"""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__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						str													=		AudioLDMPipeline
						lowerCAmelCase__  :						Union[str, Any]													=		TEXT_TO_AUDIO_PARAMS
						lowerCAmelCase__  :						int													=		TEXT_TO_AUDIO_BATCH_PARAMS
						lowerCAmelCase__  :						str													=		frozenset(
						    [
						        'num_inference_steps',
						        'num_waveforms_per_prompt',
						        'generator',
						        'latents',
						        'output_type',
						        'return_dict',
						        'callback',
						        'callback_steps',
						    ] )





						def 				snake_case__	(       self     )					->				str:
													torch.manual_seed(0     )
													A__	     =							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__	     =							DDIMScheduler(
													    beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,beta_schedule='scaled_linear'		,clip_sample=__UpperCAmelCase		,set_alpha_to_one=__UpperCAmelCase		,)
													torch.manual_seed(0     )
													A__	     =							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__	     =							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=10_00		,projection_dim=32		,)
													A__	     =							ClapTextModelWithProjection(__UpperCAmelCase     )
													A__	     =							RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'		,model_max_length=77     )

													A__	     =							SpeechTaHifiGanConfig(
													    model_in_dim=8		,sampling_rate=1_60_00		,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__	     =							SpeechTaHifiGan(__UpperCAmelCase     )

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





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=0     )					->				List[Any]:
													if str(__UpperCAmelCase     ).startswith('mps'     ):
																				A__	     =							torch.manual_seed(__UpperCAmelCase     )
													else:
																				A__	     =							torch.Generator(device=__UpperCAmelCase     ).manual_seed(__UpperCAmelCase     )
													A__	     =							{
													    'prompt': 'A hammer hitting a wooden surface',
													    'generator': generator,
													    'num_inference_steps': 2,
													    'guidance_scale': 6.0,
													}
													return inputs





						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							'cpu'  # ensure determinism for the device-dependent torch.Generator

													A__	     =							self.get_dummy_components()
													A__	     =							AudioLDMPipeline(**__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							self.get_dummy_inputs(__UpperCAmelCase     )
													A__	     =							audioldm_pipe(**__UpperCAmelCase     )
													A__	     =							output.audios[0]

													assert audio.ndim == 1
													assert len(__UpperCAmelCase     ) == 2_56

													A__	     =							audio[:10]
													A__	     =							np.array(
													    [-0.0_0_5_0, 0.0_0_5_0, -0.0_0_6_0, 0.0_0_3_3, -0.0_0_2_6, 0.0_0_3_3, -0.0_0_2_7, 0.0_0_3_3, -0.0_0_2_8, 0.0_0_3_3]     )

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





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							self.get_dummy_components()
													A__	     =							AudioLDMPipeline(**__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							self.get_dummy_inputs(__UpperCAmelCase     )
													A__	     =							3 * [inputs['prompt']]

													# forward
													A__	     =							audioldm_pipe(**__UpperCAmelCase     )
													A__	     =							output.audios[0]

													A__	     =							self.get_dummy_inputs(__UpperCAmelCase     )
													A__	     =							3 * [inputs.pop('prompt'     )]

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

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

													A__	     =							prompt_embeds

													# forward
													A__	     =							audioldm_pipe(**__UpperCAmelCase     )
													A__	     =							output.audios[0]

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





						def 				snake_case__	(       self     )					->				str:
													A__	     =							self.get_dummy_components()
													A__	     =							AudioLDMPipeline(**__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

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

													# forward
													A__	     =							audioldm_pipe(**__UpperCAmelCase     )
													A__	     =							output.audios[0]

													A__	     =							self.get_dummy_inputs(__UpperCAmelCase     )
													A__	     =							3 * [inputs.pop('prompt'     )]

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

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

																				embeds.append(__UpperCAmelCase     )

													A__     ,  A__	     =							embeds

													# forward
													A__	     =							audioldm_pipe(**__UpperCAmelCase     )
													A__	     =							output.audios[0]

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





						def 				snake_case__	(       self     )					->				str:
													A__	     =							'cpu'  # ensure determinism for the device-dependent torch.Generator
													A__	     =							self.get_dummy_components()
													A__	     =							PNDMScheduler(skip_prk_steps=__UpperCAmelCase     )
													A__	     =							AudioLDMPipeline(**__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

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

													assert audio.ndim == 1
													assert len(__UpperCAmelCase     ) == 2_56

													A__	     =							audio[:10]
													A__	     =							np.array(
													    [-0.0_0_5_1, 0.0_0_5_0, -0.0_0_6_0, 0.0_0_3_4, -0.0_0_2_6, 0.0_0_3_3, -0.0_0_2_7, 0.0_0_3_3, -0.0_0_2_8, 0.0_0_3_2]     )

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





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							'cpu'  # ensure determinism for the device-dependent torch.Generator
													A__	     =							self.get_dummy_components()
													A__	     =							PNDMScheduler(skip_prk_steps=__UpperCAmelCase     )
													A__	     =							AudioLDMPipeline(**__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'A hammer hitting a wooden surface'

													# test num_waveforms_per_prompt=1 (default)
													A__	     =							audioldm_pipe(__UpperCAmelCase		,num_inference_steps=2     ).audios

													assert audios.shape == (1, 2_56)

