






import math
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 SchedulerMixin, SchedulerOutput



class 				_UpperCamelCase      (a_				,		a_      ):
      snake_case_	=				1


      @register_to_config
      def __init__(						self       ,    __UpperCamelCase = 1_0_0_0       ,    __UpperCamelCase = None						)->						List[Any]:
           # set `betas`, `alphas`, `timesteps`
           self.set_timesteps(__UpperCamelCase						)

           # standard deviation of the initial noise distribution
           __lowerCAmelCase             =       1.0

           # For now we only support F-PNDM, i.e. the runge-kutta method
           # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
           # mainly at formula (9), (12), (13) and the Algorithm 2.
           __lowerCAmelCase             =       4

           # running values
           __lowerCAmelCase             =       []


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None						)->						Union[str, Any]:
           __lowerCAmelCase             =       num_inference_steps
           __lowerCAmelCase             =       torch.linspace(1       ,    0       ,    num_inference_steps + 1						)[:-1]
           __lowerCAmelCase             =       torch.cat([steps, torch.tensor([0.0]						)]						)

           if self.config.trained_betas is not None:
                __lowerCAmelCase             =       torch.tensor(self.config.trained_betas       ,    dtype=torch.floataa						)
           else:
                __lowerCAmelCase             =       torch.sin(steps * math.pi / 2						) ** 2

           __lowerCAmelCase             =       (1.0 - self.betas**2) ** 0.5

           __lowerCAmelCase             =       (torch.atana(self.betas       ,    self.alphas						) / math.pi * 2)[:-1]
           __lowerCAmelCase             =       timesteps.to(__UpperCamelCase						)

           __lowerCAmelCase             =       []


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase = True       ,    )->						Union[SchedulerOutput, Tuple]:
           if self.num_inference_steps is None:
                raise ValueError(
                    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"						)

           __lowerCAmelCase             =       (self.timesteps == timestep).nonzero().item()
           __lowerCAmelCase             =       timestep_index + 1

           __lowerCAmelCase             =       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
           self.ets.append(__UpperCamelCase						)

           if len(self.ets						) == 1:
                __lowerCAmelCase             =       self.ets[-1]
           elif len(self.ets						) == 2:
                __lowerCAmelCase             =       (3 * self.ets[-1] - self.ets[-2]) / 2
           elif len(self.ets						) == 3:
                __lowerCAmelCase             =       (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
           else:
                __lowerCAmelCase             =       (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

           __lowerCAmelCase             =       self._get_prev_sample(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)

           if not return_dict:
                return (prev_sample,)

           return SchedulerOutput(prev_sample=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						torch.FloatTensor:
           return sample


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       self.alphas[timestep_index]
           __lowerCAmelCase             =       self.betas[timestep_index]

           __lowerCAmelCase             =       self.alphas[prev_timestep_index]
           __lowerCAmelCase             =       self.betas[prev_timestep_index]

           __lowerCAmelCase             =       (sample - sigma * ets) / max(__UpperCamelCase       ,    1e-8						)
           __lowerCAmelCase             =       next_alpha * pred + ets * next_sigma

           return prev_sample


      def __len__(						self						)->						List[str]:
           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


lowerCamelCase      :       int    =						logging.get_logger(__name__)





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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       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)
     ):
          __lowerCAmelCase             =       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:
          __lowerCAmelCase             =       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:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("embedding",)
          return renamed_pt_tuple_key, pt_tensor

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

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

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

     # old PyTorch layer norm bias
     __lowerCAmelCase             =       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   (    __snake_case       ,			__snake_case       ,			__snake_case=42							):
     # Step 1: Convert pytorch tensor to numpy
     __lowerCAmelCase             =       {k: v.numpy() for k, v in pt_state_dict.items()}

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

     __lowerCAmelCase             =       flatten_dict(__snake_case							)
     __lowerCAmelCase             =       {}

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

          # Correctly rename weight parameters
          __lowerCAmelCase      ,			__lowerCAmelCase             =       rename_key_and_reshape_tensor(__snake_case       ,			__snake_case       ,			__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
          __lowerCAmelCase             =       jnp.asarray(__snake_case							)

     return unflatten_dict(__snake_case							)






