



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 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   List[str]       						="""linear"""
				UpperCamelCase__			:   str       						="""cosine"""
				UpperCamelCase__			:   Any       						="""cosine_with_restarts"""
				UpperCamelCase__			:   Union[str, Any]       						="""polynomial"""
				UpperCamelCase__			:   int       						="""constant"""
				UpperCamelCase__			:   int       						="""constant_with_warmup"""
				UpperCamelCase__			:   Optional[int]       						="""piecewise_constant"""



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = -1					):
			return LambdaLR(SCREAMING_SNAKE_CASE_  ,					lambda SCREAMING_SNAKE_CASE_					: 1  ,					last_epoch=SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = -1					):

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

			return LambdaLR(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					last_epoch=SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = -1					):
			lowercase__   					=			{}
			lowercase__   					=			step_rules.split(","					)
			for rule_str in rule_list[:-1]:
						lowercase__					,    lowercase__   					=			rule_str.split(":"					)
						lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			float(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			value
			lowercase__   					=			float(rule_list[-1]					)

			def create_rules_function(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						def rule_func(SCREAMING_SNAKE_CASE_					) -> float:
									lowercase__   					=			sorted(rules_dict.keys()					)
									for i, sorted_step in enumerate(SCREAMING_SNAKE_CASE_					):
												if steps < sorted_step:
															return rules_dict[sorted_steps[i]]
									return last_lr_multiple

						return rule_func

			lowercase__   					=			create_rules_function(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			return LambdaLR(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					last_epoch=SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=-1					):

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

			return LambdaLR(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = 0.5  ,					SCREAMING_SNAKE_CASE_ = -1					):

			def lr_lambda(SCREAMING_SNAKE_CASE_					):
						if current_step < num_warmup_steps:
									return float(SCREAMING_SNAKE_CASE_					) / float(max(1  ,					SCREAMING_SNAKE_CASE_					)					)
						lowercase__   					=			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(SCREAMING_SNAKE_CASE_					) * 2.0 * progress					))					)

			return LambdaLR(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = 1  ,					SCREAMING_SNAKE_CASE_ = -1					):

			def lr_lambda(SCREAMING_SNAKE_CASE_					):
						if current_step < num_warmup_steps:
									return float(SCREAMING_SNAKE_CASE_					) / float(max(1  ,					SCREAMING_SNAKE_CASE_					)					)
						lowercase__   					=			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(SCREAMING_SNAKE_CASE_					) * progress) % 1.0)					))					)

			return LambdaLR(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=1e-7  ,					SCREAMING_SNAKE_CASE_=1.0  ,					SCREAMING_SNAKE_CASE_=-1					):
			lowercase__   					=			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(SCREAMING_SNAKE_CASE_					):
						if current_step < num_warmup_steps:
									return float(SCREAMING_SNAKE_CASE_					) / float(max(1  ,					SCREAMING_SNAKE_CASE_					)					)
						elif current_step > num_training_steps:
									return lr_end / lr_init  # as LambdaLR multiplies by lr_init
						else:
									lowercase__   					=			lr_init - lr_end
									lowercase__   					=			num_training_steps - num_warmup_steps
									lowercase__   					=			1 - (current_step - num_warmup_steps) / decay_steps
									lowercase__   					=			lr_range * pct_remaining**power + lr_end
									return decay / lr_init  # as LambdaLR multiplies by lr_init

			return LambdaLR(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)


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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = None  ,					SCREAMING_SNAKE_CASE_ = None  ,					SCREAMING_SNAKE_CASE_ = None  ,					SCREAMING_SNAKE_CASE_ = 1  ,					SCREAMING_SNAKE_CASE_ = 1.0  ,					SCREAMING_SNAKE_CASE_ = -1  ,					):
			lowercase__   					=			SchedulerType(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			TYPE_TO_SCHEDULER_FUNCTION[name]
			if name == SchedulerType.CONSTANT:
						return schedule_func(SCREAMING_SNAKE_CASE_  ,					last_epoch=SCREAMING_SNAKE_CASE_					)

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

			# 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(SCREAMING_SNAKE_CASE_  ,					num_warmup_steps=SCREAMING_SNAKE_CASE_  ,					last_epoch=SCREAMING_SNAKE_CASE_					)

			# 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(
						    SCREAMING_SNAKE_CASE_  ,					num_warmup_steps=SCREAMING_SNAKE_CASE_  ,					num_training_steps=SCREAMING_SNAKE_CASE_  ,					num_cycles=SCREAMING_SNAKE_CASE_  ,					last_epoch=SCREAMING_SNAKE_CASE_  ,					)

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

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









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
else:
							from .modeling_text_unet import UNetFlatConditionModel
							from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
							from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
							from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
							from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline




