


"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =(DEISMultistepScheduler,)
							__UpperCAmelCase						:    Optional[int]				      =(("""num_inference_steps""", 2_5),)

							def 			snake_case				(     self    ,   **__a	):
										__lowerCAmelCase				    =			{
										    "num_train_timesteps": 10_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										}

										config.update(**__a	)
										return config

							def 			snake_case				(     self    ,   __a=0    ,   **__a	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)
										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)
										__lowerCAmelCase				    =			self.dummy_sample
										__lowerCAmelCase				    =			0.1 * sample
										__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config(**__a	)
													__lowerCAmelCase				    =			scheduler_class(**__a	)
													scheduler.set_timesteps(__a	)
													# copy over dummy past residuals
													__lowerCAmelCase				    =			dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__a	)
																__lowerCAmelCase				    =			scheduler_class.from_pretrained(__a	)
																new_scheduler.set_timesteps(__a	)
																# copy over dummy past residuals
																__lowerCAmelCase				    =			dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowerCAmelCase    ,	__lowerCAmelCase				    =			sample, sample
													for t in range(__a    ,   time_step + scheduler.config.solver_order + 1	):
																__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
																__lowerCAmelCase				    =			new_scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample

																assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

							def 			snake_case				(     self	):
										pass

							def 			snake_case				(     self    ,   __a=0    ,   **__a	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)
										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)
										__lowerCAmelCase				    =			self.dummy_sample
										__lowerCAmelCase				    =			0.1 * sample
										__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config()
													__lowerCAmelCase				    =			scheduler_class(**__a	)
													scheduler.set_timesteps(__a	)

													# copy over dummy past residuals (must be after setting timesteps)
													__lowerCAmelCase				    =			dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__a	)
																__lowerCAmelCase				    =			scheduler_class.from_pretrained(__a	)
																# copy over dummy past residuals
																new_scheduler.set_timesteps(__a	)

																# copy over dummy past residual (must be after setting timesteps)
																__lowerCAmelCase				    =			dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
													__lowerCAmelCase				    =			new_scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample

													assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

							def 			snake_case				(     self    ,   __a=None    ,   **__a	):
										if scheduler is None:
													__lowerCAmelCase				    =			self.scheduler_classes[0]
													__lowerCAmelCase				    =			self.get_scheduler_config(**__a	)
													__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(**__a	)
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.dummy_model()
										__lowerCAmelCase				    =			self.dummy_sample_deter
										scheduler.set_timesteps(__a	)

										for i, t in enumerate(scheduler.timesteps	):
													__lowerCAmelCase				    =			model(__a    ,   __a	)
													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a	).prev_sample

										return sample

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)

										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config()
													__lowerCAmelCase				    =			scheduler_class(**__a	)

													__lowerCAmelCase				    =			self.dummy_sample
													__lowerCAmelCase				    =			0.1 * sample

													if num_inference_steps is not None and hasattr(__a    ,   "set_timesteps"	):
																scheduler.set_timesteps(__a	)
													elif num_inference_steps is not None and not hasattr(__a    ,   "set_timesteps"	):
																__lowerCAmelCase				    =			num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													__lowerCAmelCase				    =			dummy_past_residuals[: scheduler.config.solver_order]

													__lowerCAmelCase				    =			scheduler.timesteps[5]
													__lowerCAmelCase				    =			scheduler.timesteps[6]

													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample

													self.assertEqual(output_a.shape    ,   sample.shape	)
													self.assertEqual(output_a.shape    ,   output_a.shape	)

							def 			snake_case				(     self	):
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										__lowerCAmelCase				    =			DEISMultistepScheduler(**self.get_scheduler_config()	)
										__lowerCAmelCase				    =			self.full_loop(scheduler=__a	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

										__lowerCAmelCase				    =			DPMSolverSinglestepScheduler.from_config(scheduler.config	)
										__lowerCAmelCase				    =			DPMSolverMultistepScheduler.from_config(scheduler.config	)
										__lowerCAmelCase				    =			UniPCMultistepScheduler.from_config(scheduler.config	)
										__lowerCAmelCase				    =			DEISMultistepScheduler.from_config(scheduler.config	)

