


"""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__	(						__lowercase ,     unittest.TestCase):


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

					def 		a__						(	self:						List[Any]	)->    Optional[int]:
											torch.manual_seed(0	)
											lowerCamelCase					:					Dict        =	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     ,		)
											lowerCamelCase					:					List[str]        =	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	)
											lowerCamelCase					:					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	)
											lowerCamelCase					:					List[Any]        =	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     ,		)
											lowerCamelCase					:					Union[str, Any]        =	ClapTextModelWithProjection(__a	)
											lowerCamelCase					:					List[Any]        =	RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""     ,		model_max_length=77	)

											lowerCamelCase					:					List[Any]        =	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=__a     ,		)

											lowerCamelCase					:					Optional[Any]        =	SpeechTaHifiGan(__a	)

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

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

					def 		a__						(	self:						List[Any]	)->    Optional[Any]:
											lowerCamelCase					:					List[Any]        =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

											lowerCamelCase					:					Optional[int]        =	self.get_dummy_components()
											lowerCamelCase					:					List[str]        =	AudioLDMPipeline(**__a	)
											lowerCamelCase					:					Tuple        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)

											lowerCamelCase					:					Optional[int]        =	self.get_dummy_inputs(__a	)
											lowerCamelCase					:					List[Any]        =	audioldm_pipe(**__a	)
											lowerCamelCase					:					Optional[Any]        =	output.audios[0]

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

											lowerCamelCase					:					str        =	audio[:10]
											lowerCamelCase					:					Any        =	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 		a__						(	self:						Tuple	)->    Dict:
											lowerCamelCase					:					str        =	self.get_dummy_components()
											lowerCamelCase					:					int        =	AudioLDMPipeline(**__a	)
											lowerCamelCase					:					Tuple        =	audioldm_pipe.to(__a	)
											lowerCamelCase					:					int        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)

											lowerCamelCase					:					int        =	self.get_dummy_inputs(__a	)
											lowerCamelCase					:					Optional[int]        =	3 * [inputs["""prompt"""]]

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

											lowerCamelCase					:					Optional[int]        =	self.get_dummy_inputs(__a	)
											lowerCamelCase					:					Tuple        =	3 * [inputs.pop("""prompt"""	)]

											lowerCamelCase					:					Dict        =	audioldm_pipe.tokenizer(
											    __a     ,		padding="""max_length"""     ,		max_length=audioldm_pipe.tokenizer.model_max_length     ,		truncation=__a     ,		return_tensors="""pt"""     ,		)
											lowerCamelCase					:					List[Any]        =	text_inputs["""input_ids"""].to(__a	)

											lowerCamelCase					:					List[str]        =	audioldm_pipe.text_encoder(
											    __a     ,		)
											lowerCamelCase					:					Any        =	prompt_embeds.text_embeds
											# additional L_2 normalization over each hidden-state
											lowerCamelCase					:					List[str]        =	F.normalize(__a     ,		dim=-1	)

											lowerCamelCase					:					str        =	prompt_embeds

											# forward
											lowerCamelCase					:					Dict        =	audioldm_pipe(**__a	)
											lowerCamelCase					:					List[Any]        =	output.audios[0]

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

					def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
											lowerCamelCase					:					str        =	self.get_dummy_components()
											lowerCamelCase					:					List[Any]        =	AudioLDMPipeline(**__a	)
											lowerCamelCase					:					List[Any]        =	audioldm_pipe.to(__a	)
											lowerCamelCase					:					Tuple        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)

											lowerCamelCase					:					Optional[int]        =	self.get_dummy_inputs(__a	)
											lowerCamelCase					:					Optional[Any]        =	3 * ["""this is a negative prompt"""]
											lowerCamelCase					:					int        =	negative_prompt
											lowerCamelCase					:					Optional[int]        =	3 * [inputs["""prompt"""]]

											# forward
											lowerCamelCase					:					Any        =	audioldm_pipe(**__a	)
											lowerCamelCase					:					Optional[Any]        =	output.audios[0]

											lowerCamelCase					:					Any        =	self.get_dummy_inputs(__a	)
											lowerCamelCase					:					List[Any]        =	3 * [inputs.pop("""prompt"""	)]

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

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

																	embeds.append(__a	)

											lowerCamelCase					:					Union[str, Any]        =	embeds

											# forward
											lowerCamelCase					:					List[str]        =	audioldm_pipe(**__a	)
											lowerCamelCase					:					List[str]        =	output.audios[0]

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

					def 		a__						(	self:						Optional[Any]	)->    List[str]:
											lowerCamelCase					:					int        =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											lowerCamelCase					:					Dict        =	self.get_dummy_components()
											lowerCamelCase					:					List[Any]        =	PNDMScheduler(skip_prk_steps=__a	)
											lowerCamelCase					:					Optional[Any]        =	AudioLDMPipeline(**__a	)
											lowerCamelCase					:					int        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)

											lowerCamelCase					:					List[Any]        =	self.get_dummy_inputs(__a	)
											lowerCamelCase					:					Any        =	"""egg cracking"""
											lowerCamelCase					:					str        =	audioldm_pipe(**__a     ,		negative_prompt=__a	)
											lowerCamelCase					:					Optional[Any]        =	output.audios[0]

