




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      List[str]=0.9_99 ,     UpperCamelCase__				:      str="cosine" ,     )     ->       List[str]:





				"""simple docstring"""


				if alpha_transform_type == "cosine":

								def alpha_bar_fn(UpperCamelCase__				:      Union[str, Any]      ):
												return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(UpperCamelCase__				:      Optional[int]      ):
												return math.exp(t * -12.0      )

				else:
								raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

				__lowerCamelCase		       =    []
				for i in range(__snake_case      ):
								__lowerCamelCase		       =    i / num_diffusion_timesteps
								__lowerCamelCase		       =    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(__snake_case      ) / alpha_bar_fn(__snake_case      ) ,     __snake_case      )      )
				return torch.tensor(__snake_case ,     dtype=torch.floataa      )







class     __lowerCAmelCase (       _snake_case			,				_snake_case ):

			"""simple docstring"""







			snake_case_   		=       [e.name for e in KarrasDiffusionSchedulers]
			snake_case_   		=       2


			@register_to_config
			def __init__(      self			,						lowerCamelCase__ = 1_000			,						lowerCamelCase__ = 0.0_00_85			,						lowerCamelCase__ = 0.0_12			,						lowerCamelCase__ = "linear"			,						lowerCamelCase__ = None			,						lowerCamelCase__ = "epsilon"			,						lowerCamelCase__ = "linspace"			,						lowerCamelCase__ = 0			,						)    ->      Optional[Any]:



							'''simple docstring'''
							if trained_betas is not None:
											__lowerCamelCase		       =    torch.tensor(_UpperCamelCase			,						dtype=torch.floataa							)
							elif beta_schedule == "linear":
											__lowerCamelCase		       =    torch.linspace(_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						dtype=torch.floataa							)
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__lowerCamelCase		       =    (
											    torch.linspace(beta_start**0.5			,						beta_end**0.5			,						_UpperCamelCase			,						dtype=torch.floataa							) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__lowerCamelCase		       =    betas_for_alpha_bar(_UpperCamelCase							)
							else:
											raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

							__lowerCamelCase		       =    1.0 - self.betas
							__lowerCamelCase		       =    torch.cumprod(self.alphas			,						dim=0							)

							#  set all values
							self.set_timesteps(_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=None							)    ->      List[Any]:



							'''simple docstring'''
							if schedule_timesteps is None:
											__lowerCamelCase		       =    self.timesteps

							__lowerCamelCase		       =    (schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter							) == 0:
											__lowerCamelCase		       =    1 if len(_UpperCamelCase							) > 1 else 0
							else:
											__lowerCamelCase		       =    timestep.cpu().item() if torch.is_tensor(_UpperCamelCase							) else timestep
											__lowerCamelCase		       =    self._index_counter[timestep_int]

							return indices[pos].item()


			@property
			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						)    ->      torch.FloatTensor:



							'''simple docstring'''
							__lowerCamelCase		       =    self.index_for_timestep(_UpperCamelCase							)

							if self.state_in_first_order:
											__lowerCamelCase		       =    self.sigmas[step_index]
							else:
											__lowerCamelCase		       =    self.sigmas_interpol[step_index]

							__lowerCamelCase		       =    sample / ((sigma**2 + 1) ** 0.5)
							return sample


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    num_inference_steps

							__lowerCamelCase		       =    num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
											__lowerCamelCase		       =    np.linspace(0			,						num_train_timesteps - 1			,						_UpperCamelCase			,						dtype=_UpperCamelCase							)[::-1].copy()
							elif self.config.timestep_spacing == "leading":
											__lowerCamelCase		       =    num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											__lowerCamelCase		       =    (np.arange(0			,						_UpperCamelCase							) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase							)
											timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
											__lowerCamelCase		       =    num_train_timesteps / self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											__lowerCamelCase		       =    (np.arange(_UpperCamelCase			,						0			,						-step_ratio							)).round().copy().astype(_UpperCamelCase							)
											timesteps -= 1
							else:
											raise ValueError(
											    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""							)

							__lowerCamelCase		       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
							__lowerCamelCase		       =    torch.from_numpy(np.log(_UpperCamelCase							)							).to(_UpperCamelCase							)

							__lowerCamelCase		       =    np.interp(_UpperCamelCase			,						np.arange(0			,						len(_UpperCamelCase							)							)			,						_UpperCamelCase							)
							__lowerCamelCase		       =    np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
							__lowerCamelCase		       =    torch.from_numpy(_UpperCamelCase							).to(device=_UpperCamelCase							)

							# interpolate sigmas
							__lowerCamelCase		       =    sigmas.log().lerp(sigmas.roll(1							).log()			,						0.5							).exp()

							__lowerCamelCase		       =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
							__lowerCamelCase		       =    torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

							if str(_UpperCamelCase							).startswith('mps'							):
											# mps does not support float64
											__lowerCamelCase		       =    torch.from_numpy(_UpperCamelCase							).to(_UpperCamelCase			,						dtype=torch.floataa							)
							else:
											__lowerCamelCase		       =    torch.from_numpy(_UpperCamelCase							).to(_UpperCamelCase							)

