




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


__a							     =					logging.get_logger(__name__)

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''linear'''
    lowerCAmelCase         = '''cosine'''
    lowerCAmelCase         = '''cosine_with_restarts'''
    lowerCAmelCase         = '''polynomial'''
    lowerCAmelCase         = '''constant'''
    lowerCAmelCase         = '''constant_with_warmup'''
    lowerCAmelCase         = '''piecewise_constant'''







def lowerCamelCase__  (     _lowercase					,		_lowercase = -1 ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase = -1 ):

     '''simple docstring'''


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

     return LambdaLR(_lowercase					,		_lowercase					,		last_epoch=_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase = -1 ):

     '''simple docstring'''


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

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

          return rule_func

     UpperCAmelCase_     :				Dict  =     create_rules_function(_lowercase					,		_lowercase )

     return LambdaLR(_lowercase					,		_lowercase					,		last_epoch=_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=-1 ):

     '''simple docstring'''


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

     return LambdaLR(_lowercase					,		_lowercase					,		_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase = 0.5					,		_lowercase = -1 ):

     '''simple docstring'''


     def lr_lambda(_lowercase ):
          if current_step < num_warmup_steps:
               return float(_lowercase ) / float(max(1					,		_lowercase ) )
          UpperCAmelCase_     :				Optional[int]  =     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(_lowercase ) * 2.0 * progress )) )

     return LambdaLR(_lowercase					,		_lowercase					,		_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase = 1					,		_lowercase = -1 ):

     '''simple docstring'''


     def lr_lambda(_lowercase ):
          if current_step < num_warmup_steps:
               return float(_lowercase ) / float(max(1					,		_lowercase ) )
          UpperCAmelCase_     :				List[Any]  =     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(_lowercase ) * progress) % 1.0) )) )

     return LambdaLR(_lowercase					,		_lowercase					,		_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=1E-7					,		_lowercase=1.0					,		_lowercase=-1 ):

     '''simple docstring'''


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

     return LambdaLR(_lowercase					,		_lowercase					,		_lowercase )


__a							     =					{
    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__  (     _lowercase					,		_lowercase					,		_lowercase = None					,		_lowercase = None					,		_lowercase = None					,		_lowercase = 1					,		_lowercase = 1.0					,		_lowercase = -1					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				Union[str, Any]  =     SchedulerType(_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(_lowercase					,		last_epoch=_lowercase )

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

     # 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(_lowercase					,		num_warmup_steps=_lowercase					,		last_epoch=_lowercase )

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

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

     return schedule_func(
         _lowercase					,		num_warmup_steps=_lowercase					,		num_training_steps=_lowercase					,		last_epoch=_lowercase )
"""simple docstring"""






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase       :			str   	="""path-to-your-trained-model"""
__lowerCAmelCase       :			int   	=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

__lowerCAmelCase       :			Optional[int]   	="""A photo of sks dog in a bucket"""
__lowerCAmelCase       :			int   	=pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")



