_bar_config(disable=lowercase_     )

           SCREAMING_SNAKE_CASE__      :					str											=		['hey']

           SCREAMING_SNAKE_CASE__      :					Any											=		audioldm_pipe(lowercase_		,			num_inference_steps=1     )
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		output.audios.shape
           assert audio_shape == (1, 256)

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		audioldm_pipe.vocoder.config
           config.model_in_dim *= 2
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		SpeechTaHifiGan(lowercase_     ).to(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		audioldm_pipe(lowercase_		,			num_inference_steps=1     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		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     )     ->					Any:


           """simple docstring"""


           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase_     )

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


           """simple docstring"""


           self._test_inference_batch_single_identical(test_mean_pixel_difference=lowercase_     )





     @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:


           """simple docstring"""


           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase_     )





@slow
class    __a							(unittest.TestCase):






     '''simple docstring'''

     def     _a	(  self     )     ->					Dict:


           """simple docstring"""


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

     def     _a	(  self		,			_a		,			_a="cpu"		,			_a=torch.floataa		,			_a=0     )     ->					Optional[int]:


           """simple docstring"""


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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""     )
           SCREAMING_SNAKE_CASE__      :					int											=		audioldm_pipe.to(lowercase_     )
           audioldm_pipe.set_progress_bar_config(disable=lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		self.get_inputs(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Dict											=		25
           SCREAMING_SNAKE_CASE__      :					str											=		audioldm_pipe(**lowercase_     ).audios[0]

           assert audio.ndim == 1
           assert len(lowercase_     ) == 81_920

           SCREAMING_SNAKE_CASE__      :					Dict											=		audio[77_230:77_240]
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		np.array(
               [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315]     )
           SCREAMING_SNAKE_CASE__      :					Any											=		np.abs(expected_slice - audio_slice     ).max()
           assert max_diff < 1E-2





     def     _a	(  self     )     ->					List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Any											=		AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config     )
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe.to(lowercase_     )
           audioldm_pipe.set_progress_bar_config(disable=lowercase_     )

           SCREAMING_SNAKE_CASE__      :					Tuple											=		self.get_inputs(lowercase_     )
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		audioldm_pipe(**lowercase_     ).audios[0]

           assert audio.ndim == 1
           assert len(lowercase_     ) == 81_920

           SCREAMING_SNAKE_CASE__      :					List[str]											=		audio[27_780:27_790]
           SCREAMING_SNAKE_CASE__      :					str											=		np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212]     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		np.abs(expected_slice - audio_slice     ).max()
           assert max_diff < 3E-2








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
 from .camera import create_pan_cameras
 from .pipeline_shap_e import ShapEPipeline
 from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
 from .renderer import (
     BoundingBoxVolume,
     ImportanceRaySampler,
     MLPNeRFModelOutput,
     MLPNeRSTFModel,
     ShapEParamsProjModel,
     ShapERenderer,
     StratifiedRaySampler,
     VoidNeRFModel,
 )