							# interpolate timesteps
							__lowerCamelCase		       =    self.sigma_to_t(_UpperCamelCase							).to(_UpperCamelCase			,						dtype=timesteps.dtype							)
							__lowerCamelCase		       =    torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])			,						dim=-1							).flatten()

							__lowerCamelCase		       =    torch.cat([timesteps[:1], interleaved_timesteps]							)

							__lowerCamelCase		       =    None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							__lowerCamelCase		       =    defaultdict(_UpperCamelCase							)


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							# get log sigma
							__lowerCamelCase		       =    sigma.log()

							# get distribution
							__lowerCamelCase		       =    log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							__lowerCamelCase		       =    dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
							__lowerCamelCase		       =    low_idx + 1

							__lowerCamelCase		       =    self.log_sigmas[low_idx]
							__lowerCamelCase		       =    self.log_sigmas[high_idx]

							# interpolate sigmas
							__lowerCamelCase		       =    (low - log_sigma) / (low - high)
							__lowerCamelCase		       =    w.clamp(0			,						1							)

							# transform interpolation to time range
							__lowerCamelCase		       =    (1 - w) * low_idx + w * high_idx
							__lowerCamelCase		       =    t.view(sigma.shape							)
							return t


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							return self.sample is None


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = True			,						)    ->      Union[SchedulerOutput, Tuple]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.index_for_timestep(_UpperCamelCase							)

							# advance index counter by 1
							__lowerCamelCase		       =    timestep.cpu().item() if torch.is_tensor(_UpperCamelCase							) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
											__lowerCamelCase		       =    self.sigmas[step_index]
											__lowerCamelCase		       =    self.sigmas_interpol[step_index + 1]
											__lowerCamelCase		       =    self.sigmas[step_index + 1]
							else:
											# 2nd order / KDPM2's method
											__lowerCamelCase		       =    self.sigmas[step_index - 1]
											__lowerCamelCase		       =    self.sigmas_interpol[step_index]
											__lowerCamelCase		       =    self.sigmas[step_index]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
											__lowerCamelCase		       =    sigma_hat if self.state_in_first_order else sigma_interpol
											__lowerCamelCase		       =    sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
											__lowerCamelCase		       =    sigma_hat if self.state_in_first_order else sigma_interpol
											__lowerCamelCase		       =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
							elif self.config.prediction_type == "sample":
											raise NotImplementedError('prediction_type not implemented yet: sample'							)
							else:
											raise ValueError(
											    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""							)

							if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											__lowerCamelCase		       =    (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											__lowerCamelCase		       =    sigma_interpol - sigma_hat

											# store for 2nd order step
											__lowerCamelCase		       =    sample
							else:
											# DPM-Solver-2
											# 2. Convert to an ODE derivative for 2nd order
											__lowerCamelCase		       =    (sample - pred_original_sample) / sigma_interpol

											# 3. delta timestep
											__lowerCamelCase		       =    sigma_next - sigma_hat

											__lowerCamelCase		       =    self.sample
											__lowerCamelCase		       =    None

							__lowerCamelCase		       =    sample + derivative * dt

							if not return_dict:
											return (prev_sample,)

							return SchedulerOutput(prev_sample=_UpperCamelCase							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						)    ->      torch.FloatTensor:



							'''simple docstring'''
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							__lowerCamelCase		       =    self.sigmas.to(device=original_samples.device			,						dtype=original_samples.dtype							)
							if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase							):
											# mps does not support float64
											__lowerCamelCase		       =    self.timesteps.to(original_samples.device			,						dtype=torch.floataa							)
											__lowerCamelCase		       =    timesteps.to(original_samples.device			,						dtype=torch.floataa							)
							else:
											__lowerCamelCase		       =    self.timesteps.to(original_samples.device							)
											__lowerCamelCase		       =    timesteps.to(original_samples.device							)

							__lowerCamelCase		       =    [self.index_for_timestep(_UpperCamelCase			,						_UpperCamelCase							) for t in timesteps]

							__lowerCamelCase		       =    sigmas[step_indices].flatten()
							while len(sigma.shape							) < len(original_samples.shape							):
											__lowerCamelCase		       =    sigma.unsqueeze(-1							)

							__lowerCamelCase		       =    original_samples + noise * sigma
							return noisy_samples






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



							'''simple docstring'''
							return self.config.num_train_timesteps










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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    '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   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							Dict:
							logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'   )
							UpperCAmelCase_    :      Any            =						model
							UpperCAmelCase_    :      int            =						kwargs.get('model_save_dir'				,		_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						kwargs.get('latest_model_name'				,		_UpperCamelCase   )



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



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase				,		_UpperCamelCase=None				,		_UpperCamelCase=None   )	->							List[Any]:
							if provider is None:
												logger.info('No onnxruntime provider specified, using CPUExecutionProvider'   )
												UpperCAmelCase_    :      List[str]            =						'CPUExecutionProvider'

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      Any            =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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



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



		@classmethod
		def 					__UpperCAmelCase			(							cls				,		_UpperCamelCase				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Optional[int]:
							UpperCAmelCase_    :      List[str]            =						None
							if len(str(_UpperCamelCase   ).split('@'   )   ) == 2:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						model_id.split('@'   )

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






