



import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


__A       =						logging.get_logger(__name__)

__A       =						{
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple=None						,  **__UpperCAmelCase     :						str					)      ->			Any:
							logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""					)
							UpperCAmelCase_=       model
							UpperCAmelCase_=       kwargs.get("""model_save_dir"""						,  __UpperCAmelCase					)
							UpperCAmelCase_=       kwargs.get("""latest_model_name"""						,  __UpperCAmelCase					)




	def __call__(     self     :						Optional[int]						,  **__UpperCAmelCase     :						Dict					)      ->			Dict:
							UpperCAmelCase_=       {k: np.array(__UpperCAmelCase					) for k, v in kwargs.items()}
							return self.model.run(__UpperCAmelCase						,  __UpperCAmelCase					)




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						Union[str, Path]						,  __UpperCAmelCase     :						str=None						,  __UpperCAmelCase     :						Tuple=None					)      ->			Optional[int]:
							if provider is None:
													logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""					)
													UpperCAmelCase_=       """CPUExecutionProvider"""

							return ort.InferenceSession(__UpperCAmelCase						,  providers=[provider]						,  sess_options=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Union[str, Path]						,  __UpperCAmelCase     :						Optional[str] = None						,  **__UpperCAmelCase     :						Tuple					)      ->			Dict:
							UpperCAmelCase_=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

							UpperCAmelCase_=       self.model_save_dir.joinpath(self.latest_model_name					)
							UpperCAmelCase_=       Path(__UpperCAmelCase					).joinpath(__UpperCAmelCase					)
							try:
													shutil.copyfile(__UpperCAmelCase						,  __UpperCAmelCase					)
							except shutil.SameFileError:
													pass

							# copy external weights (for models >2GB)
							UpperCAmelCase_=       self.model_save_dir.joinpath(__UpperCAmelCase					)
							if src_path.exists():
													UpperCAmelCase_=       Path(__UpperCAmelCase					).joinpath(__UpperCAmelCase					)
													try:
																			shutil.copyfile(__UpperCAmelCase						,  __UpperCAmelCase					)
													except shutil.SameFileError:
																			pass




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						Union[str, os.PathLike]						,  **__UpperCAmelCase     :						List[str]						,  )      ->			str:
							if os.path.isfile(__UpperCAmelCase					):
													logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""					)
													return

							os.makedirs(__UpperCAmelCase						,  exist_ok=__UpperCAmelCase					)

							# saving model weights/files
							self._save_pretrained(__UpperCAmelCase						,  **__UpperCAmelCase					)




	@classmethod
	def   _SCREAMING_SNAKE_CASE		(     cls     :						Tuple						,  __UpperCAmelCase     :						Union[str, Path]						,  __UpperCAmelCase     :						Optional[Union[bool, str, None]] = None						,  __UpperCAmelCase     :						Optional[Union[str, None]] = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						Optional[str] = None						,  __UpperCAmelCase     :						Optional[str] = None						,  __UpperCAmelCase     :						Optional[str] = None						,  __UpperCAmelCase     :						Optional["ort.SessionOptions"] = None						,  **__UpperCAmelCase     :						List[Any]						,  )      ->			Dict:
							UpperCAmelCase_=       file_name if file_name is not None else ONNX_WEIGHTS_NAME
							# load model from local directory
							if os.path.isdir(__UpperCAmelCase					):
													UpperCAmelCase_=       OnnxRuntimeModel.load_model(
													    os.path.join(__UpperCAmelCase						,  __UpperCAmelCase					)						,  provider=__UpperCAmelCase						,  sess_options=__UpperCAmelCase					)
													UpperCAmelCase_=       Path(__UpperCAmelCase					)
							# load model from hub
							else:
													# download model
													UpperCAmelCase_=       hf_hub_download(
													    repo_id=__UpperCAmelCase						,  filename=__UpperCAmelCase						,  use_auth_token=__UpperCAmelCase						,  revision=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  force_download=__UpperCAmelCase						,  )
													UpperCAmelCase_=       Path(__UpperCAmelCase					).parent
													UpperCAmelCase_=       Path(__UpperCAmelCase					).name
													UpperCAmelCase_=       OnnxRuntimeModel.load_model(__UpperCAmelCase						,  provider=__UpperCAmelCase						,  sess_options=__UpperCAmelCase					)
							return cls(model=__UpperCAmelCase						,  **__UpperCAmelCase					)




	@classmethod
	def   _SCREAMING_SNAKE_CASE		(     cls     :						Optional[int]						,  __UpperCAmelCase     :						Union[str, Path]						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						Optional[str] = None						,  __UpperCAmelCase     :						Optional[str] = None						,  **__UpperCAmelCase     :						Optional[int]						,  )      ->			Optional[int]:
							UpperCAmelCase_=       None
							if len(str(__UpperCAmelCase					).split("""@"""					)					) == 2:
													UpperCAmelCase_,       UpperCAmelCase_=       model_id.split("""@"""					)

							return cls._from_pretrained(
							    model_id=__UpperCAmelCase						,  revision=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  force_download=__UpperCAmelCase						,  use_auth_token=__UpperCAmelCase						,  **__UpperCAmelCase						,  )









from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						np.ndarray = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						Union[str, Any]=True						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													UpperCAmelCase_=       (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													UpperCAmelCase_=       randn_tensor(
													    (
													        batch_size,
													        self.unet.config.in_channels,
													        self.unet.config.sample_size[0],
													        self.unet.config.sample_size[1],
													    )						,  generator=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 2 - 1
													UpperCAmelCase_=       torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float					).to(self.device					)

													if self.vqvae is not None:
																			UpperCAmelCase_=       self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  self.scheduler.timesteps[start_step - 1]					)

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
													if isinstance(self.unet						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

													if isinstance(self.scheduler						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  eta=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]
													else:
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]

													if mask is not None:
																			if mask_start > 0:
																									UpperCAmelCase_=       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									UpperCAmelCase_=       mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
													# 0.18215 was scaling factor used in training to ensure unit variance
													UpperCAmelCase_=       1 / self.vqvae.config.scaling_factor * images
													UpperCAmelCase_=       self.vqvae.decode(__UpperCAmelCase					)["""sample"""]

							UpperCAmelCase_=       (images / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       images.cpu().permute(0						,  2						,  3						,  1					).numpy()
							UpperCAmelCase_=       (images * 255).round().astype("""uint8"""					)
							UpperCAmelCase_=       list(
							    (Image.fromarray(_[:, :, 0]					) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__UpperCAmelCase						,  mode="""RGB"""					).convert("""L"""					) for _ in images)					)

							UpperCAmelCase_=       [self.mel.image_to_audio(__UpperCAmelCase					) for _ in images]
							if not return_dict:
													return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase					)[:, np.newaxis, :]					)						,  **ImagePipelineOutput(__UpperCAmelCase					)					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,)					)					):
													UpperCAmelCase_=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
													UpperCAmelCase_=       self.scheduler.alphas_cumprod[t]
													UpperCAmelCase_=       (
													    self.scheduler.alphas_cumprod[prev_timestep]
													    if prev_timestep >= 0
													    else self.scheduler.final_alpha_cumprod
													)
													UpperCAmelCase_=       1 - alpha_prod_t
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													UpperCAmelCase_=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
													UpperCAmelCase_=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
													UpperCAmelCase_=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




