



# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class 	snake_case_			:




       """simple docstring"""






       _lowerCamelCase           =					42
       _lowerCamelCase           =					42

       # setable values
       _lowerCamelCase           =					42
       _lowerCamelCase           =					42
       _lowerCamelCase           =					None



       @classmethod
       def 			A_ (  cls  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,):

             """simple docstring"""





             return cls(
                 common=lowercase  ,final_alpha_cumprod=lowercase  ,init_noise_sigma=lowercase  ,timesteps=lowercase  ,)


@dataclass
class 	snake_case_			(lowercase__	):




       """simple docstring"""






       _lowerCamelCase           =					42


class 	snake_case_			(lowercase__			,			lowercase__	):




       """simple docstring"""






       _lowerCamelCase           =					[e.name for e in FlaxKarrasDiffusionSchedulers]

       _lowerCamelCase           =					42



       @property
       def 			A_ (  self):

             """simple docstring"""





             return True



       @register_to_config
       def __init__(  self  ,lowercase = 1000  ,lowercase = 0.0001  ,lowercase = 0.02  ,lowercase = "linear"  ,lowercase = None  ,lowercase = True  ,lowercase = 0  ,lowercase = "epsilon"  ,lowercase = jnp.floataa  ,):

             """simple docstring"""





             UpperCAmelCase_	:						str					   =		dtype



       def 			A_ (  self  ,lowercase = None):

             """simple docstring"""





             if common is None:
                   UpperCAmelCase_	:						Union[str, Any]					   =		CommonSchedulerState.create(self)

             # At every step in ddim, we are looking into the previous alphas_cumprod
             # For the final step, there is no previous alphas_cumprod because we are already at 0
             # `set_alpha_to_one` decides whether we set this parameter simply to one or
             # whether we use the final alpha of the "non-previous" one.
             UpperCAmelCase_	:						Tuple					   =		(
                 jnp.array(1.0  ,dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
             )

             # standard deviation of the initial noise distribution
             UpperCAmelCase_	:						List[str]					   =		jnp.array(1.0  ,dtype=self.dtype)

             UpperCAmelCase_	:						str					   =		jnp.arange(0  ,self.config.num_train_timesteps).round()[::-1]

             return DDIMSchedulerState.create(
                 common=lowercase  ,final_alpha_cumprod=lowercase  ,init_noise_sigma=lowercase  ,timesteps=lowercase  ,)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase = None):

             """simple docstring"""





             return sample



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase = ()):

             """simple docstring"""





             UpperCAmelCase_	:						int					   =		self.config.num_train_timesteps // num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # rounding to avoid issues when num_inference_step is power of 3
             UpperCAmelCase_	:						int					   =		(jnp.arange(0  ,lowercase) * step_ratio).round()[::-1] + self.config.steps_offset

             return state.replace(
                 num_inference_steps=lowercase  ,timesteps=lowercase  ,)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						List[Any]					   =		state.common.alphas_cumprod[timestep]
             UpperCAmelCase_	:						Optional[int]					   =		jnp.where(
                 prev_timestep >= 0  ,state.common.alphas_cumprod[prev_timestep]  ,state.final_alpha_cumprod)
             UpperCAmelCase_	:						Union[str, Any]					   =		1 - alpha_prod_t
             UpperCAmelCase_	:						List[Any]					   =		1 - alpha_prod_t_prev

             UpperCAmelCase_	:						Dict					   =		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

             return variance



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,lowercase = 0.0  ,lowercase = True  ,):

             """simple docstring"""





             if state.num_inference_steps is None:
                   raise ValueError(
                       "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler")

             # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
             # Ideally, read DDIM paper in-detail understanding

             # Notation (<variable name> -> <name in paper>
             # - pred_noise_t -> e_theta(x_t, t)
             # - pred_original_sample -> f_theta(x_t, t) or x_0
             # - std_dev_t -> sigma_t
             # - eta -> η
             # - pred_sample_direction -> "direction pointing to x_t"
             # - pred_prev_sample -> "x_t-1"

             # 1. get previous step value (=t-1)
             UpperCAmelCase_	:						Tuple					   =		timestep - self.config.num_train_timesteps // state.num_inference_steps

             UpperCAmelCase_	:						Any					   =		state.common.alphas_cumprod
             UpperCAmelCase_	:						Dict					   =		state.final_alpha_cumprod

             # 2. compute alphas, betas
             UpperCAmelCase_	:						Tuple					   =		alphas_cumprod[timestep]
             UpperCAmelCase_	:						str					   =		jnp.where(prev_timestep >= 0  ,alphas_cumprod[prev_timestep]  ,lowercase)

             UpperCAmelCase_	:						List[str]					   =		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_	:						int					   =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                   UpperCAmelCase_	:						List[str]					   =		model_output
             elif self.config.prediction_type == "sample":
                   UpperCAmelCase_	:						List[str]					   =		model_output
                   UpperCAmelCase_	:						Dict					   =		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
             elif self.config.prediction_type == "v_prediction":
                   UpperCAmelCase_	:						List[str]					   =		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                   UpperCAmelCase_	:						int					   =		(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. compute variance: "sigma_t(η)" -> see formula (16)
             # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
             UpperCAmelCase_	:						Any					   =		self._get_variance(lowercase  ,lowercase  ,lowercase)
             UpperCAmelCase_	:						Any					   =		eta * variance ** 0.5

             # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
             UpperCAmelCase_	:						Optional[Any]					   =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

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

             if not return_dict:
                   return (prev_sample, state)

             return FlaxDDIMSchedulerOutput(prev_sample=lowercase  ,state=lowercase)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,):

             """simple docstring"""





             return add_noise_common(state.common  ,lowercase  ,lowercase  ,lowercase)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,):

             """simple docstring"""





             return get_velocity_common(state.common  ,lowercase  ,lowercase  ,lowercase)



       def __len__(  self):

             """simple docstring"""





             return self.config.num_train_timesteps





import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case=None     )  ->  Any:






      '''simple docstring'''







      UpperCAmelCase_	:						Dict					   =		None
      # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
      if str(weight.dtype     ) == "torch.bfloat16":
            # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
            UpperCAmelCase_	:						Optional[Any]					   =		weight.view(torch.intaa     )
            UpperCAmelCase_	:						Dict					   =		"bfloat16"
      UpperCAmelCase_	:						Optional[int]					   =		weight.cpu().numpy()
      UpperCAmelCase_	:						Tuple					   =		os.path.join(__snake_case       ,	F"""{weight_name}.dat"""     )
      if index is not None:
            if dtype is None:
                  UpperCAmelCase_	:						int					   =		str(array.dtype     )
            UpperCAmelCase_	:						Any					   =		{"dtype": dtype, "shape": list(array.shape     )}
      if array.ndim == 0:
            UpperCAmelCase_	:						List[str]					   =		array[None]
      UpperCAmelCase_	:						List[Any]					   =		np.memmap(__snake_case       ,	dtype=array.dtype       ,	mode="w+"       ,	shape=array.shape     )
      UpperCAmelCase_	:						List[str]					   =		array[:]
      file_array.flush()
      return index







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Any:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		tuple(weight_info["shape"]     )
      if shape == ():
            # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
            UpperCAmelCase_	:						Dict					   =		(1,)

      UpperCAmelCase_	:						Tuple					   =		weight_info["dtype"]
      if dtype == "bfloat16":
            # NumPy does not support bfloat16 so this was saved as a int16
            UpperCAmelCase_	:						Tuple					   =		"int16"

      UpperCAmelCase_	:						Union[str, Any]					   =		np.memmap(__snake_case       ,	dtype=__snake_case       ,	shape=__snake_case       ,	mode="r"     )

      if len(weight_info["shape"]     ) == 0:
            UpperCAmelCase_	:						Optional[Any]					   =		weight[0]
      UpperCAmelCase_	:						List[str]					   =		torch.tensor(__snake_case     )
      if weight_info["dtype"] == "bfloat16":
            UpperCAmelCase_	:						Union[str, Any]					   =		weight.view(torch.bfloataa     )

      return weight







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  List[Any]:






