"""simple docstring"""







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      _lowercase						(				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     A	(       self    :      Union[str, Any]    ,      _lowercase    :      Union[str, Any]=0.9999       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						UNetaDConditionModel.from_pretrained(self.model_id    ,      subfolder="""unet"""       )
										UpperCAmelCase__   		=						unet.to(_lowercase       )
										UpperCAmelCase__   		=						EMAModel(unet.parameters()    ,      decay=_lowercase    ,      model_cls=_lowercase    ,      model_config=unet.config       )
										return unet, ema_unet

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						torch.randn(
										    self.batch_size    ,      self.num_in_channels    ,      self.latent_height    ,      self.latent_width    ,      generator=self.generator       ).to(_lowercase       )
										UpperCAmelCase__   		=						torch.randint(0    ,      1_0_0_0    ,      size=(self.batch_size,)    ,      generator=self.generator       ).to(_lowercase       )
										UpperCAmelCase__   		=						torch.randn(
										    self.batch_size    ,      self.prompt_length    ,      self.text_encoder_hidden_dim    ,      generator=self.generator       ).to(_lowercase       )
										return noisy_latents, timesteps, encoder_hidden_states

				def     A	(       self    :      Tuple    ,      _lowercase    :      Tuple       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						{}
										for k, param in unet.state_dict().items():
																UpperCAmelCase__   		=						torch.randn_like(_lowercase       ) + (param * torch.randn_like(_lowercase       ))
																updated_state_dict.update({k: updated_param}       )
										unet.load_state_dict(_lowercase       )
										return unet

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						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     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						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()       )
										UpperCAmelCase__   		=						list(unet.parameters()       )
										for s_param, param in zip(ema_unet.shadow_params    ,      _lowercase       ):
																assert torch.allclose(_lowercase    ,      _lowercase       )

										# 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    ,      _lowercase       ):
																assert torch.allclose(_lowercase    ,      _lowercase       )

				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						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.
										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )

										# 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.
										UpperCAmelCase__   		=						list(unet_pseudo_updated_step_one.parameters()       )
										for s_param, param in zip(ema_unet.shadow_params    ,      _lowercase       ):
																assert ~torch.allclose(_lowercase    ,      _lowercase       )

										# 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    ,      _lowercase       ):
																assert ~torch.allclose(_lowercase    ,      _lowercase       )

				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										# If we call EMA step after a backpropagation consecutively for two times,
										# the shadow params from those two steps should be different.
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_models()

										# First backprop + EMA
										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )
										ema_unet.step(unet_step_one.parameters()       )
										UpperCAmelCase__   		=						ema_unet.shadow_params

										# Second backprop + EMA
										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )
										ema_unet.step(unet_step_two.parameters()       )
										UpperCAmelCase__   		=						ema_unet.shadow_params

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

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										# If there's no decay even if there are backprops, EMA steps
										# won't take any effect i.e., the shadow params would remain the
										# same.
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_models(decay=0.0       )
										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )
										ema_unet.step(unet_step_one.parameters()       )
										UpperCAmelCase__   		=						ema_unet.shadow_params

										UpperCAmelCase__   		=						self.simulate_backprop(_lowercase       )
										ema_unet.step(unet_step_two.parameters()       )
										UpperCAmelCase__   		=						ema_unet.shadow_params

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

				@skip_mps
				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_models()
										UpperCAmelCase__		, UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_dummy_inputs()

										with tempfile.TemporaryDirectory() as tmpdir:
																ema_unet.save_pretrained(_lowercase       )
																UpperCAmelCase__   		=						UNetaDConditionModel.from_pretrained(_lowercase    ,      model_cls=_lowercase       )
																UpperCAmelCase__   		=						loaded_unet.to(unet.device       )

										# Since no EMA step has been performed the outputs should match.
										UpperCAmelCase__   		=						unet(_lowercase    ,      _lowercase    ,      _lowercase       ).sample
										UpperCAmelCase__   		=						loaded_unet(_lowercase    ,      _lowercase    ,      _lowercase       ).sample

										assert torch.allclose(_lowercase    ,      _lowercase    ,      atol=1e-4       )


"""simple docstring"""







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

						UpperCamelCase_    :    Union[str, Any]											=						True
						UpperCamelCase_    :    int											=						""""""
except Exception as e:
						UpperCamelCase_    :    Any											=						False
						UpperCamelCase_    :    Any											=						(
						    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						(				__lowerCamelCase , __lowerCamelCase					):
				lowerCamelCase__									=    'mel_config.json'

				@register_to_config
				def __init__(       self    :      int    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_2_0_5_0    ,      _lowercase    :      int = 2_0_4_8    ,      _lowercase    :      int = 5_1_2    ,      _lowercase    :      int = 8_0    ,      _lowercase    :      int = 3_2    ,      ):


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

										if not _librosa_can_be_imported:
																raise ValueError(_import_error       )

				def     A	(       self    :      Any    ,      _lowercase    :      int    ,      _lowercase    :      int       ):


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

				def     A	(       self    :      Dict    ,      _lowercase    :      str = None    ,      _lowercase    :      np.ndarray = None       ):


										'''simple docstring'''
										if audio_file is not None:
																UpperCAmelCase__		, UpperCAmelCase__   		=						librosa.load(_lowercase    ,      mono=_lowercase    ,      sr=self.sr       )
										else:
																UpperCAmelCase__   		=						raw_audio

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

				def     A	(       self    :      int       ):


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

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      int = 0       ):


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

				def     A	(       self    :      Dict       ):


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

				def     A	(       self    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						librosa.feature.melspectrogram(
										    y=self.get_audio_slice(_lowercase       )    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_mels=self.n_mels       )
										UpperCAmelCase__   		=						librosa.power_to_db(_lowercase    ,      ref=np.max    ,      top_db=self.top_db       )
										UpperCAmelCase__   		=						(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0    ,      2_5_5       ) + 0.5).astype(np.uinta       )
										UpperCAmelCase__   		=						Image.fromarray(_lowercase       )
										return image

				def     A	(       self    :      Dict    ,      _lowercase    :      Image.Image       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						np.frombuffer(image.tobytes()    ,      dtype="""uint8"""       ).reshape((image.height, image.width)       )
										UpperCAmelCase__   		=						bytedata.astype("""float"""       ) * self.top_db / 2_5_5 - self.top_db
										UpperCAmelCase__   		=						librosa.db_to_power(_lowercase       )
										UpperCAmelCase__   		=						librosa.feature.inverse.mel_to_audio(
										    _lowercase    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_iter=self.n_iter       )
										return audio