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

											lowerCamelCase					:					int        =	audio[:10]
											lowerCamelCase					:					List[Any]        =	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 		a__						(	self:						List[str]	)->    Union[str, Any]:
											lowerCamelCase					:					int        =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											lowerCamelCase					:					Tuple        =	self.get_dummy_components()
											lowerCamelCase					:					Dict        =	PNDMScheduler(skip_prk_steps=__a	)
											lowerCamelCase					:					Any        =	AudioLDMPipeline(**__a	)
											lowerCamelCase					:					List[Any]        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)

											lowerCamelCase					:					Optional[Any]        =	"""A hammer hitting a wooden surface"""

											# test num_waveforms_per_prompt=1 (default)
											lowerCamelCase					:					Dict        =	audioldm_pipe(__a     ,		num_inference_steps=2	).audios

											assert audios.shape == (1, 256)

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

											assert audios.shape == (batch_size, 256)

											# test num_waveforms_per_prompt for single prompt
											lowerCamelCase					:					Optional[int]        =	2
											lowerCamelCase					:					Dict        =	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
											lowerCamelCase					:					str        =	2
											lowerCamelCase					:					Union[str, Any]        =	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 		a__						(	self:						Optional[Any]	)->    int:
											lowerCamelCase					:					Any        =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											lowerCamelCase					:					str        =	self.get_dummy_components()
											lowerCamelCase					:					str        =	AudioLDMPipeline(**__a	)
											lowerCamelCase					:					Any        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)
											lowerCamelCase					:					List[Any]        =	audioldm_pipe.vocoder.config.sampling_rate

											lowerCamelCase					:					Union[str, Any]        =	self.get_dummy_inputs(__a	)
											lowerCamelCase					:					str        =	audioldm_pipe(audio_length_in_s=0.0_16     ,		**__a	)
											lowerCamelCase					:					int        =	output.audios[0]

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

											lowerCamelCase					:					int        =	audioldm_pipe(audio_length_in_s=0.0_32     ,		**__a	)
											lowerCamelCase					:					Any        =	output.audios[0]

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

					def 		a__						(	self:						Union[str, Any]	)->    int:
											lowerCamelCase					:					Tuple        =	self.get_dummy_components()
											lowerCamelCase					:					int        =	AudioLDMPipeline(**__a	)
											lowerCamelCase					:					List[str]        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)

											lowerCamelCase					:					int        =	["""hey"""]

											lowerCamelCase					:					str        =	audioldm_pipe(__a     ,		num_inference_steps=1	)
											lowerCamelCase					:					Optional[Any]        =	output.audios.shape
											assert audio_shape == (1, 256)

											lowerCamelCase					:					Optional[int]        =	audioldm_pipe.vocoder.config
											config.model_in_dim *= 2
											lowerCamelCase					:					Optional[Any]        =	SpeechTaHifiGan(__a	).to(__a	)
											lowerCamelCase					:					Dict        =	audioldm_pipe(__a     ,		num_inference_steps=1	)
											lowerCamelCase					:					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:						Optional[Any]	)->    int:
											self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__a	)

					def 		a__						(	self:						List[Any]	)->    str:
											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 		a__						(	self:						Dict	)->    str:
											self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a	)


@slow
class 			A__	(						unittest.TestCase):


					"""simple docstring"""

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

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

					def 		a__						(	self:						Tuple	)->    Optional[int]:
											lowerCamelCase					:					Dict        =	AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""	)
											lowerCamelCase					:					Optional[Any]        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)

											lowerCamelCase					:					int        =	self.get_inputs(__a	)
											lowerCamelCase					:					Optional[Any]        =	25
											lowerCamelCase					:					Tuple        =	audioldm_pipe(**__a	).audios[0]

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

											lowerCamelCase					:					Any        =	audio[77_230:77_240]
											lowerCamelCase					:					Any        =	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]	)
											lowerCamelCase					:					int        =	np.abs(expected_slice - audio_slice	).max()
											assert max_diff < 1e-2







					def 		a__						(	self:						Any	)->    List[Any]:
											lowerCamelCase					:					Dict        =	AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""	)
											lowerCamelCase					:					str        =	LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config	)
											lowerCamelCase					:					Optional[int]        =	audioldm_pipe.to(__a	)
											audioldm_pipe.set_progress_bar_config(disable=__a	)

											lowerCamelCase					:					Tuple        =	self.get_inputs(__a	)
											lowerCamelCase					:					int        =	audioldm_pipe(**__a	).audios[0]

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

											lowerCamelCase					:					List[str]        =	audio[27_780:27_790]
											lowerCamelCase					:					Tuple        =	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]	)
											lowerCamelCase					:					int        =	np.abs(expected_slice - audio_slice	).max()
											assert max_diff < 3e-2








"""simple docstring"""


from __future__ import annotations

__lowerCamelCase      :int	    =		10




def   snake_case		(     UpperCamelCase__       :						list[int]    )   ->      list[int]:
      lowerCamelCase					:					int        =	1
      lowerCamelCase					:					Union[str, Any]        =	max(UpperCamelCase__    )
      while placement <= max_digit:
            # declare and initialize empty buckets
            lowerCamelCase					:					list[list]        =	[[] for _ in range(UpperCamelCase__    )]
            # split list_of_ints between the buckets
            for i in list_of_ints:
                  lowerCamelCase					:					Any        =	int((i / placement) % RADIX    )
                  buckets[tmp].append(UpperCamelCase__    )
            # put each buckets' contents into list_of_ints
            lowerCamelCase					:					Dict        =	0
            for b in range(UpperCamelCase__    ):
                  for i in buckets[b]:
                        lowerCamelCase					:					List[str]        =	i
                        a += 1
        # move to next
            placement *= RADIX
      return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()




