


"""simple docstring"""






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

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class 							A_      (       _a ):
  lowerCAmelCase__      			=	42
  lowerCAmelCase__      			=	42




class 							A_      (       _a     ,	_a ):
  lowerCAmelCase__      			=	1


  @register_to_config
  def __init__(			self:						List[Any]							,__lowerCAmelCase:						int = 2_000							,__lowerCAmelCase:						float = 0.15							,__lowerCAmelCase:						float = 0.01							,__lowerCAmelCase:						float = 13_48.0							,__lowerCAmelCase:						float = 1e-5							,__lowerCAmelCase:						int = 1							,):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     sigma_max

     # setable values
     _lowerCamelCase   :				Tuple							=     None

     self.set_sigmas(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    )


  def 							_lowercase					(			self:						Union[str, Any]							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						Optional[int] = None    ):







     '''simple docstring'''







     return sample


  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						int							,__lowerCAmelCase:						float = None							,__lowerCAmelCase:						Union[str, torch.device] = None    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     sampling_eps if sampling_eps is not None else self.config.sampling_eps

     _lowerCamelCase   :				str							=     torch.linspace(1							,__lowerCAmelCase							,__lowerCAmelCase							,device=__lowerCAmelCase    )


  def 							_lowercase					(			self:						int							,__lowerCAmelCase:						int							,__lowerCAmelCase:						float = None							,__lowerCAmelCase:						float = None							,__lowerCAmelCase:						float = None    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     sigma_min if sigma_min is not None else self.config.sigma_min
     _lowerCamelCase   :				List[Any]							=     sigma_max if sigma_max is not None else self.config.sigma_max
     _lowerCamelCase   :				Any							=     sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
        self.set_timesteps(__lowerCAmelCase							,__lowerCAmelCase    )

     _lowerCamelCase   :				Dict							=     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
     _lowerCamelCase   :				str							=     torch.exp(torch.linspace(math.log(__lowerCAmelCase    )							,math.log(__lowerCAmelCase    )							,__lowerCAmelCase    )    )
     _lowerCamelCase   :				Tuple							=     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )


  def 							_lowercase					(			self:						Optional[Any]							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						Optional[Any]    ):







     '''simple docstring'''







     return torch.where(
         timesteps == 0							,torch.zeros_like(t.to(timesteps.device    )    )							,self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,)


  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						int							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						Optional[torch.Generator] = None							,__lowerCAmelCase:						bool = True							,):







     '''simple docstring'''







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

     _lowerCamelCase   :				Optional[int]							=     timestep * torch.ones(
         sample.shape[0]							,device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
     _lowerCamelCase   :				Dict							=     (timestep * (len(self.timesteps    ) - 1)).long()

     # mps requires indices to be in the same device, so we use cpu as is the default with cuda
     _lowerCamelCase   :				Any							=     timesteps.to(self.discrete_sigmas.device    )

     _lowerCamelCase   :				Optional[Any]							=     self.discrete_sigmas[timesteps].to(sample.device    )
     _lowerCamelCase   :				Optional[Any]							=     self.get_adjacent_sigma(__lowerCAmelCase							,__lowerCAmelCase    ).to(sample.device    )
     _lowerCamelCase   :				Optional[Any]							=     torch.zeros_like(__lowerCAmelCase    )
     _lowerCamelCase   :				Tuple							=     (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
     _lowerCamelCase   :				Optional[int]							=     diffusion.flatten()
     while len(diffusion.shape    ) < len(sample.shape    ):
        _lowerCamelCase   :				Tuple							=     diffusion.unsqueeze(-1    )
     _lowerCamelCase   :				Any							=     drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     _lowerCamelCase   :				str							=     randn_tensor(
         sample.shape							,layout=sample.layout							,generator=__lowerCAmelCase							,device=sample.device							,dtype=sample.dtype    )
     _lowerCamelCase   :				int							=     sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     _lowerCamelCase   :				Optional[int]							=     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

     if not return_dict:
        return (prev_sample, prev_sample_mean)

     return SdeVeOutput(prev_sample=__lowerCAmelCase							,prev_sample_mean=__lowerCAmelCase    )


  def 							_lowercase					(			self:						str							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						Optional[torch.Generator] = None							,__lowerCAmelCase:						bool = True							,):







     '''simple docstring'''







     if self.timesteps is None:
        raise ValueError(
            "`self.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
     _lowerCamelCase   :				Optional[Any]							=     randn_tensor(sample.shape							,layout=sample.layout							,generator=__lowerCAmelCase    ).to(sample.device    )

