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

				__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = True
				__SCREAMING_SNAKE_CASE						:    Optional[Any]     = ""
except Exception as e:
				__SCREAMING_SNAKE_CASE						:    int     = False
				__SCREAMING_SNAKE_CASE						:    List[str]     = (
				    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    lowercase_    (     _a						, _a		):
							_lowerCamelCase						=		'mel_config.json'



							@register_to_config
							def __init__(						self      ,				lowercase_ = 256      ,				lowercase_ = 256      ,				lowercase_ = 22_050      ,				lowercase_ = 2_048      ,				lowercase_ = 512      ,				lowercase_ = 80      ,				lowercase_ = 32      ,				):
													_snake_case						:     List[str]				=    hop_length
													_snake_case						:     Optional[Any]				=    sample_rate
													_snake_case						:     Optional[Any]				=    n_fft
													_snake_case						:     Dict				=    top_db
													_snake_case						:     List[Any]				=    n_iter
													self.set_resolution(_UpperCAmelCase      ,				_UpperCAmelCase				)
													_snake_case						:     Optional[int]				=    None

													if not _librosa_can_be_imported:
																			raise ValueError(_import_error				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     Dict				=    x_res
													_snake_case						:     Union[str, Any]				=    y_res
													_snake_case						:     Any				=    self.y_res
													_snake_case						:     Optional[Any]				=    self.x_res * self.hop_length - 1



							def        UpperCamelCase       (						self      ,				lowercase_ = None      ,				lowercase_ = None				):




													if audio_file is not None:
																			_snake_case						:     Dict				=    librosa.load(_UpperCAmelCase      ,				mono=_UpperCAmelCase      ,				sr=self.sr				)
													else:
																			_snake_case						:     Any				=    raw_audio

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



							def        UpperCamelCase       (						self				):
													return len(self.audio				) // self.slice_size



							def        UpperCamelCase       (						self      ,				lowercase_ = 0				):
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]



							def        UpperCamelCase       (						self				):
													return self.sr



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     Union[str, Any]				=    librosa.feature.melspectrogram(
													    y=self.get_audio_slice(_UpperCAmelCase				)      ,				sr=self.sr      ,				n_fft=self.n_fft      ,				hop_length=self.hop_length      ,				n_mels=self.n_mels				)
													_snake_case						:     List[Any]				=    librosa.power_to_db(_UpperCAmelCase      ,				ref=np.max      ,				top_db=self.top_db				)
													_snake_case						:     List[Any]				=    (((log_S + self.top_db) * 255 / self.top_db).clip(0      ,				255				) + 0.5).astype(np.uinta				)
													_snake_case						:     Optional[Any]				=    Image.fromarray(_UpperCAmelCase				)
													return image



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     Optional[int]				=    np.frombuffer(image.tobytes()      ,				dtype="uint8"				).reshape((image.height, image.width)				)
													_snake_case						:     Optional[int]				=    bytedata.astype("float"				) * self.top_db / 255 - self.top_db
													_snake_case						:     List[Any]				=    librosa.db_to_power(_UpperCAmelCase				)
													_snake_case						:     Optional[int]				=    librosa.feature.inverse.mel_to_audio(
													    _UpperCAmelCase      ,				sr=self.sr      ,				n_fft=self.n_fft      ,				hop_length=self.hop_length      ,				n_iter=self.n_iter				)
													return audio





import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()
class 				__snake_case     (unittest.TestCase						):
				lowerCAmelCase__			=   "hf-internal-testing/tiny-stable-diffusion-pipe"
				lowerCAmelCase__			=   1
				lowerCAmelCase__			=   7_7
				lowerCAmelCase__			=   3_2
				lowerCAmelCase__			=   4
				lowerCAmelCase__			=   lowerCAmelCase__			=   6_4
				lowerCAmelCase__			=   torch.manual_seed(0						)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						Optional[Any]=0.9999					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					UNetaDConditionModel.from_pretrained(self.model_id						,		subfolder="""unet"""					)
								_lowerCAmelCase			:			Optional[Any]        =					unet.to(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					EMAModel(unet.parameters()						,		decay=_UpperCAmelCase						,		model_cls=_UpperCAmelCase						,		model_config=unet.config					)
								return unet, ema_unet



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					torch.randn(
								    self.batch_size						,		self.num_in_channels						,		self.latent_height						,		self.latent_width						,		generator=self.generator					).to(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					torch.randint(0						,		1000						,		size=(self.batch_size,)						,		generator=self.generator					).to(_UpperCAmelCase					)
								_lowerCAmelCase			:			Any        =					torch.randn(
								    self.batch_size						,		self.prompt_length						,		self.text_encoder_hidden_dim						,		generator=self.generator					).to(_UpperCAmelCase					)
								return noisy_latents, timesteps, encoder_hidden_states



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						Dict					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					{}
								for k, param in unet.state_dict().items():
												_lowerCAmelCase			:			List[Any]        =					torch.randn_like(_UpperCAmelCase					) + (param * torch.randn_like(_UpperCAmelCase					))
												updated_state_dict.update({k: updated_param}					)
								unet.load_state_dict(_UpperCAmelCase					)
								return unet



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[int]        =					self.get_models()
								# Take the first (hypothetical) EMA step.
								ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 1

								# Take two more.
								for _ in range(2					):
												ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 3



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					self.get_models()
								# Since the `unet` is not being updated (i.e., backprop'd)
								# there won't be any difference between the `params` of `unet`
								# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
								ema_unet.step(unet.parameters()					)
								_lowerCAmelCase			:			Optional[int]        =					list(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)

								# The above holds true even if we call `ema.step()` multiple times since
								# `unet` params are still not being updated.
								for _ in range(4					):
												ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					self.get_models()
								# Here we simulate the parameter updates for `unet`. Since there might
								# be some parameters which are initialized to zero we take extra care to
								# initialize their values to something non-zero before the multiplication.
								_lowerCAmelCase			:			str        =					self.simulate_backprop(_UpperCAmelCase					)

								# Take the EMA step.
								ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

								# Now the EMA'd parameters won't be equal to the original model parameters.
								_lowerCAmelCase			:			Tuple        =					list(unet_pseudo_updated_step_one.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)

								# Ensure this is the case when we take multiple EMA steps.
								for _ in range(4					):
												ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			str        =					self.get_models()

								# First backprop + EMA
								_lowerCAmelCase			:			Optional[int]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_one.parameters()					)
								_lowerCAmelCase			:			List[str]        =					ema_unet.shadow_params

								# Second backprop + EMA
								_lowerCAmelCase			:			Optional[int]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_two.parameters()					)
								_lowerCAmelCase			:			Tuple        =					ema_unet.shadow_params

								for step_one, step_two in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			int        =					self.get_models(decay=0.0					)
								_lowerCAmelCase			:			Dict        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_one.parameters()					)
								_lowerCAmelCase			:			Any        =					ema_unet.shadow_params

								_lowerCAmelCase			:			List[Any]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_two.parameters()					)
								_lowerCAmelCase			:			int        =					ema_unet.shadow_params

								for step_one, step_two in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				@skip_mps
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.get_models()
								_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdir:
												ema_unet.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			Union[str, Any]        =					UNetaDConditionModel.from_pretrained(_UpperCAmelCase						,		model_cls=_UpperCAmelCase					)
												_lowerCAmelCase			:			Any        =					loaded_unet.to(unet.device					)

								# Since no EMA step has been performed the outputs should match.
								_lowerCAmelCase			:			str        =					unet(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					).sample
								_lowerCAmelCase			:			Union[str, Any]        =					loaded_unet(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					).sample

								assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase						,		atol=1E-4					)

