




"""simple docstring"""






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


UpperCamelCase__						=			NewType('''DataClass''', Any)
UpperCamelCase__						=			NewType('''DataClassType''', Any)





def 							UpperCAmelCase		(		snake_case			:					Tuple    ):
     if isinstance(snake_case      ,       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 							UpperCAmelCase		(		snake_case			:					list    ):
     _lowerCAmelCase:int									=  {str(snake_case    ): choice for choice in choices}
     return lambda snake_case    : str_to_choice.get(snake_case      ,       snake_case    )





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

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





class   a__			(       UpperCamelCase_  ):
      snake_case__             = 4_2





      def __init__(							self      :	List[Any]     ,a__      :	Union[DataClassType, Iterable[DataClassType]]     ,**a__      :	Optional[Any])						->       Any:




           """simple docstring"""







           if "formatter_class" not in kwargs:
                _lowerCAmelCase:List[Any]									=  ArgumentDefaultsHelpFormatter
           super().__init__(**a__)
           if dataclasses.is_dataclass(a__):
                _lowerCAmelCase:int									=  [dataclass_types]
           _lowerCAmelCase:List[Any]									=  list(a__)
           for dtype in self.dataclass_types:
                self._add_dataclass_arguments(a__)





      @staticmethod
      def       __UpperCamelCase     (							a__      :	ArgumentParser     ,a__      :	dataclasses.Field)						->       str:




           """simple docstring"""







           _lowerCAmelCase:Tuple									=  F'--{field.name}'
           _lowerCAmelCase: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     ,a__):
                raise RuntimeError(
                    '''Unresolved type detected, which should have been done with the help of '''
                    '''`typing.get_type_hints` method by default''')

           _lowerCAmelCase:Optional[int]									=  kwargs.pop('''aliases'''     ,[])
           if isinstance(a__     ,a__):
                _lowerCAmelCase:Union[str, Any]									=  [aliases]

           _lowerCAmelCase:Any									=  getattr(field.type     ,'''__origin__'''     ,field.type)
           if origin_type is Union or (hasattr(a__     ,'''UnionType''') and isinstance(a__     ,types.UnionType)):
                if str not in field.type.__args__ and (
                    len(field.type.__args__) != 2 or type(a__) 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(a__) not in field.type.__args__:
                     # filter `str` in Union
                     _lowerCAmelCase:int									=  field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
                     _lowerCAmelCase:Union[str, Any]									=  getattr(field.type     ,'''__origin__'''     ,field.type)
                elif bool not in field.type.__args__:
                     # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                     _lowerCAmelCase:int									=  (
                         field.type.__args__[0] if isinstance(a__     ,field.type.__args__[1]) else field.type.__args__[1]
                     )
                     _lowerCAmelCase:Tuple									=  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)
           _lowerCAmelCase:Union[str, Any]									=  {}
           if origin_type is Literal or (isinstance(field.type     ,a__) and issubclass(field.type     ,a__)):
                if origin_type is Literal:
                     _lowerCAmelCase:Optional[int]									=  field.type.__args__
                else:
                     _lowerCAmelCase:Dict									=  [x.value for x in field.type]

                _lowerCAmelCase:Any									=  make_choice_type_function(kwargs['''choices'''])

                if field.default is not dataclasses.MISSING:
                     _lowerCAmelCase:Optional[Any]									=  field.default
                else:
                     _lowerCAmelCase: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
                _lowerCAmelCase:Tuple									=  copy(a__)

                # Hack because type=bool in argparse does not behave as we want.
                _lowerCAmelCase:Dict									=  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.
                     _lowerCAmelCase:int									=  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
                     _lowerCAmelCase:Tuple									=  default
                     # This tells argparse we accept 0 or 1 value after --field_name
                     _lowerCAmelCase:Any									=  '''?'''
                     # This is the value that will get picked if we do --field_name (without value)
                     _lowerCAmelCase:str									=  True
           elif isclass(a__) and issubclass(a__     ,a__):
                _lowerCAmelCase:Dict									=  field.type.__args__[0]
                _lowerCAmelCase:Dict									=  '''+'''
                if field.default_factory is not dataclasses.MISSING:
                     _lowerCAmelCase:List[Any]									=  field.default_factory()
                elif field.default is dataclasses.MISSING:
                     _lowerCAmelCase:str									=  True
           else:
                _lowerCAmelCase:Tuple									=  field.type
                if field.default is not dataclasses.MISSING:
                     _lowerCAmelCase:List[Any]									=  field.default
                elif field.default_factory is not dataclasses.MISSING:
                     _lowerCAmelCase:Optional[int]									=  field.default_factory()
                else:
                     _lowerCAmelCase:str									=  True
           parser.add_argument(a__     ,*a__     ,**a__)

           # 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]):
                _lowerCAmelCase:Any									=  False
                parser.add_argument(F'--no_{field.name}'     ,action='''store_false'''     ,dest=field.name     ,**a__)





      def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	DataClassType)						->       Tuple:




           """simple docstring"""







           if hasattr(a__     ,'''_argument_group_name'''):
                _lowerCAmelCase:Union[str, Any]									=  self.add_argument_group(dtype._argument_group_name)
           else:
                _lowerCAmelCase:int									=  self

           try:
                _lowerCAmelCase:Dict[str, type]									=  get_type_hints(a__)
           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(a__):
                     _lowerCAmelCase:Optional[int]									=  '''.'''.join(map(a__     ,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(a__):
                if not field.init:
                     continue
                _lowerCAmelCase:int									=  type_hints[field.name]
                self._parse_dataclass_field(a__     ,a__)





      def       __UpperCamelCase     (							self      :	str     ,a__      :	str=None     ,a__      :	Union[str, Any]=False     ,a__      :	Union[str, Any]=True     ,a__      :	List[str]=None     ,a__      :	List[str]=None     ,)						->       Tuple[DataClass, ...]:




           """simple docstring"""







           if args_file_flag or args_filename or (look_for_args_file and len(sys.argv)):
                _lowerCAmelCase:str									=  []

                if args_filename:
                     args_files.append(Path(a__))
                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
                     _lowerCAmelCase:Union[str, Any]									=  ArgumentParser()
                     args_file_parser.add_argument(a__     ,type=a__     ,action='''append''')

                     # Use only remaining args for further parsing (remove the args_file_flag)
                     _lowerCAmelCase:int									=  args_file_parser.parse_known_args(args=a__)
                     _lowerCAmelCase:int									=  vars(a__).get(args_file_flag.lstrip('''-''')     ,a__)

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

                _lowerCAmelCase:List[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
                _lowerCAmelCase:Optional[Any]									=  file_args + args if args is not None else file_args + sys.argv[1:]
           _lowerCAmelCase:Optional[Any]									=  self.parse_known_args(args=a__)
           _lowerCAmelCase:List[Any]									=  []
           for dtype in self.dataclass_types:
                _lowerCAmelCase:str									=  {f.name for f in dataclasses.fields(a__) if f.init}
                _lowerCAmelCase:Any									=  {k: v for k, v in vars(a__).items() if k in keys}
                for k in keys:
                     delattr(a__     ,a__)
                _lowerCAmelCase:str									=  dtype(**a__)
                outputs.append(a__)
           if len(namespace.__dict__) > 0:
                # additional namespace.
                outputs.append(a__)
           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       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Dict[str, Any]     ,a__      :	bool = False)						->       Tuple[DataClass, ...]:




           """simple docstring"""







           _lowerCAmelCase:Dict									=  set(args.keys())
           _lowerCAmelCase:str									=  []
           for dtype in self.dataclass_types:
                _lowerCAmelCase:Optional[int]									=  {f.name for f in dataclasses.fields(a__) if f.init}
                _lowerCAmelCase:Any									=  {k: v for k, v in args.items() if k in keys}
                unused_keys.difference_update(inputs.keys())
                _lowerCAmelCase:Optional[Any]									=  dtype(**a__)
                outputs.append(a__)
           if not allow_extra_keys and unused_keys:
                raise ValueError(F'Some keys are not used by the HfArgumentParser: {sorted(a__)}')
           return tuple(a__)





      def       __UpperCamelCase     (							self      :	Any     ,a__      :	str     ,a__      :	bool = False)						->       Tuple[DataClass, ...]:




           """simple docstring"""







           with open(Path(a__)     ,encoding='''utf-8''') as open_json_file:
                _lowerCAmelCase:Union[str, Any]									=  json.loads(open_json_file.read())
           _lowerCAmelCase:Optional[Any]									=  self.parse_dict(a__     ,allow_extra_keys=a__)
           return tuple(a__)







      def       __UpperCamelCase     (							self      :	str     ,a__      :	str     ,a__      :	bool = False)						->       Tuple[DataClass, ...]:




           """simple docstring"""







