






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def      __A					(     _lowercase					,			_lowercase=0.9_99					,			_lowercase="cosine"					,			):

					'''simple docstring'''







					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(_lowercase ):
															return math.exp(t * -12.0 )

					else:
										raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )

					_A								= []
					for i in range(lowerCamelCase__ ):
										_A								= i / num_diffusion_timesteps
										_A								= (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(lowerCamelCase__ ) / alpha_bar_fn(lowerCamelCase__ )					,			lowerCamelCase__ ) )
					return torch.tensor(lowerCamelCase__					,			dtype=torch.floataa )





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case					):





							"""simple docstring"""

							A_						  =					[e.name for e in KarrasDiffusionSchedulers]
							A_						  =					2

							@register_to_config
							def __init__(  self:   Tuple		,     __A:   int = 10_00		,     __A:   float = 0.00_085		,     __A:   float = 0.012		,     __A:   str = "linear"		,     __A:   Optional[Union[np.ndarray, List[float]]] = None		,     __A:   str = "epsilon"		,     __A:   Optional[bool] = False		,     __A:   Optional[bool] = False		,     __A:   float = 1.0		,     __A:   str = "linspace"		,     __A:   int = 0		,     )    ->				Optional[int]:

												if trained_betas is not None:
																	_A								= torch.tensor(__A		,     dtype=torch.floataa   )
												elif beta_schedule == "linear":
																	_A								= torch.linspace(__A		,     __A		,     __A		,     dtype=torch.floataa   )
												elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	_A								= (
																	    torch.linspace(beta_start**0.5		,     beta_end**0.5		,     __A		,     dtype=torch.floataa   ) ** 2
																	)
												elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	_A								= betas_for_alpha_bar(__A		,     alpha_transform_type='''cosine'''   )
												elif beta_schedule == "exp":
																	_A								= betas_for_alpha_bar(__A		,     alpha_transform_type='''exp'''   )
												else:
																	raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""   )

												_A								= 1.0 - self.betas
												_A								= torch.cumprod(self.alphas		,     dim=0   )

												#  set all values
												self.set_timesteps(__A		,     __A		,     __A   )
												_A								= use_karras_sigmas

							def       __A (  self:   List[str]		,     __A:   str		,     __A:   Union[str, Any]=None   )    ->				Optional[Any]:

												if schedule_timesteps is None:
																	_A								= self.timesteps

												_A								= (schedule_timesteps == timestep).nonzero()

												# The sigma index that is taken for the **very** first `step`
												# is always the second index (or the last index if there is only 1)
												# This way we can ensure we don't accidentally skip a sigma in
												# case we start in the middle of the denoising schedule (e.g. for image-to-image)
												if len(self._index_counter   ) == 0:
																	_A								= 1 if len(__A   ) > 1 else 0
												else:
																	_A								= timestep.cpu().item() if torch.is_tensor(__A   ) else timestep
																	_A								= self._index_counter[timestep_int]

												return indices[pos].item()

							@property
							def       __A (  self:   Dict   )    ->				Dict:

												if self.config.timestep_spacing in ["linspace", "trailing"]:
																	return self.sigmas.max()

												return (self.sigmas.max() ** 2 + 1) ** 0.5

							def       __A (  self:   Optional[Any]		,     __A:   torch.FloatTensor		,     __A:   Union[float, torch.FloatTensor]		,     )    ->				torch.FloatTensor:
												_A								= self.index_for_timestep(__A   )

												_A								= self.sigmas[step_index]
												_A								= sample / ((sigma**2 + 1) ** 0.5)
												return sample

							def       __A (  self:   Dict		,     __A:   int		,     __A:   Union[str, torch.device] = None		,     __A:   Optional[int] = None		,     )    ->				Optional[Any]:
												_A								= num_inference_steps

