



"""simple docstring"""
# 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 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     CommonSchedulerState
					_A						:     jnp.ndarray

					# setable values
					_A						:     jnp.ndarray
					_A						:     jnp.ndarray
					_A						:     Optional[int]		=	None






					@classmethod
					def   lowerCAmelCase_							(			cls:				str		,	snake_case:				CommonSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      str:
											return cls(
											    common=snake_case		,	final_alpha_cumprod=snake_case		,	init_noise_sigma=snake_case		,	timesteps=snake_case		,	)

@dataclass
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     DDIMSchedulerState

class 							lowerCamelCase    (     _lowerCAmelCase       ,		_lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Any		=	[e.name for e in FlaxKarrasDiffusionSchedulers]

					_A						:     jnp.dtype






					@property
					def   lowerCAmelCase_							(			self:				int		)  ->      Optional[Any]:
											return True






					@register_to_config
					def __init__(			self:				Dict		,	snake_case:				int = 1_000		,	snake_case:				float = 0.0_0_0_1		,	snake_case:				float = 0.0_2		,	snake_case:				str = "linear"		,	snake_case:				Optional[jnp.ndarray] = None		,	snake_case:				bool = True		,	snake_case:				int = 0		,	snake_case:				str = "epsilon"		,	snake_case:				jnp.dtype = jnp.floataa		,	)  ->      int:
											snake_case_					:int		=     dtype






					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				Optional[CommonSchedulerState] = None		)  ->      DDIMSchedulerState:
											if common is None:
																	snake_case_					:str		=     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.
											snake_case_					:Union[str, Any]		=     (
											    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
											snake_case_					:Optional[Any]		=     jnp.array(1.0		,	dtype=self.dtype		)

											snake_case_					:List[Any]		=     jnp.arange(0		,	self.config.num_train_timesteps		).round()[::-1]

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






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				Optional[int] = None		)  ->      jnp.ndarray:
											return sample






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				DDIMSchedulerState		,	snake_case:				int		,	snake_case:				Tuple = ()		)  ->      DDIMSchedulerState:
											snake_case_					:str		=     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
											snake_case_					:List[str]		=     (jnp.arange(0		,	snake_case		) * step_ratio).round()[::-1] + self.config.steps_offset

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






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				DDIMSchedulerState		,	snake_case:				Union[str, Any]		,	snake_case:				int		)  ->      str:
											snake_case_					:List[Any]		=     state.common.alphas_cumprod[timestep]
											snake_case_					:Union[str, Any]		=     jnp.where(
											    prev_timestep >= 0		,	state.common.alphas_cumprod[prev_timestep]		,	state.final_alpha_cumprod		)
											snake_case_					:Dict		=     1 - alpha_prod_t
											snake_case_					:List[str]		=     1 - alpha_prod_t_prev

											snake_case_					:List[Any]		=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance






					def   lowerCAmelCase_							(			self:				Union[str, Any]		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				int		,	snake_case:				jnp.ndarray		,	snake_case:				float = 0.0		,	snake_case:				bool = True		,	)  ->      Union[FlaxDDIMSchedulerOutput, Tuple]:
											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)
											snake_case_					:Any		=     timestep - self.config.num_train_timesteps // state.num_inference_steps

											snake_case_					:Union[str, Any]		=     state.common.alphas_cumprod
											snake_case_					:int		=     state.final_alpha_cumprod

											# 2. compute alphas, betas
											snake_case_					:int		=     alphas_cumprod[timestep]
											snake_case_					:Optional[int]		=     jnp.where(prev_timestep >= 0		,	alphas_cumprod[prev_timestep]		,	snake_case		)

											snake_case_					:List[Any]		=     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_					:str		=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																	snake_case_					:Any		=     model_output
											elif self.config.prediction_type == "sample":
																	snake_case_					:Optional[Any]		=     model_output
																	snake_case_					:List[Any]		=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																	snake_case_					:str		=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																	snake_case_					:Union[str, Any]		=     (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)
											snake_case_					:Optional[int]		=     self._get_variance(snake_case		,	snake_case		,	snake_case		)
											snake_case_					:Union[str, Any]		=     eta * variance ** 0.5

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											snake_case_					: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
											snake_case_					:Optional[int]		=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																	return (prev_sample, state)

											return FlaxDDIMSchedulerOutput(prev_sample=snake_case		,	state=snake_case		)






					def   lowerCAmelCase_							(			self:				Dict		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      jnp.ndarray:
											return add_noise_common(state.common		,	snake_case		,	snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				int		,	snake_case:				DDIMSchedulerState		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	snake_case:				jnp.ndarray		,	)  ->      jnp.ndarray:
											return get_velocity_common(state.common		,	snake_case		,	snake_case		,	snake_case		)






					def __len__(			self:				List[Any]		)  ->      List[Any]:
											return self.config.num_train_timesteps





"""simple docstring"""
# 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


__a           =							"Update an existing config file with the latest defaults while maintaining the old configuration."
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:int		=     args.config_file
						if config_file is None and Path(_lowercase      ).exists():
												snake_case_					:str		=     default_config_file
						elif not Path(_lowercase      ).exists():
												raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""      )
						snake_case_					:str		=     load_config_from_file(_lowercase      )

						if config_file.endswith(""".json"""      ):
												config.to_json_file(_lowercase      )
						else:
												config.to_yaml_file(_lowercase      )
						return config_file
def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Any		=     parser.add_parser("""update""",   parents=_lowercase,   help=_lowercase,   formatter_class=_lowercase      )
						parser.add_argument(
						    """--config_file""",   default=_lowercase,   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=_lowercase      )
						return parser

def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:str		=     update_config(_lowercase      )
						print(f"""Sucessfully updated the configuration file at {config_file}."""      )
