
'''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__				:	Any					=						True
							UpperCAmelCase__				:	Optional[Any]					=						""
except Exception as e:
							UpperCAmelCase__				:	Optional[int]					=						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 				A       (		SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Dict				       =       'mel_config.json'





	@register_to_config
	def __init__(   self     :    str ,							__magic_name__     :    int = 256 ,							__magic_name__     :    int = 256 ,							__magic_name__     :    List[Any] = 22050 ,							__magic_name__     :    List[Any] = 2048 ,							__magic_name__     :    Union[str, Any] = 512 ,							__magic_name__     :    Any = 80 ,							__magic_name__     :    Dict = 32 ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							hop_length
				lowerCAmelCase__									=							sample_rate
				lowerCAmelCase__									=							n_fft
				lowerCAmelCase__									=							top_db
				lowerCAmelCase__									=							n_iter
				self.set_resolution(__magic_name__ ,							__magic_name__							)
				lowerCAmelCase__									=							None

				if not _librosa_can_be_imported:
							raise ValueError(_import_error							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Tuple ,							__magic_name__     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							x_res
				lowerCAmelCase__									=							y_res
				lowerCAmelCase__									=							self.y_res
				lowerCAmelCase__									=							self.x_res * self.hop_length - 1





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    List[Any] = None ,							__magic_name__     :    Any = None							):


				"""simple docstring"""

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

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





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


				"""simple docstring"""

				return len(self.audio							) // self.slice_size





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    Optional[int] = 0							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				return self.sr





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

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







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

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







'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCAmelCase__				:	Tuple					=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Dict				       =       ['safety_checker', 'feature_extractor']





	def __init__(   self     :    Optional[Any] ,							__magic_name__     :    AutoencoderKL ,							__magic_name__     :    CLIPTextModel ,							__magic_name__     :    CLIPTokenizer ,							__magic_name__     :    UNetaDConditionModel ,							__magic_name__     :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,							__magic_name__     :    StableDiffusionSafetyChecker ,							__magic_name__     :    CLIPImageProcessor ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				super().__init__()
				if hasattr(scheduler.config ,							"steps_offset"							) and scheduler.config.steps_offset != 1:
							lowerCAmelCase__									=							(
							    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
							    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
							    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
							    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
							    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
							    " file"
							)
							deprecate("steps_offset!=1" ,							"1.0.0" ,							__magic_name__ ,							standard_warn=__magic_name__							)
							lowerCAmelCase__									=							dict(scheduler.config							)
							lowerCAmelCase__									=							1
							lowerCAmelCase__									=							FrozenDict(__magic_name__							)

				self.register_modules(
				    vae=__magic_name__ ,							text_encoder=__magic_name__ ,							tokenizer=__magic_name__ ,							unet=__magic_name__ ,							scheduler=__magic_name__ ,							safety_checker=__magic_name__ ,							feature_extractor=__magic_name__ ,							)
				self.register_to_config(requires_safety_checker=__magic_name__							)





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				return {k: getattr(self ,							__magic_name__							) for k in self.config.keys() if not k.startswith("_"							)}





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[Union[str, int]] = "auto"							):


				"""simple docstring"""

				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							lowerCAmelCase__									=							self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__magic_name__							)





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


				"""simple docstring"""

				self.enable_attention_slicing(__magic_name__							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionInpaintPipelineLegacy(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							mask_image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							)





	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    Union[torch.FloatTensor, PIL.Image.Image] ,							__magic_name__     :    float = 0.8 ,							__magic_name__     :    Optional[int] = 50 ,							__magic_name__     :    Optional[float] = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    Optional[float] = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							**__magic_name__     :    List[str] ,							):


				"""simple docstring"""

				return StableDiffusionImgaImgPipeline(**self.components							)(
				    prompt=__magic_name__ ,							image=__magic_name__ ,							strength=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)







	@torch.no_grad()
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Union[str, List[str]] ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 512 ,							__magic_name__     :    int = 50 ,							__magic_name__     :    float = 7.5 ,							__magic_name__     :    Optional[Union[str, List[str]]] = None ,							__magic_name__     :    Optional[int] = 1 ,							__magic_name__     :    float = 0.0 ,							__magic_name__     :    Optional[torch.Generator] = None ,							__magic_name__     :    Optional[torch.FloatTensor] = None ,							__magic_name__     :    Optional[str] = "pil" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				return StableDiffusionPipeline(**self.components							)(
				    prompt=__magic_name__ ,							height=__magic_name__ ,							width=__magic_name__ ,							num_inference_steps=__magic_name__ ,							guidance_scale=__magic_name__ ,							negative_prompt=__magic_name__ ,							num_images_per_prompt=__magic_name__ ,							eta=__magic_name__ ,							generator=__magic_name__ ,							latents=__magic_name__ ,							output_type=__magic_name__ ,							return_dict=__magic_name__ ,							callback=__magic_name__ ,							callback_steps=__magic_name__ ,							)





