



"""simple docstring"""


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

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 				A_      (					SCREAMING_SNAKE_CASE_    ):



      """simple docstring"""
      __UpperCamelCase		    =							42
      __UpperCamelCase		    =							None


def 			_lowerCAmelCase				(       lowercase_		,     lowercase_=0.9_9_9		,     lowercase_="cosine"		,     ):
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(lowercase_						):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(lowercase_						):
                  return math.exp(t * -12.0						)

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

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







class 				A_      (					SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_    ):



      """simple docstring"""
      __UpperCamelCase		    =							1






      @register_to_config
      def __init__( self			:Dict       ,  lowercase_			:int = 10_00       ,  lowercase_			:float = 0.0001       ,  lowercase_			:float = 0.02       ,  lowercase_			:str = "linear"       ,  lowercase_			:Optional[Union[np.ndarray, List[float]]] = None       ,  lowercase_			:bool = True       ,  lowercase_			:bool = True       ,  lowercase_			:int = 0       ,  lowercase_			:str = "epsilon"       ,  lowercase_			:float = 1.0       ,  **lowercase_			:Dict       ,  )    ->      List[str]:
            if kwargs.get('set_alpha_to_one'       ,  lowercase_ ) is not None:
                  UpperCAmelCase					=			(
                      'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
                  )
                  deprecate('set_alpha_to_one'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
                  UpperCAmelCase					=			kwargs['set_alpha_to_one']
            if trained_betas is not None:
                  UpperCAmelCase					=			torch.tensor(lowercase_       ,  dtype=torch.floataa )
            elif beta_schedule == "linear":
                  UpperCAmelCase					=			torch.linspace(lowercase_       ,  lowercase_       ,  lowercase_       ,  dtype=torch.floataa )
            elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  UpperCAmelCase					=			(
                      torch.linspace(beta_start**0.5       ,  beta_end**0.5       ,  lowercase_       ,  dtype=torch.floataa ) ** 2
                  )
            elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  UpperCAmelCase					=			betas_for_alpha_bar(lowercase_ )
            else:
                  raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""" )

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

            # At every step in inverted ddim, we are looking into the next alphas_cumprod
            # For the final step, there is no next alphas_cumprod, and the index is out of bounds
            # `set_alpha_to_zero` decides whether we set this parameter simply to zero
            # in this case, self.step() just output the predicted noise
            # or whether we use the final alpha of the "non-previous" one.
            UpperCAmelCase					=			torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]

            # standard deviation of the initial noise distribution
            UpperCAmelCase					=			1.0

            # setable values
            UpperCAmelCase					=			None
            UpperCAmelCase					=			torch.from_numpy(np.arange(0       ,  lowercase_ ).copy().astype(np.intaa ) )






      def      UpperCAmelCase__					( self			:Any       ,  lowercase_			:torch.FloatTensor       ,  lowercase_			:Optional[int] = None )    ->      torch.FloatTensor:
            return sample






      def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:int       ,  lowercase_			:Union[str, torch.device] = None )    ->      int:

            if num_inference_steps > self.config.num_train_timesteps:
                  raise ValueError(
                      f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
                      f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
                      f""" maximal {self.config.num_train_timesteps} timesteps.""" )

            UpperCAmelCase					=			num_inference_steps
            UpperCAmelCase					=			self.config.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
            UpperCAmelCase					=			(np.arange(0       ,  lowercase_ ) * step_ratio).round().copy().astype(np.intaa )
            UpperCAmelCase					=			torch.from_numpy(lowercase_ ).to(lowercase_ )
            self.timesteps += self.config.steps_offset






      def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:torch.FloatTensor       ,  lowercase_			:int       ,  lowercase_			:torch.FloatTensor       ,  lowercase_			:float = 0.0       ,  lowercase_			:bool = False       ,  lowercase_			:Optional[torch.FloatTensor] = None       ,  lowercase_			:bool = True       ,  )    ->      Union[DDIMSchedulerOutput, Tuple]:
            # 1. get previous step value (=t+1)
            UpperCAmelCase					=			timestep + self.config.num_train_timesteps // self.num_inference_steps

            # 2. compute alphas, betas
            # change original implementation to exactly match noise levels for analogous forward process
            UpperCAmelCase					=			self.alphas_cumprod[timestep]
            UpperCAmelCase					=			(
                self.alphas_cumprod[prev_timestep]
                if prev_timestep < self.config.num_train_timesteps
                else self.final_alpha_cumprod
            )

            UpperCAmelCase					=			1 - alpha_prod_t

            # 3. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            if self.config.prediction_type == "epsilon":
                  UpperCAmelCase					=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                  UpperCAmelCase					=			model_output
            elif self.config.prediction_type == "sample":
                  UpperCAmelCase					=			model_output
                  UpperCAmelCase					=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
            elif self.config.prediction_type == "v_prediction":
                  UpperCAmelCase					=			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                  UpperCAmelCase					=			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
            else:
                  raise ValueError(
                      f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
                      ' `v_prediction`' )

            # 4. Clip or threshold "predicted x_0"
            if self.config.clip_sample:
                  UpperCAmelCase					=			pred_original_sample.clamp(
                      -self.config.clip_sample_range       ,  self.config.clip_sample_range )

            # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            UpperCAmelCase					=			(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

            # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            UpperCAmelCase					=			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

            if not return_dict:
                  return (prev_sample, pred_original_sample)
            return DDIMSchedulerOutput(prev_sample=lowercase_       ,  pred_original_sample=lowercase_ )






      def __len__( self			:int )    ->      Optional[int]:
            return self.config.num_train_timesteps










"""simple docstring"""


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


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			R'\w+[.]\d+'
						UpperCAmelCase					=			re.findall(lowercase_		,     lowercase_						)
						for pat in pats:
												UpperCAmelCase					=			key.replace(lowercase_		,     '_'.join(pat.split('.'						)						)						)
						return key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			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)
						):
												UpperCAmelCase					=			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:
												UpperCAmelCase					=			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:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

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

						# linear layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight":
												UpperCAmelCase					=			pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

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

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

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

						UpperCAmelCase					=			flatten_dict(lowercase_						)
						UpperCAmelCase					=			{}

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

												# Correctly rename weight parameters
												UpperCAmelCase				,   UpperCAmelCase					=			rename_key_and_reshape_tensor(lowercase_		,     lowercase_		,     lowercase_						)

												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
												UpperCAmelCase					=			jnp.asarray(lowercase_						)

						return unflatten_dict(lowercase_						)





