'''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      ):
    a__			:		Dict			  =    AudioLDMPipeline
    a__			:		Dict			  =    TEXT_TO_AUDIO_PARAMS
    a__			:		Dict			  =    TEXT_TO_AUDIO_BATCH_PARAMS
    a__			:		List[Any]			  =    frozenset(
        [
            """num_inference_steps""",
            """num_waveforms_per_prompt""",
            """generator""",
            """latents""",
            """output_type""",
            """return_dict""",
            """callback""",
            """callback_steps""",
        ]      )



    def    _lowercase		(self       :       Union[str, Any]					):
         torch.manual_seed(0					)
         UpperCAmelCase_			       =  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  ,     )
         UpperCAmelCase_			       =  DDIMScheduler(
             beta_start=0.0_00_85  ,     beta_end=0.0_12  ,     beta_schedule="scaled_linear"  ,     clip_sample=__a  ,     set_alpha_to_one=__a  ,     )
         torch.manual_seed(0					)
         UpperCAmelCase_			       =  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_			       =  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_			       =  ClapTextModelWithProjection(__a					)
         UpperCAmelCase_			       =  RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"  ,     model_max_length=77					)

         UpperCAmelCase_			       =  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  ,     )

         UpperCAmelCase_			       =  SpeechTaHifiGan(__a					)

         UpperCAmelCase_			       =  {
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "vocoder": vocoder,
         }
         return components



    def    _lowercase		(self       :       int  ,     __a       :       Optional[Any]  ,     __a       :       Dict=0					):
         if str(__a					).startswith("mps"					):
              UpperCAmelCase_			       =  torch.manual_seed(__a					)
         else:
              UpperCAmelCase_			       =  torch.Generator(device=__a					).manual_seed(__a					)
         UpperCAmelCase_			       =  {
             "prompt": "A hammer hitting a wooden surface",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
         }
         return inputs



    def    _lowercase		(self       :       Optional[Any]					):
         UpperCAmelCase_			       =  "cpu"  # ensure determinism for the device-dependent torch.Generator

         UpperCAmelCase_			       =  self.get_dummy_components()
         UpperCAmelCase_			       =  AudioLDMPipeline(**__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)

         UpperCAmelCase_			       =  self.get_dummy_inputs(__a					)
         UpperCAmelCase_			       =  audioldm_pipe(**__a					)
         UpperCAmelCase_			       =  output.audios[0]

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

         UpperCAmelCase_			       =  audio[:10]
         UpperCAmelCase_			       =  np.array(
             [-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33]					)

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



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  self.get_dummy_components()
         UpperCAmelCase_			       =  AudioLDMPipeline(**__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)

         UpperCAmelCase_			       =  self.get_dummy_inputs(__a					)
         UpperCAmelCase_			       =  3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase_			       =  audioldm_pipe(**__a					)
         UpperCAmelCase_			       =  output.audios[0]

         UpperCAmelCase_			       =  self.get_dummy_inputs(__a					)
         UpperCAmelCase_			       =  3 * [inputs.pop("prompt"					)]

         UpperCAmelCase_			       =  audioldm_pipe.tokenizer(
             __a  ,     padding="max_length"  ,     max_length=audioldm_pipe.tokenizer.model_max_length  ,     truncation=__a  ,     return_tensors="pt"  ,     )
         UpperCAmelCase_			       =  text_inputs["input_ids"].to(__a					)

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

         UpperCAmelCase_			       =  prompt_embeds

         # forward
         UpperCAmelCase_			       =  audioldm_pipe(**__a					)
         UpperCAmelCase_			       =  output.audios[0]

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



    def    _lowercase		(self       :       Optional[Any]					):
         UpperCAmelCase_			       =  self.get_dummy_components()
         UpperCAmelCase_			       =  AudioLDMPipeline(**__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)

         UpperCAmelCase_			       =  self.get_dummy_inputs(__a					)
         UpperCAmelCase_			       =  3 * ["this is a negative prompt"]
         UpperCAmelCase_			       =  negative_prompt
         UpperCAmelCase_			       =  3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase_			       =  audioldm_pipe(**__a					)
         UpperCAmelCase_			       =  output.audios[0]

         UpperCAmelCase_			       =  self.get_dummy_inputs(__a					)
         UpperCAmelCase_			       =  3 * [inputs.pop("prompt"					)]

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

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

              embeds.append(__a					)

         UpperCAmelCase_       ,					UpperCAmelCase_			       =  embeds

         # forward
         UpperCAmelCase_			       =  audioldm_pipe(**__a					)
         UpperCAmelCase_			       =  output.audios[0]

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



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_			       =  self.get_dummy_components()
         UpperCAmelCase_			       =  PNDMScheduler(skip_prk_steps=__a					)
         UpperCAmelCase_			       =  AudioLDMPipeline(**__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)

         UpperCAmelCase_			       =  self.get_dummy_inputs(__a					)
         UpperCAmelCase_			       =  "egg cracking"
         UpperCAmelCase_			       =  audioldm_pipe(**__a  ,     negative_prompt=__a					)
         UpperCAmelCase_			       =  output.audios[0]

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

         UpperCAmelCase_			       =  audio[:10]
         UpperCAmelCase_			       =  np.array(
             [-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32]					)

