



"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline






"""simple docstring"""

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowercase__			   =  logging.get_logger(__name__)




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"linear"
						_lowerCAmelCase							=					"cosine"
						_lowerCAmelCase							=					"cosine_with_restarts"
						_lowerCAmelCase							=					"polynomial"
						_lowerCAmelCase							=					"constant"
						_lowerCAmelCase							=					"constant_with_warmup"
						_lowerCAmelCase							=					"piecewise_constant"







def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int = -1					):
							return LambdaLR(_lowerCamelCase  ,       lambda _lowerCamelCase					: 1  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1.0  ,       _lowerCamelCase					)					)
														return 1.0

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int = -1					):
							__a		:		Optional[int]									=       {}
							__a		:		Any									=       step_rules.split(""","""					)
							for rule_str in rule_list[:-1]:
														__a							,     __a		:		int									=       rule_str.split(""":"""					)
														__a		:		Optional[int]									=       int(_lowerCamelCase					)
														__a		:		str									=       float(_lowerCamelCase					)
														__a		:		int									=       value
							__a		:		Dict									=       float(rule_list[-1]					)

							def create_rules_function(_lowerCamelCase				: str  ,       _lowerCamelCase				: Tuple					):
														def rule_func(_lowerCamelCase				: int					) -> float:
																					__a		:		Optional[Any]									=       sorted(rules_dict.keys()					)
																					for i, sorted_step in enumerate(_lowerCamelCase					):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							__a		:		Optional[int]									=       create_rules_function(_lowerCamelCase  ,       _lowerCamelCase					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: str=-1					):

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														return max(
														    0.0  ,       float(num_training_steps - current_step					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: float = 0.5  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Any					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		Dict									=       float(current_step - num_warmup_steps					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)
														return max(0.0  ,       0.5 * (1.0 + math.cos(math.pi * float(_lowerCamelCase					) * 2.0 * progress					))					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Optional[int]					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		Dict									=       float(current_step - num_warmup_steps					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)
														if progress >= 1.0:
																					return 0.0
														return max(0.0  ,       0.5 * (1.0 + math.cos(math.pi * ((float(_lowerCamelCase					) * progress) % 1.0)					))					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[Any]=1E-7  ,       _lowerCamelCase				: Optional[int]=1.0  ,       _lowerCamelCase				: Optional[int]=-1					):
							__a		:		Union[str, Any]									=       optimizer.defaults["""lr"""]
							if not (lr_init > lr_end):
														raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''					)

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					__a		:		Tuple									=       lr_init - lr_end
																					__a		:		int									=       num_training_steps - num_warmup_steps
																					__a		:		Optional[int]									=       1 - (current_step - num_warmup_steps) / decay_steps
																					__a		:		List[str]									=       lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)


lowercase__			   =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def       __magic_name__    (   _lowerCamelCase				: Union[str, SchedulerType]  ,       _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: Optional[str] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: float = 1.0  ,       _lowerCamelCase				: int = -1  ,       ):
							__a		:		int									=       SchedulerType(_lowerCamelCase					)
							__a		:		Optional[int]									=       TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(_lowerCamelCase  ,       step_rules=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''					)

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(_lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''					)

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       num_cycles=_lowerCamelCase  ,       last_epoch=_lowerCamelCase  ,       )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       power=_lowerCamelCase  ,       last_epoch=_lowerCamelCase  ,       )

							return schedule_func(
							    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