      '''simple docstring'''







      if index is None or len(__snake_case     ) == 0:
            # Nothing to save
            return

      UpperCAmelCase_	:						str					   =		os.path.join(__snake_case       ,	"index.json"     )
      if os.path.isfile(__snake_case     ):
            with open(__snake_case       ,	"r"       ,	encoding="utf-8"     ) as f:
                  UpperCAmelCase_	:						str					   =		json.load(__snake_case     )
      else:
            UpperCAmelCase_	:						Dict					   =		{}
      current_index.update(__snake_case     )

      with open(__snake_case       ,	"w"       ,	encoding="utf-8"     ) as f:
            json.dump(__snake_case       ,	__snake_case       ,	indent=2     )







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Union[str, Any]:






      '''simple docstring'''







      os.makedirs(__snake_case       ,	exist_ok=__snake_case     )
      UpperCAmelCase_	:						Any					   =		{}
      for name, parameter in state_dict.items():
            UpperCAmelCase_	:						Tuple					   =		offload_weight(__snake_case       ,	__snake_case       ,	__snake_case       ,	index=__snake_case     )

      # Update index
      save_offload_index(__snake_case       ,	__snake_case     )


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       def __init__(  self  ,lowercase  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[Any]					   =		dataset
             UpperCAmelCase_	:						Any					   =		prefix



       def __getitem__(  self  ,lowercase):

             """simple docstring"""





             return self.dataset[F"""{self.prefix}{key}"""]



       def __iter__(  self):

             """simple docstring"""





             return iter([key for key in self.dataset if key.startswith(self.prefix)])



       def __len__(  self):

             """simple docstring"""





             return len(self.dataset)


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       def __init__(  self  ,lowercase = None  ,lowercase = None  ,lowercase = None  ,lowercase=None  ,):

             """simple docstring"""





             if state_dict is None and save_folder is None:
                   raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights.")

             UpperCAmelCase_	:						Tuple					   =		{} if state_dict is None else state_dict
             UpperCAmelCase_	:						List[Any]					   =		save_folder
             if index is None and save_folder is not None:
                   with open(os.path.join(lowercase  ,"index.json")) as f:
                         UpperCAmelCase_	:						Dict					   =		json.load(lowercase)
             UpperCAmelCase_	:						List[str]					   =		{} if index is None else index
             UpperCAmelCase_	:						int					   =		list(self.state_dict.keys())
             self.all_keys.extend([key for key in self.index if key not in self.all_keys])
             UpperCAmelCase_	:						Optional[Any]					   =		device



       def __getitem__(  self  ,lowercase):

             """simple docstring"""





             if key in self.state_dict:
                   return self.state_dict[key]
             UpperCAmelCase_	:						str					   =		self.index[key]
             if weight_info.get("safetensors_file") is not None:
                   if not is_safetensors_available():
                         raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`.")

                   from safetensors import safe_open

                   UpperCAmelCase_	:						Any					   =		"cpu" if self.device is None else self.device
                   with safe_open(weight_info["safetensors_file"]  ,framework="pt"  ,device=lowercase) as f:
                         UpperCAmelCase_	:						int					   =		f.get_tensor(weight_info.get("weight_name"  ,lowercase))

                   if "dtype" in weight_info:
                         return tensor.to(getattr(lowercase  ,weight_info["dtype"]))
                   else:
                         return tensor

             UpperCAmelCase_	:						Optional[int]					   =		os.path.join(self.save_folder  ,F"""{key}.dat""")
             return load_offloaded_weight(lowercase  ,lowercase)



       def __iter__(  self):

             """simple docstring"""





             return iter(self.all_keys)



       def __len__(  self):

             """simple docstring"""





             return len(self.all_keys)







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						str					   =		{}
      for module_name in submodule_names:
            # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
            # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
            result.update(
                {
                    key: param
                    for key, param in state_dict.items()
                    if key == module_name or key.startswith(module_name + "."     )
                }     )
      return result
