


'''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


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






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



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







def     UpperCamelCase_      (   A__ :       Optimizer							,   A__ :       int = -1 ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       Optimizer							,   A__ :       int							,   A__ :       int = -1 ):
  '''simple docstring'''





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

  return LambdaLR(A__							,   A__							,   last_epoch=A__ )







def     UpperCamelCase_      (   A__ :       Optimizer							,   A__ :       str							,   A__ :       int = -1 ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       {}
  lowerCAmelCase_      :				str      =       step_rules.split(""",""" )
  for rule_str in rule_list[:-1]:
    lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       rule_str.split(""":""" )
    lowerCAmelCase_      :				Tuple      =       int(A__ )
    lowerCAmelCase_      :				Any      =       float(A__ )
    lowerCAmelCase_      :				Any      =       value
  lowerCAmelCase_      :				Dict      =       float(rule_list[-1] )

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

    return rule_func

  lowerCAmelCase_      :				List[Any]      =       create_rules_function(A__							,   A__ )

  return LambdaLR(A__							,   A__							,   last_epoch=A__ )







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Union[str, Any]							,   A__ :       List[str]							,   A__ :       Dict=-1 ):
  '''simple docstring'''





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

  return LambdaLR(A__							,   A__							,   A__ )







def     UpperCamelCase_      (   A__ :       Optimizer							,   A__ :       int							,   A__ :       int							,   A__ :       float = 0.5							,   A__ :       int = -1 ):
  '''simple docstring'''





  def lr_lambda(A__ :       Any ):
    if current_step < num_warmup_steps:
      return float(A__ ) / float(max(1							,   A__ ) )
    lowerCAmelCase_      :				Union[str, 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(A__ ) * 2.0 * progress )) )

  return LambdaLR(A__							,   A__							,   A__ )







def     UpperCamelCase_      (   A__ :       Optimizer							,   A__ :       int							,   A__ :       int							,   A__ :       int = 1							,   A__ :       int = -1 ):
  '''simple docstring'''





  def lr_lambda(A__ :       Dict ):
    if current_step < num_warmup_steps:
      return float(A__ ) / float(max(1							,   A__ ) )
    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(A__ ) * progress) % 1.0) )) )

  return LambdaLR(A__							,   A__							,   A__ )







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Any							,   A__ :       Union[str, Any]							,   A__ :       Optional[Any]=1E-7							,   A__ :       Tuple=1.0							,   A__ :       Tuple=-1 ):
  '''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(A__ :       int ):
    if current_step < num_warmup_steps:
      return float(A__ ) / float(max(1							,   A__ ) )
    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_      :				Dict      =       num_training_steps - num_warmup_steps
      lowerCAmelCase_      :				Union[str, Any]      =       1 - (current_step - num_warmup_steps) / decay_steps
      lowerCAmelCase_      :				List[Any]      =       lr_range * pct_remaining**power + lr_end
      return decay / lr_init  # as LambdaLR multiplies by lr_init

  return LambdaLR(A__							,   A__							,   A__ )


__A :  List[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_      (   A__ :       Union[str, SchedulerType]							,   A__ :       Optimizer							,   A__ :       Optional[str] = None							,   A__ :       Optional[int] = None							,   A__ :       Optional[int] = None							,   A__ :       int = 1							,   A__ :       float = 1.0							,   A__ :       int = -1							,   ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       SchedulerType(A__ )
  lowerCAmelCase_      :				List[str]      =       TYPE_TO_SCHEDULER_FUNCTION[name]
  if name == SchedulerType.CONSTANT:
    return schedule_func(A__							,   last_epoch=A__ )

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

  # 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(A__							,   num_warmup_steps=A__							,   last_epoch=A__ )

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

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

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







'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       UNetaDModel(
          block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,						)
      return model





    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







@slow
@require_torch
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2