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



    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_			       =  self.get_dummy_components()
         UpperCAmelCase_			       =  PNDMScheduler(skip_prk_steps=__a					)
         UpperCAmelCase_			       =  AudioLDMPipeline(**__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)

         UpperCAmelCase_			       =  "A hammer hitting a wooden surface"

         # test num_waveforms_per_prompt=1 (default)
         UpperCAmelCase_			       =  audioldm_pipe(__a  ,     num_inference_steps=2					).audios

         assert audios.shape == (1, 256)

         # test num_waveforms_per_prompt=1 (default) for batch of prompts
         UpperCAmelCase_			       =  2
         UpperCAmelCase_			       =  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_			       =  2
         UpperCAmelCase_			       =  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
         UpperCAmelCase_			       =  2
         UpperCAmelCase_			       =  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    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_			       =  self.get_dummy_components()
         UpperCAmelCase_			       =  AudioLDMPipeline(**__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)
         UpperCAmelCase_			       =  audioldm_pipe.vocoder.config.sampling_rate

         UpperCAmelCase_			       =  self.get_dummy_inputs(__a					)
         UpperCAmelCase_			       =  audioldm_pipe(audio_length_in_s=0.0_16  ,     **__a					)
         UpperCAmelCase_			       =  output.audios[0]

         assert audio.ndim == 1
         assert len(__a					) / vocoder_sampling_rate == 0.0_16

         UpperCAmelCase_			       =  audioldm_pipe(audio_length_in_s=0.0_32  ,     **__a					)
         UpperCAmelCase_			       =  output.audios[0]

         assert audio.ndim == 1
         assert len(__a					) / vocoder_sampling_rate == 0.0_32



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  self.get_dummy_components()
         UpperCAmelCase_			       =  AudioLDMPipeline(**__a					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)

         UpperCAmelCase_			       =  ["hey"]

         UpperCAmelCase_			       =  audioldm_pipe(__a  ,     num_inference_steps=1					)
         UpperCAmelCase_			       =  output.audios.shape
         assert audio_shape == (1, 256)

         UpperCAmelCase_			       =  audioldm_pipe.vocoder.config
         config.model_in_dim *= 2
         UpperCAmelCase_			       =  SpeechTaHifiGan(__a					).to(__a					)
         UpperCAmelCase_			       =  audioldm_pipe(__a  ,     num_inference_steps=1					)
         UpperCAmelCase_			       =  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    _lowercase		(self       :       Dict					):
         self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__a					)



    def    _lowercase		(self       :       int					):
         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    _lowercase		(self       :       List[str]					):
         self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a					)




@slow
class 			__A (					unittest.TestCase      ):



    def    _lowercase		(self       :       Optional[int]					):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       Dict  ,     __a       :       Optional[Any]="cpu"  ,     __a       :       Union[str, Any]=torch.floataa  ,     __a       :       Union[str, Any]=0					):
         UpperCAmelCase_			       =  torch.Generator(device=__a					).manual_seed(__a					)
         UpperCAmelCase_			       =  np.random.RandomState(__a					).standard_normal((1, 8, 128, 16)					)
         UpperCAmelCase_			       =  torch.from_numpy(__a					).to(device=__a  ,     dtype=__a					)
         UpperCAmelCase_			       =  {
             "prompt": "A hammer hitting a wooden surface",
             "latents": latents,
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 2.5,
         }
         return inputs



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  AudioLDMPipeline.from_pretrained("cvssp/audioldm"					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)

         UpperCAmelCase_			       =  self.get_inputs(__a					)
         UpperCAmelCase_			       =  25
         UpperCAmelCase_			       =  audioldm_pipe(**__a					).audios[0]

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

         UpperCAmelCase_			       =  audio[77230:77240]
         UpperCAmelCase_			       =  np.array(
             [-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15]					)
         UpperCAmelCase_			       =  np.abs(expected_slice - audio_slice					).max()
         assert max_diff < 1E-2






    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  AudioLDMPipeline.from_pretrained("cvssp/audioldm"					)
         UpperCAmelCase_			       =  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config					)
         UpperCAmelCase_			       =  audioldm_pipe.to(__a					)
         audioldm_pipe.set_progress_bar_config(disable=__a					)

         UpperCAmelCase_			       =  self.get_inputs(__a					)
         UpperCAmelCase_			       =  audioldm_pipe(**__a					).audios[0]

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

         UpperCAmelCase_			       =  audio[27780:27790]
         UpperCAmelCase_			       =  np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12]					)
         UpperCAmelCase_			       =  np.abs(expected_slice - audio_slice					).max()
         assert max_diff < 3E-2









import requests
from bsa import BeautifulSoup






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= BeautifulSoup(requests.get(__UpperCAmelCase							,  params=__UpperCAmelCase       ).content							,  '''html.parser'''       )
 a__												= soup.find('''div'''							,  attrs={'''class''': '''gs_ri'''}       )
 a__												= div.find('''div'''							,  attrs={'''class''': '''gs_fl'''}       ).find_all('''a'''       )
 return anchors[2].get_text()


if __name__ == "__main__":
     a_      :      Optional[Any]    =     {
         'title': (
             'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
             'capacitance, volumetric capacitance, and energy density'
         ),
         'journal': 'Chem. Mater.',
         'volume': 30,
         'pages': '3979-3990',
         'year': 20_18,
         'hl': 'en',
     }
     print(get_citation('https://scholar.google.com/scholar_lookup', params=params))





