

import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						_lowerCAmelCase      :    List[Any]						       =    True
						_lowerCAmelCase      :    Union[str, Any]						       =    ""
except Exception as e:
						_lowerCAmelCase      :    List[Any]						       =    False
						_lowerCAmelCase      :    Tuple						       =    (
						    f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
						)


from PIL import Image  # noqa: E402






class 			__magic_name__					(    __UpperCAmelCase						,					__UpperCAmelCase   ):
			SCREAMING_SNAKE_CASE							     =		"""mel_config.json"""





			@register_to_config
			def __init__(	self      ,						__snake_case = 256      ,						__snake_case = 256      ,						__snake_case = 2_2050      ,						__snake_case = 2048      ,						__snake_case = 512      ,						__snake_case = 80      ,						__snake_case = 32      ,						)     ->				str:




						'''simple docstring'''
						__a						   =hop_length
						__a						   =sample_rate
						__a						   =n_fft
						__a						   =top_db
						__a						   =n_iter
						self.set_resolution(lowerCamelCase__      ,						lowerCamelCase__      )
						__a						   =None

						if not _librosa_can_be_imported:
									raise ValueError(_import_error      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      )     ->				Dict:




						'''simple docstring'''
						__a						   =x_res
						__a						   =y_res
						__a						   =self.y_res
						__a						   =self.x_res * self.hop_length - 1





			def 	__magic_name__					(	self      ,						__snake_case = None      ,						__snake_case = None      )     ->				Optional[int]:




						'''simple docstring'''



						if audio_file is not None:
									__a   ,					__a						   =librosa.load(lowerCamelCase__      ,						mono=lowerCamelCase__      ,						sr=self.sr      )
						else:
									__a						   =raw_audio

						# Pad with silence if necessary.
						if len(self.audio      ) < self.x_res * self.hop_length:
									__a						   =np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio      ),)      )]      )





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						return len(self.audio      ) // self.slice_size





			def 	__magic_name__					(	self      ,						__snake_case = 0      )     ->				np.ndarray:




						'''simple docstring'''
						return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						return self.sr





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Image.Image:




						'''simple docstring'''
						__a						   =librosa.feature.melspectrogram(
						    y=self.get_audio_slice(lowerCamelCase__      )      ,						sr=self.sr      ,						n_fft=self.n_fft      ,						hop_length=self.hop_length      ,						n_mels=self.n_mels      )
						__a						   =librosa.power_to_db(lowerCamelCase__      ,						ref=np.max      ,						top_db=self.top_db      )
						__a						   =(((log_S + self.top_db) * 255 / self.top_db).clip(0      ,						255      ) + 0.5).astype(np.uinta      )
						__a						   =Image.fromarray(lowerCamelCase__      )
						return image





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				np.ndarray:




						'''simple docstring'''
						__a						   =np.frombuffer(image.tobytes()      ,						dtype='uint8'      ).reshape((image.height, image.width)      )
						__a						   =bytedata.astype('float'      ) * self.top_db / 255 - self.top_db
						__a						   =librosa.db_to_power(lowerCamelCase__      )
						__a						   =librosa.feature.inverse.mel_to_audio(
						    lowerCamelCase__      ,						sr=self.sr      ,						n_fft=self.n_fft      ,						hop_length=self.hop_length      ,						n_iter=self.n_iter      )
						return audio









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase      :    List[str]						       =    False






@nightly
@require_torch_gpu
class 			__magic_name__					(    unittest.TestCase   ):





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(__snake_case      )
									__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained(__snake_case      )

						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =generator.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   ='cyberpunk 2077'
						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt=__snake_case      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=50      ,						output_type='numpy'      ,						).images

						__a						   =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a						   =np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]      )

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