												_A								= num_train_timesteps or self.config.num_train_timesteps

												# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
												if self.config.timestep_spacing == "linspace":
																	_A								= np.linspace(0		,     num_train_timesteps - 1		,     __A		,     dtype=__A   )[::-1].copy()
												elif self.config.timestep_spacing == "leading":
																	_A								= 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
																	_A								= (np.arange(0		,     __A   ) * step_ratio).round()[::-1].copy().astype(__A   )
																	timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																	_A								= 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
																	_A								= (np.arange(__A		,     0		,     -step_ratio   )).round().copy().astype(__A   )
																	timesteps -= 1
												else:
																	raise ValueError(
																	    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""   )

												_A								= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
												_A								= np.log(__A   )
												_A								= np.interp(__A		,     np.arange(0		,     len(__A   )   )		,     __A   )

												if self.config.use_karras_sigmas:
																	_A								= self._convert_to_karras(in_sigmas=__A		,     num_inference_steps=self.num_inference_steps   )
																	_A								= np.array([self._sigma_to_t(__A		,     __A   ) for sigma in sigmas]   )

												_A								= np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
												_A								= torch.from_numpy(__A   ).to(device=__A   )
												_A								= torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2   ), sigmas[-1:]]   )

												_A								= torch.from_numpy(__A   )
												_A								= torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2   )]   )

												if str(__A   ).startswith('''mps'''   ):
																	# mps does not support float64
																	_A								= timesteps.to(__A		,     dtype=torch.floataa   )
												else:
																	_A								= timesteps.to(device=__A   )

												# empty dt and derivative
												_A								= None
												_A								= None

												# for exp beta schedules, such as the one for `pipeline_shap_e.py`
												# we need an index counter
												_A								= defaultdict(__A   )

							def       __A (  self:   Optional[Any]		,     __A:   Union[str, Any]		,     __A:   Any   )    ->				List[Any]:
												_A								= np.log(__A   )

												# get distribution
												_A								= log_sigma - log_sigmas[:, np.newaxis]

												# get sigmas range
												_A								= np.cumsum((dists >= 0)		,     axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
												_A								= low_idx + 1

												_A								= log_sigmas[low_idx]
												_A								= log_sigmas[high_idx]

												# interpolate sigmas
												_A								= (low - log_sigma) / (low - high)
												_A								= np.clip(__A		,     0		,     1   )

												# transform interpolation to time range
												_A								= (1 - w) * low_idx + w * high_idx
												_A								= t.reshape(sigma.shape   )
												return t

							def       __A (  self:   List[Any]		,     __A:   torch.FloatTensor		,     __A:   Tuple   )    ->				torch.FloatTensor:
												_A								= in_sigmas[-1].item()
												_A								= in_sigmas[0].item()

												_A								= 7.0  # 7.0 is the value used in the paper
												_A								= np.linspace(0		,     1		,     __A   )
												_A								= sigma_min ** (1 / rho)
												_A								= sigma_max ** (1 / rho)
												_A								= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas

							@property
							def       __A (  self:   Optional[Any]   )    ->				Tuple:
												return self.dt is None

							def       __A (  self:   List[Any]		,     __A:   Union[torch.FloatTensor, np.ndarray]		,     __A:   Union[float, torch.FloatTensor]		,     __A:   Union[torch.FloatTensor, np.ndarray]		,     __A:   bool = True		,     )    ->				Union[SchedulerOutput, Tuple]:
												_A								= self.index_for_timestep(__A   )

												# advance index counter by 1
												_A								= timestep.cpu().item() if torch.is_tensor(__A   ) else timestep
												self._index_counter[timestep_int] += 1

												if self.state_in_first_order:
																	_A								= self.sigmas[step_index]
																	_A								= self.sigmas[step_index + 1]
												else:
																	# 2nd order / Heun's method
																	_A								= self.sigmas[step_index - 1]
																	_A								= self.sigmas[step_index]