           _lowerCAmelCase:str									=  self.parse_dict(yaml.safe_load(Path(a__).read_text())     ,allow_extra_keys=a__)
           return tuple(a__)












"""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__			(       UpperCamelCase_  ):
						snake_case__             = 42
						snake_case__             = None





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int=0.9_99      ,       snake_case			:					Union[str, Any]="cosine"      ,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(snake_case			:					Dict    ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(snake_case			:					Optional[Any]    ):
															return math.exp(t * -12.0    )

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

					_lowerCAmelCase:List[str]									=  []
					for i in range(snake_case    ):
										_lowerCAmelCase:Union[str, Any]									=  i / num_diffusion_timesteps
										_lowerCAmelCase:Dict									=  (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(snake_case    ) / alpha_bar_fn(snake_case    )      ,       snake_case    )    )
					return torch.tensor(snake_case      ,       dtype=torch.floataa    )





class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Optional[Any]     ,a__      :	int = 1000     ,a__      :	float = 0.0001     ,a__      :	float = 0.02     ,a__      :	str = "linear"     ,a__      :	Optional[Union[np.ndarray, List[float]]] = None     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	int = 0     ,a__      :	str = "epsilon"     ,a__      :	float = 1.0     ,**a__      :	Any     ,)						->       str:




											"""simple docstring"""







											if kwargs.get('''set_alpha_to_one'''     ,a__) is not None:
																_lowerCAmelCase:Tuple									=  (
																    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																)
																deprecate('''set_alpha_to_one'''     ,'''1.0.0'''     ,a__     ,standard_warn=a__)
																_lowerCAmelCase:Optional[Any]									=  kwargs['''set_alpha_to_one''']
											if trained_betas is not None:
																_lowerCAmelCase:Any									=  torch.tensor(a__     ,dtype=torch.floataa)
											elif beta_schedule == "linear":
																_lowerCAmelCase:str									=  torch.linspace(a__     ,a__     ,a__     ,dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_lowerCAmelCase:int									=  (
																    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,a__     ,dtype=torch.floataa) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_lowerCAmelCase:Optional[int]									=  betas_for_alpha_bar(a__)
											else:
																raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

											_lowerCAmelCase:Any									=  1.0 - self.betas
											_lowerCAmelCase:Optional[Any]									=  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.
											_lowerCAmelCase:Optional[Any]									=  torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											_lowerCAmelCase:Any									=  1.0

											# setable values
											_lowerCAmelCase:Dict									=  None
											_lowerCAmelCase:List[Any]									=  torch.from_numpy(np.arange(0     ,a__).copy().astype(np.intaa))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	torch.FloatTensor     ,a__      :	Optional[int] = None)						->       torch.FloatTensor:




											"""simple docstring"""







											return sample





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	int     ,a__      :	Union[str, torch.device] = None)						->       List[Any]:




											"""simple docstring"""







											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.')

											_lowerCAmelCase:Union[str, Any]									=  num_inference_steps
											_lowerCAmelCase:Union[str, Any]									=  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
											_lowerCAmelCase:str									=  (np.arange(0     ,a__) * step_ratio).round().copy().astype(np.intaa)
											_lowerCAmelCase:str									=  torch.from_numpy(a__).to(a__)
											self.timesteps += self.config.steps_offset





						def       __UpperCamelCase     (							self      :	int     ,a__      :	torch.FloatTensor     ,a__      :	int     ,a__      :	torch.FloatTensor     ,a__      :	float = 0.0     ,a__      :	bool = False     ,a__      :	Optional[torch.FloatTensor] = None     ,a__      :	bool = True     ,)						->       Union[DDIMSchedulerOutput, Tuple]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  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
											_lowerCAmelCase:Optional[Any]									=  self.alphas_cumprod[timestep]
											_lowerCAmelCase:List[Any]									=  (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											_lowerCAmelCase: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":
																_lowerCAmelCase:int									=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_lowerCAmelCase:str									=  model_output
											elif self.config.prediction_type == "sample":
																_lowerCAmelCase:List[str]									=  model_output
																_lowerCAmelCase:Optional[int]									=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																_lowerCAmelCase:str									=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_lowerCAmelCase:Dict									=  (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:
																_lowerCAmelCase:List[Any]									=  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
											_lowerCAmelCase:str									=  (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
											_lowerCAmelCase:Optional[Any]									=  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=a__     ,pred_original_sample=a__)







						def __len__(							self      :	Dict)						->       Union[str, Any]:




											"""simple docstring"""







											return self.config.num_train_timesteps






