

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

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left





@flax.struct.dataclass
class     snake_case  :


   '''simple docstring'''







   UpperCamelCase__	:       Optional[jnp.ndarray]     =      None
   UpperCamelCase__	:       Optional[jnp.ndarray]     =      None
   UpperCamelCase__	:       Optional[jnp.ndarray]     =      None




   @classmethod
   def 	UpperCAmelCase       (					cls     :	Optional[Any]			)					->List[Any]:
     '''simple docstring'''







     return cls()





@dataclass
class     snake_case  (	_snake_case							):


   '''simple docstring'''







   UpperCamelCase__	:       ScoreSdeVeSchedulerState
   UpperCamelCase__	:       jnp.ndarray
   UpperCamelCase__	:       Optional[jnp.ndarray]     =      None





class     snake_case  (	_snake_case ,  _snake_case							):


   '''simple docstring'''







   @property
   def 	UpperCAmelCase       (					self     :	str			)					->List[str]:
     '''simple docstring'''







     return True




   @register_to_config
   def __init__(					self     :	Any						,    lowerCamelCase_     :	int = 2000						,    lowerCamelCase_     :	float = 0.15						,    lowerCamelCase_     :	float = 0.01						,    lowerCamelCase_     :	float = 1348.0						,    lowerCamelCase_     :	float = 1E-5						,    lowerCamelCase_     :	int = 1						,    )					->List[Any]:
     '''simple docstring'''







     pass




   def 	UpperCAmelCase       (					self     :	str			)					->List[str]:
     '''simple docstring'''







     UpperCAmelCase__    						=   ScoreSdeVeSchedulerState.create()
     return self.set_sigmas(
         lowerCamelCase_						,    self.config.num_train_timesteps						,    self.config.sigma_min						,    self.config.sigma_max						,    self.config.sampling_eps						,    )




   def 	UpperCAmelCase       (					self     :	Any						,    lowerCamelCase_     :	ScoreSdeVeSchedulerState						,    lowerCamelCase_     :	int						,    lowerCamelCase_     :	Tuple = ()						,    lowerCamelCase_     :	float = None			)					->ScoreSdeVeSchedulerState:
     '''simple docstring'''







     UpperCAmelCase__    						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

     UpperCAmelCase__    						=   jnp.linspace(1						,    lowerCamelCase_						,    lowerCamelCase_			)
     return state.replace(timesteps=lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	List[Any]						,    lowerCamelCase_     :	ScoreSdeVeSchedulerState						,    lowerCamelCase_     :	int						,    lowerCamelCase_     :	float = None						,    lowerCamelCase_     :	float = None						,    lowerCamelCase_     :	float = None						,    )					->ScoreSdeVeSchedulerState:
     '''simple docstring'''







     UpperCAmelCase__    						=   sigma_min if sigma_min is not None else self.config.sigma_min
     UpperCAmelCase__    						=   sigma_max if sigma_max is not None else self.config.sigma_max
     UpperCAmelCase__    						=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if state.timesteps is None:
       UpperCAmelCase__    						=   self.set_timesteps(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)

     UpperCAmelCase__    						=   jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_			)						,    jnp.log(lowerCamelCase_			)						,    lowerCamelCase_			)			)
     UpperCAmelCase__    						=   jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]			)

     return state.replace(discrete_sigmas=lowerCamelCase_						,    sigmas=lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Optional[int]						,    lowerCamelCase_     :	str						,    lowerCamelCase_     :	Optional[Any]						,    lowerCamelCase_     :	Optional[Any]			)					->List[Any]:
     '''simple docstring'''







     return jnp.where(timesteps == 0						,    jnp.zeros_like(lowerCamelCase_			)						,    state.discrete_sigmas[timesteps - 1]			)




   def 	UpperCAmelCase       (					self     :	Optional[Any]						,    lowerCamelCase_     :	ScoreSdeVeSchedulerState						,    lowerCamelCase_     :	jnp.ndarray						,    lowerCamelCase_     :	int						,    lowerCamelCase_     :	jnp.ndarray						,    lowerCamelCase_     :	random.KeyArray						,    lowerCamelCase_     :	bool = True						,    )					->Union[FlaxSdeVeOutput, Tuple]:
     '''simple docstring'''







     if state.timesteps is None:
       raise ValueError(
           """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