													# 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_56)

													# test num_waveforms_per_prompt for single prompt
													A__	     =							2
													A__	     =							audioldm_pipe(__UpperCAmelCase		,num_inference_steps=2		,num_waveforms_per_prompt=__UpperCAmelCase     ).audios

													assert audios.shape == (num_waveforms_per_prompt, 2_56)

													# 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=__UpperCAmelCase     ).audios

													assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							'cpu'  # ensure determinism for the device-dependent torch.Generator
													A__	     =							self.get_dummy_components()
													A__	     =							AudioLDMPipeline(**__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )
													A__	     =							audioldm_pipe.vocoder.config.sampling_rate

													A__	     =							self.get_dummy_inputs(__UpperCAmelCase     )
													A__	     =							audioldm_pipe(audio_length_in_s=0.0_1_6		,**__UpperCAmelCase     )
													A__	     =							output.audios[0]

													assert audio.ndim == 1
													assert len(__UpperCAmelCase     ) / vocoder_sampling_rate == 0.0_1_6

													A__	     =							audioldm_pipe(audio_length_in_s=0.0_3_2		,**__UpperCAmelCase     )
													A__	     =							output.audios[0]

													assert audio.ndim == 1
													assert len(__UpperCAmelCase     ) / vocoder_sampling_rate == 0.0_3_2





						def 				snake_case__	(       self     )					->				str:
													A__	     =							self.get_dummy_components()
													A__	     =							AudioLDMPipeline(**__UpperCAmelCase     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							['hey']

													A__	     =							audioldm_pipe(__UpperCAmelCase		,num_inference_steps=1     )
													A__	     =							output.audios.shape
													assert audio_shape == (1, 2_56)

													A__	     =							audioldm_pipe.vocoder.config
													config.model_in_dim *= 2
													A__	     =							SpeechTaHifiGan(__UpperCAmelCase     ).to(__UpperCAmelCase     )
													A__	     =							audioldm_pipe(__UpperCAmelCase		,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_56)





						def 				snake_case__	(       self     )					->				Tuple:
													self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				List[str]:
													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 				snake_case__	(       self     )					->				Any:
													self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCAmelCase     )






@slow
class     UpperCamelCase__(			unittest.TestCase ):





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





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase="cpu"		,__UpperCAmelCase=torch.floataa		,__UpperCAmelCase=0     )					->				Union[str, Any]:
													A__	     =							torch.Generator(device=__UpperCAmelCase     ).manual_seed(__UpperCAmelCase     )
													A__	     =							np.random.RandomState(__UpperCAmelCase     ).standard_normal((1, 8, 1_28, 16)     )
													A__	     =							torch.from_numpy(__UpperCAmelCase     ).to(device=__UpperCAmelCase		,dtype=__UpperCAmelCase     )
													A__	     =							{
													    'prompt': 'A hammer hitting a wooden surface',
													    'latents': latents,
													    'generator': generator,
													    'num_inference_steps': 3,
													    'guidance_scale': 2.5,
													}
													return inputs





						def 				snake_case__	(       self     )					->				Any:
													A__	     =							AudioLDMPipeline.from_pretrained('cvssp/audioldm'     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							self.get_inputs(__UpperCAmelCase     )
													A__	     =							25
													A__	     =							audioldm_pipe(**__UpperCAmelCase     ).audios[0]

													assert audio.ndim == 1
													assert len(__UpperCAmelCase     ) == 8_19_20

													A__	     =							audio[7_72_30:7_72_40]
													A__	     =							np.array(
													    [-0.4_8_8_4, -0.4_6_0_7, 0.0_0_2_3, 0.5_0_0_7, 0.5_8_9_6, 0.5_1_5_1, 0.3_8_1_3, -0.0_2_0_8, -0.3_6_8_7, -0.4_3_1_5]     )
													A__	     =							np.abs(expected_slice - audio_slice     ).max()
													assert max_diff < 1e-2





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							AudioLDMPipeline.from_pretrained('cvssp/audioldm'     )
													A__	     =							LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config     )
													A__	     =							audioldm_pipe.to(__UpperCAmelCase     )
													audioldm_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							self.get_inputs(__UpperCAmelCase     )
													A__	     =							audioldm_pipe(**__UpperCAmelCase     ).audios[0]

													assert audio.ndim == 1
													assert len(__UpperCAmelCase     ) == 8_19_20

													A__	     =							audio[2_77_80:2_77_90]
													A__	     =							np.array([-0.2_1_3_1, -0.0_8_7_3, -0.0_1_2_4, -0.0_1_8_9, 0.0_5_6_9, 0.1_3_7_3, 0.1_8_8_3, 0.2_8_8_6, 0.3_2_9_7, 0.2_2_1_2]     )
													A__	     =							np.abs(expected_slice - audio_slice     ).max()
													assert max_diff < 3e-2






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__     ,  A__	     =							[], []
							while len(UpperCamelCase__				) > 1:
														A__     ,  A__	     =							min(UpperCamelCase__				), max(UpperCamelCase__				)
														start.append(UpperCamelCase__				)
														end.append(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
							end.reverse()
							return start + collection + end


if __name__ == "__main__":
				__lowerCamelCase											=  input("Enter numbers separated by a comma:\n").strip()
				__lowerCamelCase											=  [int(item) for item in user_input.split(",")]
				print(*merge_sort(unsorted), sep=",")