												# currently only gamma=0 is supported. This usually works best anyways.
												# We can support gamma in the future but then need to scale the timestep before
												# passing it to the model which requires a change in API
												_A								= 0
												_A								= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																	_A								= sigma_hat if self.state_in_first_order else sigma_next
																	_A								= sample - sigma_input * model_output
												elif self.config.prediction_type == "v_prediction":
																	_A								= sigma_hat if self.state_in_first_order else sigma_next
																	_A								= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																	    sample / (sigma_input**2 + 1)
																	)
												elif self.config.prediction_type == "sample":
																	_A								= model_output
												else:
																	raise ValueError(
																	    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""   )

												if self.config.clip_sample:
																	_A								= pred_original_sample.clamp(
																	    -self.config.clip_sample_range		,     self.config.clip_sample_range   )

												if self.state_in_first_order:
																	# 2. Convert to an ODE derivative for 1st order
																	_A								= (sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	_A								= sigma_next - sigma_hat

																	# store for 2nd order step
																	_A								= derivative
																	_A								= dt
																	_A								= sample
												else:
																	# 2. 2nd order / Heun's method
																	_A								= (sample - pred_original_sample) / sigma_next
																	_A								= (self.prev_derivative + derivative) / 2

																	# 3. take prev timestep & sample
																	_A								= self.dt
																	_A								= self.sample

																	# free dt and derivative
																	# Note, this puts the scheduler in "first order mode"
																	_A								= None
																	_A								= None
																	_A								= None

												_A								= sample + derivative * dt

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=__A   )

							def       __A (  self:   Tuple		,     __A:   torch.FloatTensor		,     __A:   torch.FloatTensor		,     __A:   torch.FloatTensor		,     )    ->				torch.FloatTensor:
												_A								= self.sigmas.to(device=original_samples.device		,     dtype=original_samples.dtype   )
												if original_samples.device.type == "mps" and torch.is_floating_point(__A   ):
																	# mps does not support float64
																	_A								= self.timesteps.to(original_samples.device		,     dtype=torch.floataa   )
																	_A								= timesteps.to(original_samples.device		,     dtype=torch.floataa   )
												else:
																	_A								= self.timesteps.to(original_samples.device   )
																	_A								= timesteps.to(original_samples.device   )

												_A								= [self.index_for_timestep(__A		,     __A   ) for t in timesteps]

												_A								= sigmas[step_indices].flatten()
												while len(sigma.shape   ) < len(original_samples.shape   ):
																	_A								= sigma.unsqueeze(-1   )

												_A								= original_samples + noise * sigma
												return noisy_samples



							def __len__(  self:   str   )    ->				List[str]:
												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


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




def      __A					(     _lowercase ):

					'''simple docstring'''







					if isinstance(_lowercase					,			_lowercase ):
										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      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= {str(_lowercase ): choice for choice in choices}
					return lambda _lowercase : str_to_choice.get(_lowercase					,			_lowercase )




def      __A					(     *,
    _lowercase = None					,			_lowercase = None					,			_lowercase = dataclasses.MISSING					,			_lowercase = dataclasses.MISSING					,			_lowercase = None					,			**_lowercase					,			):

					'''simple docstring'''







					if metadata is None:
										# Important, don't use as default param in function signature because dict is mutable and shared across function calls
										_A								= {}
					if aliases is not None:
										_A								= aliases
					if help is not None:
										_A								= help

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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					42

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

							@staticmethod
							def       __A (  __A:   ArgumentParser		,     __A:   dataclasses.Field   )    ->				str:
												_A								= f"""--{field.name}"""
												_A								= 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'''   )

												_A								= kwargs.pop('''aliases'''		,     []   )
												if isinstance(__A		,     __A   ):
																	_A								= [aliases]

												_A								= 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
																						_A								= field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																						_A								= getattr(field.type		,     '''__origin__'''		,     field.type   )
																	elif bool not in field.type.__args__:
																						# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																						_A								= (
																						    field.type.__args__[0] if isinstance(__A		,     field.type.__args__[1]   ) else field.type.__args__[1]
																						)
																						_A								= 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)
												_A								= {}
												if origin_type is Literal or (isinstance(field.type		,     __A   ) and issubclass(field.type		,     __A   )):
																	if origin_type is Literal:
																						_A								= field.type.__args__
																	else:
																						_A								= [x.value for x in field.type]

