



'''simple docstring'''





import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					snake_case			(UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:Dict     =       DanceDiffusionPipeline
       lowerCAmelCase__	:List[str]     =       UNCONDITIONAL_AUDIO_GENERATION_PARAMS
       lowerCAmelCase__	:Dict     =       PipelineTesterMixin.required_optional_params - {
           "callback",
           "latents",
           "callback_steps",
           "output_type",
           "num_images_per_prompt",
       }
       lowerCAmelCase__	:Optional[Any]     =       UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
       lowerCAmelCase__	:int     =       False
       lowerCAmelCase__	:Union[str, Any]     =       False



       def 				_a   (					self   )	->				Union[str, Any]:
           torch.manual_seed(0   )
           lowercase__									=    UNetaDModel(
               block_out_channels=(32, 32, 64)  ,extra_in_channels=16  ,sample_size=512  ,sample_rate=16_000  ,in_channels=2  ,out_channels=2  ,flip_sin_to_cos=UpperCAmelCase_  ,use_timestep_embedding=UpperCAmelCase_  ,time_embedding_type="fourier"  ,mid_block_type="UNetMidBlock1D"  ,down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")  ,up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")  ,)
           lowercase__									=    IPNDMScheduler()

           lowercase__									=    {
               "unet": unet,
               "scheduler": scheduler,
           }
           return components



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_=0   )	->				Tuple:
           if str(UpperCAmelCase_   ).startswith("mps"   ):
               lowercase__									=    torch.manual_seed(UpperCAmelCase_   )
           else:
               lowercase__									=    torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
           lowercase__									=    {
               "batch_size": 1,
               "generator": generator,
               "num_inference_steps": 4,
           }
           return inputs



       def 				_a   (					self   )	->				Optional[Any]:
           lowercase__									=    "cpu"  # ensure determinism for the device-dependent torch.Generator
           lowercase__									=    self.get_dummy_components()
           lowercase__									=    DanceDiffusionPipeline(**UpperCAmelCase_   )
           lowercase__									=    pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    self.get_dummy_inputs(UpperCAmelCase_   )
           lowercase__									=    pipe(**UpperCAmelCase_   )
           lowercase__									=    output.audios

           lowercase__									=    audio[0, -3:, -3:]

           assert audio.shape == (1, 2, components["unet"].sample_size)
           lowercase__									=    np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]   )
           assert np.abs(audio_slice.flatten() - expected_slice   ).max() < 1E-2



       @skip_mps
       def 				_a   (					self   )	->				Optional[int]:
           return super().test_save_load_local()



       @skip_mps
       def 				_a   (					self   )	->				str:
           return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )



       @skip_mps
       def 				_a   (					self   )	->				Dict:
           return super().test_save_load_optional_components()



       @skip_mps
       def 				_a   (					self   )	->				int:
           return super().test_attention_slicing_forward_pass()







       def 				_a   (					self   )	->				List[Any]:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





@slow
@require_torch_gpu
class 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Union[str, Any]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       def 				_a   (					self   )	->				Dict:
           lowercase__									=    torch_device

           lowercase__									=    DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"   )
           lowercase__									=    pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    torch.manual_seed(0   )
           lowercase__									=    pipe(generator=UpperCAmelCase_  ,num_inference_steps=100  ,audio_length_in_s=4.0_96   )
           lowercase__									=    output.audios

           lowercase__									=    audio[0, -3:, -3:]

           assert audio.shape == (1, 2, pipe.unet.sample_size)
           lowercase__									=    np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]   )

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







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

           lowercase__									=    DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"  ,torch_dtype=torch.floataa   )
           lowercase__									=    pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    torch.manual_seed(0   )
           lowercase__									=    pipe(generator=UpperCAmelCase_  ,num_inference_steps=100  ,audio_length_in_s=4.0_96   )
           lowercase__									=    output.audios

           lowercase__									=    audio[0, -3:, -3:]

           assert audio.shape == (1, 2, pipe.unet.sample_size)
           lowercase__									=    np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]   )

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









'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    str							):





    '''simple docstring'''


    return " ".join(
        "".join(word[::-1]							) if len(_snake_case							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(reverse_long_words("Hey wollef sroirraw"))