     # compute step size from the model_output, the noise, and the snr
     _lowerCamelCase   :				Union[str, Any]							=     torch.norm(model_output.reshape(model_output.shape[0]							,-1    )							,dim=-1    ).mean()
     _lowerCamelCase   :				List[Any]							=     torch.norm(noise.reshape(noise.shape[0]							,-1    )							,dim=-1    ).mean()
     _lowerCamelCase   :				Optional[Any]							=     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
     _lowerCamelCase   :				Optional[Any]							=     step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
     # self.repeat_scalar(step_size, sample.shape[0])

     # compute corrected sample: model_output term and noise term
     _lowerCamelCase   :				Dict							=     step_size.flatten()
     while len(step_size.shape    ) < len(sample.shape    ):
        _lowerCamelCase   :				Optional[int]							=     step_size.unsqueeze(-1    )
     _lowerCamelCase   :				Any							=     sample + step_size * model_output
     _lowerCamelCase   :				List[str]							=     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=__lowerCAmelCase    )


  def 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						torch.FloatTensor							,__lowerCAmelCase:						torch.FloatTensor							,):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     timesteps.to(original_samples.device    )
     _lowerCamelCase   :				str							=     self.discrete_sigmas.to(original_samples.device    )[timesteps]
     _lowerCamelCase   :				Optional[Any]							=     (
         noise * sigmas[:, None, None, None]
         if noise is not None
         else torch.randn_like(__lowerCAmelCase    ) * sigmas[:, None, None, None]
     )
     _lowerCamelCase   :				Optional[Any]							=     noise + original_samples
     return noisy_samples






  def __len__(			self:						List[str]    ):







     '''simple docstring'''







     return self.config.num_train_timesteps
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


SCREAMING_SNAKE_CASE		:     List[str]         =	NewType("DataClass", Any)
SCREAMING_SNAKE_CASE		:     Optional[int]         =	NewType("DataClassType", Any)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str			):
						if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
												return v
						if v.lower() in ("yes", "true", "t", "y", "1"):
												return True
						elif v.lower() in ("no", "false", "f", "n", "0"):
												return False
						else:
												raise ArgumentTypeError(
												    f'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				list			):
						UpperCamelCase_		:							Tuple  =		{str(_SCREAMING_SNAKE_CASE			): choice for choice in choices}
						return lambda _SCREAMING_SNAKE_CASE			: str_to_choice.get(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   *,
    _SCREAMING_SNAKE_CASE     :				Union[str, List[str]] = None		, _SCREAMING_SNAKE_CASE     :				str = None		, _SCREAMING_SNAKE_CASE     :				Any = dataclasses.MISSING		, _SCREAMING_SNAKE_CASE     :				Callable[[], Any] = dataclasses.MISSING		, _SCREAMING_SNAKE_CASE     :				dict = None		, **_SCREAMING_SNAKE_CASE     :				List[str]		, ):
						if metadata is None:
												# Important, don't use as default param in function signature because dict is mutable and shared across function calls
												UpperCamelCase_		:							Union[str, Any]  =		{}
						if aliases is not None:
												UpperCamelCase_		:							Optional[Any]  =		aliases
						if help is not None:
												UpperCamelCase_		:							List[str]  =		help

						return dataclasses.field(metadata=_SCREAMING_SNAKE_CASE		, default=_SCREAMING_SNAKE_CASE		, default_factory=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE			)







class        UpperCamelCase						(				__a				):
						a__		:Iterable[DataClassType]





						def __init__(self			,     __UpperCamelCase			,     **__UpperCamelCase						)				->				Optional[int]:
												# To make the default appear when using --help
												if "formatter_class" not in kwargs:
																		UpperCamelCase_		:							int  =		ArgumentDefaultsHelpFormatter
												super().__init__(**__UpperCamelCase						)
												if dataclasses.is_dataclass(__UpperCamelCase						):
																		UpperCamelCase_		:							str  =		[dataclass_types]
												UpperCamelCase_		:							Optional[int]  =		list(__UpperCamelCase						)
												for dtype in self.dataclass_types:
																		self._add_dataclass_arguments(__UpperCamelCase						)





						@staticmethod
						def        A_							(__UpperCamelCase			,     __UpperCamelCase						)				->				List[str]:
												UpperCamelCase_		:							Any  =		f'''--{field.name}'''
												UpperCamelCase_		:							int  =		field.metadata.copy()
												# field.metadata is not used at all by Data Classes,
												# it is provided as a third-party extension mechanism.
												if isinstance(field.type			,     __UpperCamelCase						):
																		raise RuntimeError(
																		    """Unresolved type detected, which should have been done with the help of """
																		    """`typing.get_type_hints` method by default"""						)

												UpperCamelCase_		:							Any  =		kwargs.pop("""aliases"""			,     []						)
												if isinstance(__UpperCamelCase			,     __UpperCamelCase						):
																		UpperCamelCase_		:							str  =		[aliases]