																	_A								= make_choice_type_function(kwargs['''choices''']   )

																	if field.default is not dataclasses.MISSING:
																						_A								= field.default
																	else:
																						_A								= 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
																	_A								= copy(__A   )

																	# Hack because type=bool in argparse does not behave as we want.
																	_A								= 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.
																						_A								= 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
																						_A								= default
																						# This tells argparse we accept 0 or 1 value after --field_name
																						_A								= '''?'''
																						# This is the value that will get picked if we do --field_name (without value)
																						_A								= True
												elif isclass(__A   ) and issubclass(__A		,     __A   ):
																	_A								= field.type.__args__[0]
																	_A								= '''+'''
																	if field.default_factory is not dataclasses.MISSING:
																						_A								= field.default_factory()
																	elif field.default is dataclasses.MISSING:
																						_A								= True
												else:
																	_A								= field.type
																	if field.default is not dataclasses.MISSING:
																						_A								= field.default
																	elif field.default_factory is not dataclasses.MISSING:
																						_A								= field.default_factory()
																	else:
																						_A								= 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]):
																	_A								= False
																	parser.add_argument(f"""--no_{field.name}"""		,     action='''store_false'''		,     dest=field.name		,     **__A   )

							def       __A (  self:   Dict		,     __A:   DataClassType   )    ->				List[Any]:
												if hasattr(__A		,     '''_argument_group_name'''   ):
																	_A								= self.add_argument_group(dtype._argument_group_name   )
												else:
																	_A								= self

												try:
																	_A								= 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   ):
																						_A								= '''.'''.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
																	_A								= type_hints[field.name]
																	self._parse_dataclass_field(__A		,     __A   )

							def       __A (  self:   int		,     __A:   Any=None		,     __A:   int=False		,     __A:   Any=True		,     __A:   Optional[Any]=None		,     __A:   Any=None		,     )    ->				Tuple[DataClass, ...]:

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

																	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
																						_A								= ArgumentParser()
																						args_file_parser.add_argument(__A		,     type=__A		,     action='''append'''   )

																						# Use only remaining args for further parsing (remove the args_file_flag)
																						_A							,_A								= args_file_parser.parse_known_args(args=__A   )
																						_A								= 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]   )

																	_A								= []
																	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
																	_A								= file_args + args if args is not None else file_args + sys.argv[1:]
												_A							,_A								= self.parse_known_args(args=__A   )
												_A								= []
												for dtype in self.dataclass_types:
																	_A								= {f.name for f in dataclasses.fields(__A   ) if f.init}
																	_A								= {k: v for k, v in vars(__A   ).items() if k in keys}
																	for k in keys:
																						delattr(__A		,     __A   )
																	_A								= 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       __A (  self:   Tuple		,     __A:   Dict[str, Any]		,     __A:   bool = False   )    ->				Tuple[DataClass, ...]:
												_A								= set(args.keys()   )
												_A								= []
												for dtype in self.dataclass_types:
																	_A								= {f.name for f in dataclasses.fields(__A   ) if f.init}
																	_A								= {k: v for k, v in args.items() if k in keys}
																	unused_keys.difference_update(inputs.keys()   )
																	_A								= 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       __A (  self:   Tuple		,     __A:   str		,     __A:   bool = False   )    ->				Tuple[DataClass, ...]:
												with open(Path(__A   )		,     encoding='''utf-8'''   ) as open_json_file:
																	_A								= json.loads(open_json_file.read()   )
												_A								= self.parse_dict(__A		,     allow_extra_keys=__A   )
												return tuple(__A   )



							def       __A (  self:   List[Any]		,     __A:   str		,     __A:   bool = False   )    ->				Tuple[DataClass, ...]:
												_A								= self.parse_dict(yaml.safe_load(Path(__A   ).read_text()   )		,     allow_extra_keys=__A   )
												return tuple(__A   )



