


"""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     ,			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"""


import unittest

from knapsack import greedy_knapsack as kp


class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	[10, 20, 30, 40, 50, 60]
           lowerCamelCase					:					Union[str, Any]        =	[2, 4, 6, 8, 10, 12]
           lowerCamelCase					:					Union[str, Any]        =	100
           self.assertEqual(kp.calc_profit(__a     ,		__a     ,		__a	)     ,		210	)

     def 		a__						(	self:						str	)->    str:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)

     def 		a__						(	self:						str	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""Weight can not be negative."""	)

     def 		a__						(	self:						Any	)->    Dict:
           self.assertRaisesRegex(__a     ,		"""Profit can not be negative."""	)

     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)







     def 		a__						(	self:						Optional[Any]	)->    Tuple:
           self.assertRaisesRegex(
               __a     ,		"""The length of profit and weight must be same."""	)


if __name__ == "__main__":
    unittest.main()