												UpperCamelCase_		:							Optional[int]  =		getattr(field.type			,     """__origin__"""			,     field.type						)
												if origin_type is Union or (hasattr(__UpperCamelCase			,     """UnionType"""						) and isinstance(__UpperCamelCase			,     types.UnionType						)):
																		if str not in field.type.__args__ and (
																		    len(field.type.__args__						) != 2 or type(__UpperCamelCase						) not in field.type.__args__
																		):
																								raise ValueError(
																								    """Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
																								    """ the argument parser only supports one type per argument."""
																								    f''' Problem encountered in field \'{field.name}\'.'''						)
																		if type(__UpperCamelCase						) not in field.type.__args__:
																								# filter `str` in Union
																								UpperCamelCase_		:							str  =		field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																								UpperCamelCase_		:							Tuple  =		getattr(field.type			,     """__origin__"""			,     field.type						)
																		elif bool not in field.type.__args__:
																								# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																								UpperCamelCase_		:							Optional[Any]  =		(
																								    field.type.__args__[0] if isinstance(__UpperCamelCase			,     field.type.__args__[1]						) else field.type.__args__[1]
																								)
																								UpperCamelCase_		:							List[Any]  =		getattr(field.type			,     """__origin__"""			,     field.type						)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
												UpperCamelCase_		:							int  =		{}
												if origin_type is Literal or (isinstance(field.type			,     __UpperCamelCase						) and issubclass(field.type			,     __UpperCamelCase						)):
																		if origin_type is Literal:
																								UpperCamelCase_		:							List[str]  =		field.type.__args__
																		else:
																								UpperCamelCase_		:							Optional[Any]  =		[x.value for x in field.type]

																		UpperCamelCase_		:							str  =		make_choice_type_function(kwargs["""choices"""]						)

																		if field.default is not dataclasses.MISSING:
																								UpperCamelCase_		:							Tuple  =		field.default
																		else:
																								UpperCamelCase_		:							Optional[Any]  =		True
												elif field.type is bool or field.type == Optional[bool]:
																		# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
																		# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
																		UpperCamelCase_		:							Optional[int]  =		copy(__UpperCamelCase						)

																		# Hack because type=bool in argparse does not behave as we want.
																		UpperCamelCase_		:							Optional[Any]  =		string_to_bool
																		if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																								# Default value is False if we have no default when of type bool.
																								UpperCamelCase_		:							List[str]  =		False if field.default is dataclasses.MISSING else field.default
																								# This is the value that will get picked if we don't include --field_name in any way
																								UpperCamelCase_		:							List[Any]  =		default
																								# This tells argparse we accept 0 or 1 value after --field_name
																								UpperCamelCase_		:							Optional[Any]  =		"""?"""
																								# This is the value that will get picked if we do --field_name (without value)
																								UpperCamelCase_		:							Union[str, Any]  =		True
												elif isclass(__UpperCamelCase						) and issubclass(__UpperCamelCase			,     __UpperCamelCase						):
																		UpperCamelCase_		:							Optional[Any]  =		field.type.__args__[0]
																		UpperCamelCase_		:							str  =		"""+"""
																		if field.default_factory is not dataclasses.MISSING:
																								UpperCamelCase_		:							List[Any]  =		field.default_factory()
																		elif field.default is dataclasses.MISSING:
																								UpperCamelCase_		:							Dict  =		True
												else:
																		UpperCamelCase_		:							List[str]  =		field.type
																		if field.default is not dataclasses.MISSING:
																								UpperCamelCase_		:							List[Any]  =		field.default
																		elif field.default_factory is not dataclasses.MISSING:
																								UpperCamelCase_		:							Optional[Any]  =		field.default_factory()
																		else:
																								UpperCamelCase_		:							Optional[Any]  =		True
												parser.add_argument(__UpperCamelCase			,     *__UpperCamelCase			,     **__UpperCamelCase						)

												# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
												# Order is important for arguments with the same destination!
												# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
												# here and we do not need those changes/additional keys.
												if field.default is True and (field.type is bool or field.type == Optional[bool]):
																		UpperCamelCase_		:							str  =		False
																		parser.add_argument(f'''--no_{field.name}'''			,     action="""store_false"""			,     dest=field.name			,     **__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												if hasattr(__UpperCamelCase			,     """_argument_group_name"""						):
																		UpperCamelCase_		:							str  =		self.add_argument_group(dtype._argument_group_name						)
												else:
																		UpperCamelCase_		:							List[str]  =		self