										__lowerCAmelCase				    =			self.full_loop(scheduler=__a	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

							def 			snake_case				(     self	):
										for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=__a	)

							def 			snake_case				(     self	):
										self.check_over_configs(thresholding=__a	)
										for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    thresholding=__a    ,   prediction_type=__a    ,   sample_max_value=__a    ,   algorithm_type="deis"    ,   solver_order=__a    ,   solver_type=__a    ,   )

							def 			snake_case				(     self	):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__a	)

							def 			snake_case				(     self	):
										for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																for order in [1, 2, 3]:
																			for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    solver_order=__a    ,   solver_type=__a    ,   prediction_type=__a    ,   algorithm_type=__a    ,   )
																						__lowerCAmelCase				    =			self.full_loop(
																						    solver_order=__a    ,   solver_type=__a    ,   prediction_type=__a    ,   algorithm_type=__a    ,   )
																						assert not torch.isnan(__a	).any(), "Samples have nan numbers"

							def 			snake_case				(     self	):
										self.check_over_configs(lower_order_final=__a	)
										self.check_over_configs(lower_order_final=__a	)

							def 			snake_case				(     self	):
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=__a    ,   time_step=0	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.full_loop()
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.full_loop(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.0_9_1	) < 1e-3



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(thresholding=__a    ,   dynamic_thresholding_ratio=0	)
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.dummy_model()
										__lowerCAmelCase				    =			self.dummy_sample_deter.half()
										scheduler.set_timesteps(__a	)

										for i, t in enumerate(scheduler.timesteps	):
													__lowerCAmelCase				    =			model(__a    ,   __a	)
													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a	).prev_sample

										assert sample.dtype == torch.floataa









"""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__		:				Optional[Any]          =	logging.get_logger(__name__)







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """linear"""
      _SCREAMING_SNAKE_CASE  				=     """cosine"""
      _SCREAMING_SNAKE_CASE  				=     """cosine_with_restarts"""
      _SCREAMING_SNAKE_CASE  				=     """polynomial"""
      _SCREAMING_SNAKE_CASE  				=     """constant"""
      _SCREAMING_SNAKE_CASE  				=     """constant_with_warmup"""
      _SCREAMING_SNAKE_CASE  				=     """piecewise_constant"""




def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int = -1						)						->   Tuple:

  """simple docstring"""




  return LambdaLR(lowerCAmelCase__    ,   lambda lowerCAmelCase__						: 1    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = -1						)						->   str:

  """simple docstring"""




  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 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				str								= {}
  lowerCAmelCase_   :				Optional[Any]								= step_rules.split(','						)
  for rule_str in rule_list[:-1]:
    lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= rule_str.split(':'						)
    lowerCAmelCase_   :				List[str]								= int(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[int]								= float(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[Any]								= value
  lowerCAmelCase_   :				str								= float(rule_list[-1]						)

  def create_rules_function(lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				int						):
    def rule_func(lowerCAmelCase__      :				int						) -> float:
      lowerCAmelCase_   :				str								= 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

  lowerCAmelCase_   :				Dict								= create_rules_function(lowerCAmelCase__    ,   lowerCAmelCase__						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Union[str, Any]=-1						)						->   Any:

  """simple docstring"""




  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 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float = 0.5    ,   lowerCAmelCase__      :				int = -1						)						->   Union[str, Any]:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				Any						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				List[Any]								= 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 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				str						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				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 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Tuple=1e-7    ,   lowerCAmelCase__      :				Union[str, Any]=1.0    ,   lowerCAmelCase__      :				int=-1						)						->   Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				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:
      lowerCAmelCase_   :				Any								= lr_init - lr_end
      lowerCAmelCase_   :				int								= num_training_steps - num_warmup_steps
      lowerCAmelCase_   :				Dict								= 1 - (current_step - num_warmup_steps) / decay_steps
      lowerCAmelCase_   :				Dict								= lr_range * pct_remaining**power + lr_end
      return decay / lr_init  # as LambdaLR multiplies by lr_init

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)


lowercase__		:				Any          =	{
    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 				UpperCamelCase_    (    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    ,   )						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= SchedulerType(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= 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__						)