     UpperCAmelCase__    						=   timestep * jnp.ones(
         sample.shape[0]						,    )
     UpperCAmelCase__    						=   (timestep * (len(state.timesteps			) - 1)).long()

     UpperCAmelCase__    						=   state.discrete_sigmas[timesteps]
     UpperCAmelCase__    						=   self.get_adjacent_sigma(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
     UpperCAmelCase__    						=   jnp.zeros_like(lowerCamelCase_			)
     UpperCAmelCase__    						=   (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
     UpperCAmelCase__    						=   diffusion.flatten()
     UpperCAmelCase__    						=   broadcast_to_shape_from_left(lowerCamelCase_						,    sample.shape			)
     UpperCAmelCase__    						=   drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     UpperCAmelCase__    						=   random.split(lowerCamelCase_						,    num=1			)
     UpperCAmelCase__    						=   random.normal(key=lowerCamelCase_						,    shape=sample.shape			)
     UpperCAmelCase__    						=   sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     UpperCAmelCase__    						=   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

     if not return_dict:
       return (prev_sample, prev_sample_mean, state)

     return FlaxSdeVeOutput(prev_sample=lowerCamelCase_						,    prev_sample_mean=lowerCamelCase_						,    state=lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	List[str]						,    lowerCamelCase_     :	ScoreSdeVeSchedulerState						,    lowerCamelCase_     :	jnp.ndarray						,    lowerCamelCase_     :	jnp.ndarray						,    lowerCamelCase_     :	random.KeyArray						,    lowerCamelCase_     :	bool = True						,    )					->Union[FlaxSdeVeOutput, Tuple]:
     '''simple docstring'''







     if state.timesteps is None:
       raise ValueError(
           """`state.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
     UpperCAmelCase__    						=   random.split(lowerCamelCase_						,    num=1			)
     UpperCAmelCase__    						=   random.normal(key=lowerCamelCase_						,    shape=sample.shape			)

     # compute step size from the model_output, the noise, and the snr
     UpperCAmelCase__    						=   jnp.linalg.norm(lowerCamelCase_			)
     UpperCAmelCase__    						=   jnp.linalg.norm(lowerCamelCase_			)
     UpperCAmelCase__    						=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
     UpperCAmelCase__    						=   step_size * jnp.ones(sample.shape[0]			)

     # compute corrected sample: model_output term and noise term
     UpperCAmelCase__    						=   step_size.flatten()
     UpperCAmelCase__    						=   broadcast_to_shape_from_left(lowerCamelCase_						,    sample.shape			)
     UpperCAmelCase__    						=   sample + step_size * model_output
     UpperCAmelCase__    						=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
       return (prev_sample, state)

     return FlaxSdeVeOutput(prev_sample=lowerCamelCase_						,    state=lowerCamelCase_			)







   def __len__(					self     :	List[str]			)					->Optional[int]:
     '''simple docstring'''







     return self.config.num_train_timesteps










# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


snake_case__ :						str     =			'''Update an existing config file with the latest defaults while maintaining the old configuration.'''


def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   args.config_file
  if config_file is None and Path(_lowerCAmelCase      ).exists():
    UpperCAmelCase__    						=   default_config_file
  elif not Path(_lowerCAmelCase      ).exists():
    raise ValueError(F'''The passed config file located at {config_file} doesn\'t exist.'''      )
  UpperCAmelCase__    						=   load_config_from_file(_lowerCAmelCase      )

  if config_file.endswith(""".json"""      ):
    config.to_json_file(_lowerCAmelCase      )
  else:
    config.to_yaml_file(_lowerCAmelCase      )
  return config_file


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   parser.add_parser("""update"""   ,     parents=_lowerCAmelCase   ,     help=_lowerCAmelCase   ,     formatter_class=_lowerCAmelCase      )
  parser.add_argument(
      """--config_file"""   ,     default=_lowerCAmelCase   ,     help=(
          """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )   ,     )

  parser.set_defaults(func=_lowerCAmelCase      )
  return parser





def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   update_config(_lowerCAmelCase      )
  print(F'''Sucessfully updated the configuration file at {config_file}.'''      )