												try:
																		UpperCamelCase_		:							Dict[str, type]  =		get_type_hints(__UpperCamelCase						)
												except NameError:
																		raise RuntimeError(
																		    f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
																		    """removing line of `from __future__ import annotations` which opts in Postponed """
																		    """Evaluation of Annotations (PEP 563)"""						)
												except TypeError as ex:
																		# Remove this block when we drop Python 3.9 support
																		if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__UpperCamelCase						):
																								UpperCamelCase_		:							Optional[Any]  =		""".""".join(map(__UpperCamelCase			,     sys.version_info[:3]						)						)
																								raise RuntimeError(
																								    f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
																								    """line of `from __future__ import annotations` which opts in union types as """
																								    """`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
																								    """support Python versions that lower than 3.10, you need to use """
																								    """`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
																								    """`X | None`."""						) from ex
																		raise

												for field in dataclasses.fields(__UpperCamelCase						):
																		if not field.init:
																								continue
																		UpperCamelCase_		:							Optional[Any]  =		type_hints[field.name]
																		self._parse_dataclass_field(__UpperCamelCase			,     __UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase=None			,     __UpperCamelCase=False			,     __UpperCamelCase=True			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     )				->				Tuple[DataClass, ...]:

												if args_file_flag or args_filename or (look_for_args_file and len(sys.argv						)):
																		UpperCamelCase_		:							Optional[int]  =		[]

																		if args_filename:
																								args_files.append(Path(__UpperCamelCase						)						)
																		elif look_for_args_file and len(sys.argv						):
																								args_files.append(Path(sys.argv[0]						).with_suffix(""".args"""						)						)

																		# args files specified via command line flag should overwrite default args files so we add them last
																		if args_file_flag:
																								# Create special parser just to extract the args_file_flag values
																								UpperCamelCase_		:							List[str]  =		ArgumentParser()
																								args_file_parser.add_argument(__UpperCamelCase			,     type=__UpperCamelCase			,     action="""append"""						)

																								# Use only remaining args for further parsing (remove the args_file_flag)
																								UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		args_file_parser.parse_known_args(args=__UpperCamelCase						)
																								UpperCamelCase_		:							str  =		vars(__UpperCamelCase						).get(args_file_flag.lstrip("""-"""						)			,     __UpperCamelCase						)

																								if cmd_args_file_paths:
																														args_files.extend([Path(__UpperCamelCase						) for p in cmd_args_file_paths]						)

																		UpperCamelCase_		:							Optional[Any]  =		[]
																		for args_file in args_files:
																								if args_file.exists():
																														file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
																		UpperCamelCase_		:							str  =		file_args + args if args is not None else file_args + sys.argv[1:]
												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		self.parse_known_args(args=__UpperCamelCase						)
												UpperCamelCase_		:							List[str]  =		[]
												for dtype in self.dataclass_types:
																		UpperCamelCase_		:							Any  =		{f.name for f in dataclasses.fields(__UpperCamelCase						) if f.init}
																		UpperCamelCase_		:							Optional[int]  =		{k: v for k, v in vars(__UpperCamelCase						).items() if k in keys}
																		for k in keys:
																								delattr(__UpperCamelCase			,     __UpperCamelCase						)
																		UpperCamelCase_		:							List[str]  =		dtype(**__UpperCamelCase						)
																		outputs.append(__UpperCamelCase						)
												if len(namespace.__dict__						) > 0:
																		# additional namespace.
																		outputs.append(__UpperCamelCase						)
												if return_remaining_strings:
																		return (*outputs, remaining_args)
												else:
																		if remaining_args:
																								raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''						)

																		return (*outputs,)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = False						)				->				Tuple[DataClass, ...]:
												UpperCamelCase_		:							Any  =		set(args.keys()						)
												UpperCamelCase_		:							List[str]  =		[]
												for dtype in self.dataclass_types:
																		UpperCamelCase_		:							str  =		{f.name for f in dataclasses.fields(__UpperCamelCase						) if f.init}
																		UpperCamelCase_		:							int  =		{k: v for k, v in args.items() if k in keys}
																		unused_keys.difference_update(inputs.keys()						)
																		UpperCamelCase_		:							Dict  =		dtype(**__UpperCamelCase						)
																		outputs.append(__UpperCamelCase						)
												if not allow_extra_keys and unused_keys:
																		raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(__UpperCamelCase						)}'''						)
												return tuple(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = False						)				->				Tuple[DataClass, ...]:
												with open(Path(__UpperCamelCase						)			,     encoding="""utf-8"""						) as open_json_file:
																		UpperCamelCase_		:							Tuple  =		json.loads(open_json_file.read()						)
												UpperCamelCase_		:							List[str]  =		self.parse_dict(__UpperCamelCase			,     allow_extra_keys=__UpperCamelCase						)
												return tuple(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = False						)				->				Tuple[DataClass, ...]:
												UpperCamelCase_		:							Dict  =		self.parse_dict(yaml.safe_load(Path(__UpperCamelCase						).read_text()						)			,     allow_extra_keys=__UpperCamelCase						)
												return tuple(__UpperCamelCase						)






