






"""simple docstring"""
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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''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 			UpperCamelCase_       :





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





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





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

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





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

				UpperCAmelCase_     : Any			     =					self.model_save_dir.joinpath(self.latest_model_name						)
				UpperCAmelCase_     : Union[str, Any]			     =					Path(lowerCAmelCase_						).joinpath(lowerCAmelCase_						)
				try:
							shutil.copyfile(lowerCAmelCase_	,	lowerCAmelCase_						)
				except shutil.SameFileError:
							pass

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





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

				os.makedirs(lowerCAmelCase_	,	exist_ok=lowerCAmelCase_						)

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





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





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

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











"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



