



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   torch.FloatTensor
				UpperCamelCase__			:   torch.FloatTensor
class 			_snake_case     (   lowercase__      ,		lowercase__):
				UpperCamelCase__			:   Union[str, Any]       						=1




				@register_to_config
				def __init__(  self   :  Tuple,   __lowercase   :  int = 2000,   __lowercase   :  float = 0.15,   __lowercase   :  float = 0.01,   __lowercase   :  float = 1348.0,   __lowercase   :  float = 1e-5,   __lowercase   :  int = 1,   ):
							# standard deviation of the initial noise distribution
							lowercase__   					=			sigma_max

							# setable values
							lowercase__   					=			None

							self.set_sigmas(__lowercase,   __lowercase,   __lowercase,   __lowercase  )




				def 							A__ (  self   :  str,   __lowercase   :  torch.FloatTensor,   __lowercase   :  Optional[int] = None  ):
							return sample




				def 							A__ (  self   :  int,   __lowercase   :  int,   __lowercase   :  float = None,   __lowercase   :  Union[str, torch.device] = None  ):
							lowercase__   					=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

							lowercase__   					=			torch.linspace(1,   __lowercase,   __lowercase,   device=__lowercase  )




				def 							A__ (  self   :  List[Any],   __lowercase   :  int,   __lowercase   :  float = None,   __lowercase   :  float = None,   __lowercase   :  float = None  ):
							lowercase__   					=			sigma_min if sigma_min is not None else self.config.sigma_min
							lowercase__   					=			sigma_max if sigma_max is not None else self.config.sigma_max
							lowercase__   					=			sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
										self.set_timesteps(__lowercase,   __lowercase  )

							lowercase__   					=			sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
							lowercase__   					=			torch.exp(torch.linspace(math.log(__lowercase  ),   math.log(__lowercase  ),   __lowercase  )  )
							lowercase__   					=			torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )




				def 							A__ (  self   :  List[Any],   __lowercase   :  List[str],   __lowercase   :  Optional[int]  ):
							return torch.where(
							    timesteps == 0,   torch.zeros_like(t.to(timesteps.device  )  ),   self.discrete_sigmas[timesteps - 1].to(timesteps.device  ),   )




				def 							A__ (  self   :  Tuple,   __lowercase   :  torch.FloatTensor,   __lowercase   :  int,   __lowercase   :  torch.FloatTensor,   __lowercase   :  Optional[torch.Generator] = None,   __lowercase   :  bool = True,   ):
							if self.timesteps is None:
										raise ValueError(
										    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

							lowercase__   					=			timestep * torch.ones(
							    sample.shape[0],   device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
							lowercase__   					=			(timestep * (len(self.timesteps  ) - 1)).long()

							# mps requires indices to be in the same device, so we use cpu as is the default with cuda
							lowercase__   					=			timesteps.to(self.discrete_sigmas.device  )

							lowercase__   					=			self.discrete_sigmas[timesteps].to(sample.device  )
							lowercase__   					=			self.get_adjacent_sigma(__lowercase,   __lowercase  ).to(sample.device  )
							lowercase__   					=			torch.zeros_like(__lowercase  )
							lowercase__   					=			(sigma**2 - adjacent_sigma**2) ** 0.5

							# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
							# also equation 47 shows the analog from SDE models to ancestral sampling methods
							lowercase__   					=			diffusion.flatten()
							while len(diffusion.shape  ) < len(sample.shape  ):
										lowercase__   					=			diffusion.unsqueeze(-1  )
							lowercase__   					=			drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							lowercase__   					=			randn_tensor(
							    sample.shape,   layout=sample.layout,   generator=__lowercase,   device=sample.device,   dtype=sample.dtype  )
							lowercase__   					=			sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							lowercase__   					=			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

							if not return_dict:
										return (prev_sample, prev_sample_mean)

							return SdeVeOutput(prev_sample=__lowercase,   prev_sample_mean=__lowercase  )




				def 							A__ (  self   :  str,   __lowercase   :  torch.FloatTensor,   __lowercase   :  torch.FloatTensor,   __lowercase   :  Optional[torch.Generator] = None,   __lowercase   :  bool = True,   ):
							if self.timesteps is None:
										raise ValueError(
										    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

							# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
							# sample noise for correction
							lowercase__   					=			randn_tensor(sample.shape,   layout=sample.layout,   generator=__lowercase  ).to(sample.device  )

							# compute step size from the model_output, the noise, and the snr
							lowercase__   					=			torch.norm(model_output.reshape(model_output.shape[0],   -1  ),   dim=-1  ).mean()
							lowercase__   					=			torch.norm(noise.reshape(noise.shape[0],   -1  ),   dim=-1  ).mean()
							lowercase__   					=			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
							lowercase__   					=			step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
							# self.repeat_scalar(step_size, sample.shape[0])

							# compute corrected sample: model_output term and noise term
							lowercase__   					=			step_size.flatten()
							while len(step_size.shape  ) < len(sample.shape  ):
										lowercase__   					=			step_size.unsqueeze(-1  )
							lowercase__   					=			sample + step_size * model_output
							lowercase__   					=			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
										return (prev_sample,)

							return SchedulerOutput(prev_sample=__lowercase  )




				def 							A__ (  self   :  int,   __lowercase   :  torch.FloatTensor,   __lowercase   :  torch.FloatTensor,   __lowercase   :  torch.FloatTensor,   ):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							lowercase__   					=			timesteps.to(original_samples.device  )
							lowercase__   					=			self.discrete_sigmas.to(original_samples.device  )[timesteps]
							lowercase__   					=			(
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(__lowercase  ) * sigmas[:, None, None, None]
							)
							lowercase__   					=			noise + original_samples
							return noisy_samples




				def __len__(  self   :  Tuple  ):
							return self.config.num_train_timesteps








import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowercase_						=       logging.get_logger(__name__)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			r"\w+[.]\d+"
			lowercase__   					=			re.findall(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			for pat in pats:
						lowercase__   					=			key.replace(SCREAMING_SNAKE_CASE_  ,					"_".join(pat.split("."					)					)					)
			return key



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
			if (
			    any("norm" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						lowercase__   					=			pt_tuple_key[:-1] + ("embedding",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			lowercase__   					=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						lowercase__   					=			pt_tensor.transpose(2  ,					3  ,					1  ,					0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			lowercase__   					=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight":
						lowercase__   					=			pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			lowercase__   					=			pt_tuple_key[:-1] + ("weight",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			lowercase__   					=			pt_tuple_key[:-1] + ("bias",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor




def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=42					):
			# Step 1: Convert pytorch tensor to numpy
			lowercase__   					=			{k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			lowercase__   					=			flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE_					)					)

			lowercase__   					=			flatten_dict(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						lowercase__   					=			rename_key(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			tuple(renamed_pt_key.split("."					)					)

						# Correctly rename weight parameters
						lowercase__					,    lowercase__   					=			rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						lowercase__   					=			jnp.asarray(SCREAMING_SNAKE_CASE_					)

			return unflatten_dict(SCREAMING_SNAKE_CASE_					)




