




'''simple docstring'''





# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
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							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       torch.FloatTensor
     SCREAMING_SNAKE_CASE_				:       Optional[torch.FloatTensor]       							=		None







def       __a      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       Optional[Any]=0.999   ,		_UpperCamelCase:       Dict="cosine"   ,		)		->					Any:

     """simple docstring"""




     if alpha_transform_type == "cosine":

          def alpha_bar_fn(_UpperCamelCase:       List[str]       ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

     elif alpha_transform_type == "exp":

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

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

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





class   _a							(		__lowerCAmelCase   ,   __lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       int       							=		1






     @register_to_config
     def __init__(					self   ,_SCREAMING_SNAKE_CASE = 1_000   ,_SCREAMING_SNAKE_CASE = 0.0_0_0_1   ,_SCREAMING_SNAKE_CASE = 0.0_2   ,_SCREAMING_SNAKE_CASE = "linear"   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = True   ,_SCREAMING_SNAKE_CASE = True   ,_SCREAMING_SNAKE_CASE = 0   ,_SCREAMING_SNAKE_CASE = "epsilon"   ,_SCREAMING_SNAKE_CASE = 1.0   ,**_SCREAMING_SNAKE_CASE   ,)				->     Dict:
          if kwargs.get("set_alpha_to_one"   ,_SCREAMING_SNAKE_CASE       ) is not None:
               _snake_case				= (
                   "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
               )
               deprecate("set_alpha_to_one"   ,"1.0.0"   ,_SCREAMING_SNAKE_CASE   ,standard_warn=_SCREAMING_SNAKE_CASE       )
               _snake_case				= kwargs["set_alpha_to_one"]
          if trained_betas is not None:
               _snake_case				= torch.tensor(_SCREAMING_SNAKE_CASE   ,dtype=torch.floataa       )
          elif beta_schedule == "linear":
               _snake_case				= torch.linspace(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _snake_case				= (
                   torch.linspace(beta_start**0.5   ,beta_end**0.5   ,_SCREAMING_SNAKE_CASE   ,dtype=torch.floataa       ) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _snake_case				= betas_for_alpha_bar(_SCREAMING_SNAKE_CASE       )
          else:
               raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""       )

          _snake_case				= 1.0 - self.betas
          _snake_case				= 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.
          _snake_case				= torch.tensor(0.0       ) if set_alpha_to_zero else self.alphas_cumprod[-1]

          # standard deviation of the initial noise distribution
          _snake_case				= 1.0

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






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     torch.FloatTensor:
          return sample






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Dict:

          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."""       )

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






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = 0.0   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = True   ,)				->     Union[DDIMSchedulerOutput, Tuple]:
          # 1. get previous step value (=t+1)
          _snake_case				= 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
          _snake_case				= self.alphas_cumprod[timestep]
          _snake_case				= (
              self.alphas_cumprod[prev_timestep]
              if prev_timestep < self.config.num_train_timesteps
              else self.final_alpha_cumprod
          )

          _snake_case				= 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":
               _snake_case				= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
               _snake_case				= model_output
          elif self.config.prediction_type == "sample":
               _snake_case				= model_output
               _snake_case				= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
               _snake_case				= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
               _snake_case				= (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:
               _snake_case				= 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
          _snake_case				= (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
          _snake_case				= 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=_SCREAMING_SNAKE_CASE   ,pred_original_sample=_SCREAMING_SNAKE_CASE       )






     def __len__(					self       )				->     Dict:
          return self.config.num_train_timesteps








'''simple docstring'''





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_		:     Optional[int]          =		object()

# For specifying empty leaf dict `{}`
UpperCamelCase_		:     Optional[int]          =		object()





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[Any]       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				= tuple((re.compile(x + "$"       ) for x in qs)       )
     for i in range(len(_UpperCamelCase       ) - len(_UpperCamelCase       ) + 1       ):
          _snake_case				= [x.match(_UpperCamelCase       ) for x, y in zip(_UpperCamelCase   ,		ks[i:]       )]
          if matches and all(_UpperCamelCase       ):
               return True
     return False





def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     def replace(_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       List[str]       ):
          for rule, replacement in rules:
               if _match(_UpperCamelCase   ,		_UpperCamelCase       ):
                    return replacement
          return val

     return replace





def       __a      (  )		->					Any:

     """simple docstring"""




     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P("mp"   ,		_UpperCamelCase       )),
         (("transformer", "wte", "embedding"), P("mp"   ,		_UpperCamelCase       )),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(_UpperCamelCase   ,		"mp"       )),
         (("attention", "out_proj", "kernel"), P("mp"   ,		_UpperCamelCase       )),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(_UpperCamelCase   ,		"mp"       )),
         (("mlp", "c_fc", "bias"), P("mp"       )),
         (("mlp", "c_proj", "kernel"), P("mp"   ,		_UpperCamelCase       )),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]





def       __a      (  _UpperCamelCase:       Union[str, Any]       )		->					Any:

     """simple docstring"""




     _snake_case				= _get_partition_rules()
     _snake_case				= _replacement_rules(_UpperCamelCase       )
     _snake_case				= {k: _unmatched for k in flatten_dict(_UpperCamelCase       )}
     _snake_case				= {k: replace(_UpperCamelCase   ,		_UpperCamelCase       ) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(_UpperCamelCase       )       )


